Computing systems and graphical user interfaces for adaptive transformations of resource allocations under constrained conditions

Abstract

A control computing system generates, based on a first set of resource parameters, a scaling factor for an initial resource allocation. The initial resource allocation allocates resources for a first time block of multiple time blocks. The multiple time blocks are according to a schedule controlled by a resource management computing system (RMS). The initial resource allocation is generated by the RMS executing a second set of computing instructions separately from a first set of computing instructions. The second set of computing instructions are operable to cause the RMS to generate the initial resource allocation based on a second set of resource parameters. The control computing system overrides, based on the scaling factor, the initial resource allocation to a computer-generated resource allocation for the first time block. The computer-generated resource allocation is a modification of the initial resource allocation.

Description

[0001] COMPUTING SYSTEMS AND GRAPHICAL USER INTERFACES FOR ADAPTIVE TRANSFORMATIONS OF RESOURCE ALLOCATIONS UNDER CONSTRAINED CONDITIONS

[0002] CROSS-REFERENCE TO RELATED APPLICATIONS

[0003] This application claims the benefit of, and priority to U.S. Provisional Application No. 63 / 748,777, filed January 23, 2025, and claims priority to U.S. Provisional Application No.

[0004] 63 / 745,529, filed January 15, 2025, the disclosures of each of which are incorporated herein by reference in their entirety.

[0005] TECHNICAL FIELD

[0006] The present invention relates to computing devices and systems, and more particularly the present invention relates to overriding resource allocations.

[0007] SUMMARY

[0008] In an example embodiment, a computer-program product tangibly embodied in a non-transitory machine-readable storage medium is provided. The computer-program product includes a first set of computing instructions to cause a control computing system to generate, based on a first set of resource parameters, a scaling factor for an initial resource allocation. The initial resource allocation allocates resources for a first time block of multiple time blocks. The multiple time blocks are according to a schedule controlled by a resource management computing system. The initial resource allocation is generated by the resource management computing system executing a second set of computing instructions separately from the first set of computing instructions. The second set of computing instructions are operable to cause the resource management computing system to generate the initial resource allocation based on a second set of resource parameters. The first set of resource parameters and the second set of resource parameters are, for example, disjoint sets. The computer-program product includes the first set of computing instructions to cause the control computing system to override, based on the scaling factor, the initial resource allocation to a computer-generated resource allocation for the first time block. The computer-generated resource allocation is a modification of the initial resource allocation.

[0009] In another example embodiment, a control computing system is provided. The computing system includes, but is not limited to, a processor and memory. The memory contains a first set of computing instructions that when executed by the processor control the control computing system to generate, based on a first set of resource parameters, a scaling factor for an initial resource allocation. The initial resource allocation allocates resources for a first time block of multiple time blocks. The multiple time blocks are according to a schedule controlled by a resource management computing system. The initial resource allocation is generated by the resource management computing system executing a second set of computing instructions separately from the first set of computing instructions. The second set ofcomputing instructions are operable to cause the resource management computing system to generate the initial resource allocation based on a second set of resource parameters. The first set of resource parameters and the second set of resource parameters are, for example, disjoint sets. The memory contains the first set of computing instructions that when executed by the processor control the control computing system to override, based on the scaling factor, the initial resource allocation to a computer-generated resource allocation for the first time block. The computer-generated resource allocation is a modification of the initial resource allocation.

[0010] In another example embodiment, a method is provided. The method includes generating, based on a first set of resource parameters, a scaling factor for an initial resource allocation. The initial resource allocation allocates resources for a first time block of multiple time blocks. The multiple time blocks are according to a schedule controlled by a resource management computing system. The initial resource allocation is generated by the resource management computing system executing a second set of computing instructions separately from a first set of computing instructions. The second set of computing instructions are operable to cause the resource management computing system to generate the initial resource allocation based on a second set of resource parameters. The first set of resource parameters and the second set of resource parameters are, for example, disjoint sets. The method includes override, based on the scaling factor, the initial resource allocation to a computer-generated resource allocation for the first time block. The computer-generated resource allocation is a modification of the initial resource allocation.

[0011] Other features and aspects of example embodiments are presented below in the Detailed Description when read in connection with the drawings presented with this application.

[0012] BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 illustrates a block diagram that provides an illustration of the hardware components of a computing system, according to some embodiments of the present technology.

[0014] Figure 2 illustrates an example network including an example set of devices communicating with each other over an exchange system and via a network, according to some embodiments of the present technology.

[0015] Figure 3 illustrates a representation of a conceptual model of a communications protocol system, according to some embodiments of the present technology.

[0016] Figure 4 illustrates a communications grid computing system including a variety of control and worker nodes, according to some embodiments of the present technology.

[0017] Figure 5 illustrates a flow chart showing an example process for adjusting a communications grid or a work project in a communications grid after a failure of a node, according to some embodiments of the present technology.

[0018] Figure 6 illustrates a portion of a communications grid computing system including a control node and a worker node, according to some embodiments of the present technology.Figure 7 illustrates a flow chart showing an example process for executing a data analysis or processing project, according to some embodiments of the present technology.

[0019] Figure 8 illustrates a block diagram including components of an Event Stream Processing Engine (ESPE), according to embodiments of the present technology.

[0020] Figure 9 illustrates a flow chart showing an example process including operations performed by an event stream processing engine, according to some embodiments of the present technology.

[0021] Figure 10 illustrates an ESP system interfacing between a publishing device and multiple event subscribing devices, according to embodiments of the present technology.

[0022] Figure 11 illustrates a flow chart of an example of a process for generating and using a machine-learning model according to some aspects, according to embodiments of the present technology.

[0023] Figure 12 illustrates an example of a machine-learning model as a neural network, according to embodiments of the present technology.

[0024] Figure 13 illustrates various aspects of the use of containers as a mechanism to allocate processing, storage and / or other resources of a processing system to the performance of various analyses, according to embodiments of the present technology.

[0025] Figure 14 illustrates a block diagram of a system for overriding a resource allocation according to at least one embodiment of the present technology.

[0026] Figures 15A-15B illustrate a flow diagram for overriding a resource allocation according to at least one embodiment of the present technology.

[0027] Figure 16 illustrates a system for modifying a resource management system’s output according to at least one embodiment of the present technology.

[0028] Figure 17A illustrates a resource management system according to at least one embodiment of the present technology.

[0029] Figure 17B illustrates a scaling system for modifying overbooking recommendations according to at least one embodiment of the present technology.

[0030] Figure 18 illustrates a scaling factor graph according to at least one embodiment of the present technology.

[0031] Figures 19A-19B illustrate computer-generated overbooking recommendations according to at least one embodiment of the present technology.

[0032] Figures 20A-20B illustrate graphical user interfaces for room reservations according to at least one embodiment of the present technology.

[0033] Figure 21 illustrates a graphical user interface for rental reservations according to at least one embodiment of the present technology.

[0034] Figure 22A illustrates a graphical user interface for voucher distribution according to at least one embodiment of the present technology.Figure 22B illustrates a graphical user interface for options according to at least one embodiment of the present technology.

[0035] Figure 23 illustrates a graphical user interface for setting thresholds for overbooking of rooms according to at least one embodiment of the present technology.

[0036] Figure 24 illustrates a graphical user interface for displaying multiple time periods of overbooking information in a graphical user interface according to at least one embodiment of the present technology.

[0037] DETAILED DESCRIPTION

[0038] In the following description, for the purposes of explanation, specific details are set forth in order to provide a thorough understanding of embodiments of the technology. However, it will be apparent that various embodiments may be practiced without these specific details. The figures and description are not intended to be restrictive.

[0039] The ensuing description provides example embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the example embodiments will provide those skilled in the art with an enabling description for implementing an example embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the technology as set forth in the appended claims.

[0040] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.

[0041] Also, it is noted that individual embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed, but could have additional operations not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

[0042] Systems depicted in some of the figures may be provided in various configurations. In some embodiments, the systems may be configured as a distributed system where one or more components of the system are distributed across one or more networks in a cloud computing system.Figure 1 is a block diagram that provides an illustration of the hardware components of a data transmission network 100, according to embodiments of the present technology. Data transmission network 100 is a specialized computer system that may be used for processing large amounts of data where a large number of computer processing cycles are required.

[0043] Data transmission network 100 may also include computing environment 114.

[0044] Computing environment 114 may be a specialized computer or other machine that processes the data received within the data transmission network 100. Data transmission network 100 also includes one or more network devices 102. Network devices 102 may include client devices that attempt to communicate with computing environment 114. For example, network devices 102 may send data to the computing environment 114 to be processed, may send signals to the computing environment 114 to control different aspects of the computing environment or the data it is processing, among other reasons. Network devices 102 may interact with the computing environment 114 through a number of ways, such as, for example, over one or more networks 108. As shown in Figure 1, computing environment 114 may include one or more other systems. For example, computing environment 114 may include a database system 118 and / or a communications grid 120.

[0045] In other embodiments, network devices may provide a large amount of data, either all at once or streaming over a period of time (e.g., using event stream processing (ESP), described further with respect to FIGS. 8-10), to the computing environment 114 via networks 108. For example, network devices 102 may include network computers, sensors, databases, or other devices that may transmit or otherwise provide data to computing environment 114. For example, network devices may include local area network devices, such as routers, hubs, switches, or other computer networking devices. These devices may provide a variety of stored or generated data, such as network data or data specific to the network devices themselves. Network devices may also include sensors that monitor their environment or other devices to collect data regarding that environment or those devices, and such network devices may provide data they collect over time. Network devices may also include devices within the internet of things, such as devices within a home automation network. Some of these devices may be referred to as edge devices and may involve edge computing circuitry. Data may be transmitted by network devices directly to computing environment 114 or to network-attached data stores, such as network-attached data stores 110 for storage so that the data may be retrieved later by the computing environment 114 or other portions of data transmission network 100.

[0046] Data transmission network 100 may also include one or more network-attached data stores 110. Network-attached data stores 110 are used to store data to be processed by the computing environment 114 as well as any intermediate or final data generated by the computing system in non-volatile memory. However, in certain embodiments, the configuration of the computing environment 114 allows its operations to be performed such that intermediateand final data results can be stored solely in volatile memory (e.g., RAM), without a requirement that intermediate or final data results be stored to non-volatile types of memory (e.g., disk). This can be useful in certain situations, such as when the computing environment 114 receives ad hoc queries from a user and when responses, which are generated by processing large amounts of data, need to be generated on-the-fly. In this non-limiting situation, the computing environment 114 may be configured to retain the processed information within memory so that responses can be generated for the user at different levels of detail as well as allow a user to interactively query against this information.

[0047] Network-attached data stores may store a variety of different types of data organized in a variety of different ways and from a variety of different sources. For example, network-attached data storage may include storage other than primary storage located within computing environment 114 that is directly accessible by processors located therein. Network-attached data storage may include secondary, tertiary or auxiliary storage, such as large hard drives, servers, virtual memory, among other types. Storage devices may include portable or nonportable storage devices, optical storage devices, and various other mediums capable of storing, or containing data. A machine-readable storage medium or computer-readable storage medium may include a non-transitory medium in which data can be stored and that does not include carrier waves and / or transitory electronic signals. Examples of a non-transitory medium may include, for example, a magnetic disk or tape, optical storage media such as compact disk or digital versatile disk, flash memory, memory or memory devices. A computer-program product may include code and / or machine-executable instructions that may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and / or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, among others. Furthermore, the data stores may hold a variety of different types of data. For example, network-attached data stores 110 may hold unstructured (e.g., raw) data, such as manufacturing data (e.g., a database containing records identifying products being manufactured with parameter data for each product, such as colors and models) or product sales databases (e.g., a database containing individual data records identifying details of individual product sales).

[0048] The unstructured data may be presented to the computing environment 114 in different forms such as a flat file or a conglomerate of data records and may have data values and accompanying time stamps. The computing environment 114 may be used to analyze the unstructured data in a variety of ways to determine the best way to structure (e.g., hierarchically) that data, such that the structured data is tailored to a type of further analysis thata user wishes to perform on the data. For example, after being processed, the unstructured time stamped data may be aggregated by time (e.g., into daily time period units) to generate time series data and / or structured hierarchically according to one or more dimensions (e.g., parameters, attributes, and / or variables). For example, data may be stored in a hierarchical data structure, such as a ROLAP or MOLAP database, or may be stored in another tabular form, such as in a flat-hierarchy form.

[0049] Data transmission network 100 may also include one or more server farms 106.

[0050] Computing environment 114 may route select communications or data to the one or more sever farms 106 or one or more servers within the server farms. Server farms 106 can be configured to provide information in a predetermined manner. For example, server farms 106 may access data to transmit in response to a communication. Server farms 106 may be separately housed from each other device within data transmission network 100, such as computing environment 114, and / or may be part of a device or system.

[0051] Server farms 106 may host a variety of different types of data processing as part of data transmission network 100. Server farms 106 may receive a variety of different data from network devices, from computing environment 114, from cloud network 116, or from other sources. The data may have been obtained or collected from one or more sensors, as inputs from a control database, or may have been received as inputs from an external system or device. Server farms 106 may assist in processing the data by turning raw data into processed data based on one or more rules implemented by the server farms. For example, sensor data may be analyzed to determine changes in an environment over time or in real-time.

[0052] Data transmission network 100 may also include one or more cloud networks 116. Cloud network 116 may include a cloud infrastructure system that provides cloud services. In certain embodiments, services provided by the cloud network 116 may include a host of services that are made available to users of the cloud infrastructure system on demand. Cloud network 116 is shown in Figure 1 as being connected to computing environment 114 (and therefore having computing environment 114 as its client or user), but cloud network 116 may be connected to or utilized by any of the devices in Figure 1. Services provided by the cloud network can dynamically scale to meet the needs of its users. The cloud network 116 may include one or more computers, servers, and / or systems. In some embodiments, the computers, servers, and / or systems that make up the cloud network 116 are different from the user’s own on-premises computers, servers, and / or systems. For example, the cloud network 116 may host an application, and a user may, via a communication network such as the Internet, on demand, order and use the application.

[0053] While each device, server and system in Figure 1 is shown as a single device, it will be appreciated that multiple devices may instead be used. For example, a set of network devices can be used to transmit various communications from a single user, or remote server 140 mayinclude a server stack. As another example, data may be processed as part of computing environment 114.

[0054] Each communication within data transmission network 100 (e.g., between client devices, between servers 106 and computing environment 114 or between a server and a device) may occur over one or more networks 108. Networks 108 may include one or more of a variety of different types of networks, including a wireless network, a wired network, or a combination of a wired and wireless network. Examples of suitable networks include the Internet, a personal area network, a local area network (LAN), a wide area network (WAN), or a wireless local area network (WLAN). A wireless network may include a wireless interface or combination of wireless interfaces. As an example, a network in the one or more networks 108 may include a short-range communication channel, such as a BLUETOOTH® communication channel or a BLUETOOTH® Low Energy communication channel. A wired network may include a wired interface. The wired and / or wireless networks may be implemented using routers, access points, bridges, gateways, or the like, to connect devices in the network 114, as will be further described with respect to Figure 2. The one or more networks 108 can be incorporated entirely within or can include an intranet, an extranet, or a combination thereof. In one embodiment, communications between two or more systems and / or devices can be achieved by a secure communications protocol, such as secure sockets layer (SSL) or transport layer security (TLS). In addition, data and / or transactional details may be encrypted.

[0055] Some aspects may utilize the Internet of Things (loT), where things (e.g., machines, devices, phones, sensors) can be connected to networks and the data from these things can be collected and processed within the things and / or external to the things. For example, the loT can include sensors in many different devices, and high value analytics can be applied to identify hidden relationships and drive increased efficiencies. This can apply to both big data analytics and real-time (e.g., ESP) analytics. This will be described further below with respect to Figure 2.

[0056] As noted, computing environment 114 may include a communications grid 120 and a transmission network database system 118. Communications grid 120 may be a grid-based computing system for processing large amounts of data. The transmission network database system 118 may be for managing, storing, and retrieving large amounts of data that are distributed to and stored in the one or more network-attached data stores 110 or other data stores that reside at different locations within the transmission network database system 118. The compute nodes in the grid-based computing system 120 and the transmission network database system 118 may share the same processor hardware, such as processors that are located within computing environment 114.

[0057] Figure 2 illustrates an example network including an example set of devices communicating with each other over an exchange system and via a network, according to embodiments of the present technology. As noted, each communication within datatransmission network 100 may occur over one or more networks. System 200 includes a network device 204 configured to communicate with a variety of types of client devices, for example client devices 230, over a variety of types of communication channels.

[0058] As shown in Figure 2, network device 204 can transmit a communication over a network (e.g., a cellular network via a base station 210). The communication can be routed to another network device, such as network devices 205-209, via base station 210. The communication can also be routed to computing environment 214 via base station 210. For example, network device 204 may collect data either from its surrounding environment or from other network devices (such as network devices 205-209) and transmit that data to computing environment 214.

[0059] Although network devices 204-209 are shown in Figure 2 as a mobile phone, laptop computer, tablet computer, temperature sensor, motion sensor, and audio sensor respectively, the network devices may be or include sensors that are sensitive to detecting aspects of their environment. For example, the network devices may include sensors such as water sensors, power sensors, electrical current sensors, chemical sensors, optical sensors, pressure sensors, geographic or position sensors (e.g., GPS), velocity sensors, acceleration sensors, flow rate sensors, among others. Examples of characteristics that may be sensed include force, torque, load, strain, position, temperature, air pressure, fluid flow, chemical properties, resistance, electromagnetic fields, radiation, irradiance, proximity, acoustics, moisture, distance, speed, vibrations, acceleration, electrical potential, and electrical current, among others. The sensors may be mounted to various components used as part of a variety of different types of systems (e.g., an oil drilling operation). The network devices may detect and record data related to the environment that it monitors, and transmit that data to computing environment 214.

[0060] As noted, one type of system that may include various sensors that collect data to be processed and / or transmitted to a computing environment according to certain embodiments includes an oil drilling system. For example, the one or more drilling operation sensors may include surface sensors that measure a hook load, a fluid rate, a temperature and a density in and out of the wellbore, a standpipe pressure, a surface torque, a rotation speed of a drill pipe, a rate of penetration, a mechanical specific energy, etc. and downhole sensors that measure a rotation speed of a bit, fluid densities, downhole torque, downhole vibration (axial, tangential, lateral), a weight applied at a drill bit, an annular pressure, a differential pressure, an azimuth, an inclination, a dog leg severity, a measured depth, a vertical depth, a downhole temperature, etc. Besides the raw data collected directly by the sensors, other data may include parameters either developed by the sensors or assigned to the system by a client or other controlling device. For example, one or more drilling operation control parameters may control settings such as a mud motor speed to flow ratio, a bit diameter, a predicted formation top, seismic data, weather data, etc. Other data may be generated using physical models such as an earth model, a weather model, a seismic model, a bottom hole assembly model, a well plan model, anannular friction model, etc. In addition to sensor and control settings, predicted outputs, of for example, the rate of penetration, mechanical specific energy, hook load, flow in fluid rate, flow out fluid rate, pump pressure, surface torque, rotation speed of the drill pipe, annular pressure, annular friction pressure, annular temperature, equivalent circulating density, etc. may also be stored in the data warehouse.

[0061] In another example, another type of system that may include various sensors that collect data to be processed and / or transmitted to a computing environment according to certain embodiments includes a home automation or similar automated network in a different environment, such as an office space, school, public space, sports venue, or a variety of other locations. Network devices in such an automated network may include network devices that allow a user to access, control, and / or configure various home appliances located within the user’s home (e.g., a television, radio, light, fan, humidifier, sensor, microwave, iron, and / or the like), or outside of the user’s home (e.g., exterior motion sensors, exterior lighting, garage door openers, sprinkler systems, or the like). For example, network device 102 may include a home automation switch that may be coupled with a home appliance. In another embodiment, a network device can allow a user to access, control, and / or configure devices, such as office-related devices (e.g., copy machine, printer, or fax machine), audio and / or video related devices (e.g., a receiver, a speaker, a projector, a DVD player, or a television), media-playback devices (e.g., a compact disc player, a CD player, or the like), computing devices (e.g., a home computer, a laptop computer, a tablet, a personal digital assistant (PDA), a computing device, or a wearable device), lighting devices (e.g., a lamp or recessed lighting), devices associated with a security system, devices associated with an alarm system, devices that can be operated in an automobile (e.g., radio devices, navigation devices), and / or the like. Data may be collected from such various sensors in raw form, or data may be processed by the sensors to create parameters or other data either developed by the sensors based on the raw data or assigned to the system by a client or other controlling device.

[0062] In another example, another type of system that may include various sensors that collect data to be processed and / or transmitted to a computing environment according to certain embodiments includes a power or energy grid. A variety of different network devices may be included in an energy grid, such as various devices within one or more power plants, energy farms (e.g., wind farm, solar farm, among others) energy storage facilities, factories, homes and businesses of consumers, among others. One or more of such devices may include one or more sensors that detect energy gain or loss, electrical input or output or loss, and a variety of other efficiencies. These sensors may collect data to inform users of how the energy grid, and individual devices within the grid, may be functioning and how they may be made more efficient.

[0063] Network device sensors may also perform processing on data it collects before transmitting the data to the computing environment 114, or before deciding whether to transmit data to the computing environment 114. For example, network devices may determine whetherdata collected meets certain rules, for example by comparing data or values calculated from the data and comparing that data to one or more thresholds. The network device may use this data and / or comparisons to determine if the data should be transmitted to the computing environment 214 for further use or processing.

[0064] Computing environment 214 may include machines 220 and 240. Although computing environment 214 is shown in Figure 2 as having two machines, 220 and 240, computing environment 214 may have only one machine or may have more than two machines. The machines that make up computing environment 214 may include specialized computers, servers, or other machines that are configured to individually and / or collectively process large amounts of data. The computing environment 214 may also include storage devices that include one or more databases of structured data, such as data organized in one or more hierarchies, or unstructured data. The databases may communicate with the processing devices within computing environment 214 to distribute data to them. Since network devices may transmit data to computing environment 214, that data may be received by the computing environment 214 and subsequently stored within those storage devices. Data used by computing environment 214 may also be stored in data stores 235, which may also be a part of or connected to computing environment 214.

[0065] Computing environment 214 can communicate with various devices via one or more routers 225 or other inter-network or intra-network connection components. For example, computing environment 214 may communicate with devices 230 via one or more routers 225. Computing environment 214 may collect, analyze and / or store data from or pertaining to communications, client device operations, client rules, and / or user-associated actions stored at one or more data stores 235. Such data may influence communication routing to the devices within computing environment 214, how data is stored or processed within computing environment 214, among other actions.

[0066] Notably, various other devices can further be used to influence communication routing and / or processing between devices within computing environment 214 and with devices outside of computing environment 214. For example, as shown in Figure 2, computing environment 214 may include a web server 240. Thus, computing environment 214 can retrieve data of interest, such as client information (e.g., product information, client rules, etc.), technical product details, news, current or predicted weather, and so on.

[0067] In addition to computing environment 214 collecting data (e.g., as received from network devices, such as sensors, and client devices or other sources) to be processed as part of a big data analytics project, it may also receive data in real time as part of a streaming analytics environment. As noted, data may be collected using a variety of sources as communicated via different kinds of networks or locally. Such data may be received on a real-time streaming basis. For example, network devices may receive data periodically from network device sensors as the sensors continuously sense, monitor and track changes in their environments.Devices within computing environment 214 may also perform pre-analysis on data it receives to determine if the data received should be processed as part of an ongoing project. The data received and collected by computing environment 214, no matter what the source or method or timing of receipt, may be processed over a period of time for a client to determine results data based on the client’s needs and rules.

[0068] Figure 3 illustrates a representation of a conceptual model of a communications protocol system, according to embodiments of the present technology. More specifically, Figure 3 identifies operation of a computing environment in an Open Systems Interaction model that corresponds to various connection components. The model 300 shows, for example, how a computing environment, such as computing environment 314 (or computing environment 214 in Figure 2) may communicate with other devices in its network, and control how communications between the computing environment and other devices are executed and under what conditions.

[0069] The model can include layers 301-307. The layers are arranged in a stack. Each layer in the stack serves the layer one level higher than it (except for the application layer, which is the highest layer), and is served by the layer one level below it (except for the physical layer, which is the lowest layer). The physical layer is the lowest layer because it receives and transmits raw bytes of data, and the physical layer is the farthest layer from the user in a communications system. On the other hand, the application layer is the highest layer because it interacts directly with a software application.

[0070] As noted, the model includes a physical layer 301. Physical layer 301 represents physical communication and can define parameters of that physical communication. For example, such physical communication may come in the form of electrical, optical, or electromagnetic signals. Physical layer 301 also defines protocols that may control communications within a data transmission network.

[0071] Link layer 302 defines links and mechanisms used to transmit (i.e. , move) data across a network. The link layer 302 manages node-to-node communications, such as within a grid computing environment. Link layer 302 can detect and correct errors (e.g., transmission errors in the physical layer 301). Link layer 302 can also include a media access control (MAC) layer and logical link control (LLC) layer.

[0072] Network layer 303 defines the protocol for routing within a network. In other words, the network layer coordinates transferring data across nodes in a same network (e.g., such as a grid computing environment). Network layer 303 can also define the processes used to structure local addressing within the network.

[0073] Transport layer 304 can manage the transmission of data and the quality of the transmission and / or receipt of that data. Transport layer 304 can provide a protocol for transferring data, such as, for example, a Transmission Control Protocol (TCP). Transport layer304 can assemble and disassemble data frames for transmission. The transport layer can also detect transmission errors occurring in the layers below it.

[0074] Session layer 305 can establish, maintain, and manage communication connections between devices on a network. In other words, the session layer controls the dialogues or nature of communications between network devices on the network. The session layer may also establish checkpointing, adjournment, termination, and restart procedures.

[0075] Presentation layer 306 can provide translation for communications between the application and network layers. In other words, this layer may encrypt, decrypt and / or format data based on data types and / or encodings known to be accepted by an application or network layer.

[0076] Application layer 307 interacts directly with software applications and end users and manages communications between them. Application layer 307 can identify destinations, local resource states or availability and / or communication content or formatting using the applications.

[0077] Intra-network connection components 321 and 322 are shown to operate in lower levels, such as physical layer 301 and link layer 302, respectively. For example, a hub can operate in the physical layer, a switch can operate in the link layer, and a router can operate in the network layer. Inter-network connection components 323 and 328 are shown to operate on higher levels, such as layers 303-307. For example, routers can operate in the network layer and network devices can operate in the transport, session, presentation, and application layers.

[0078] As noted, a computing environment 314 can interact with and / or operate on, in various embodiments, one, more, all or any of the various layers. For example, computing environment 314 can interact with a hub (e.g., via the link layer) so as to adjust which devices the hub communicates with. The physical layer may be served by the link layer, so it may implement such data from the link layer. For example, the computing environment 314 may control which devices it will receive data from. For example, if the computing environment 314 knows that a certain network device has turned off, broken, or otherwise become unavailable or unreliable, the computing environment 314 may instruct the hub to prevent any data from being transmitted to the computing environment 314 from that network device. Such a process may be beneficial to avoid receiving data that is inaccurate or that has been influenced by an uncontrolled environment. As another example, computing environment 314 can communicate with a bridge, switch, router or gateway and influence which device within the system (e.g., system 200) the component selects as a destination. In some embodiments, computing environment 314 can interact with various layers by exchanging communications with equipment operating on a particular layer by routing or modifying existing communications. In another embodiment, such as in a grid computing environment, a node may determine how data within the environment should be routed (e.g., which node should receive certain data) based on certain parameters or information provided by other layers within the model.As noted, the computing environment 314 may be a part of a communications grid environment, the communications of which may be implemented as shown in the protocol of Figure 3. For example, referring back to Figure 2, one or more of machines 220 and 240 may be part of a communications grid computing environment. A gridded computing environment may be employed in a distributed system with non-interactive workloads where data resides in memory on the machines, or compute nodes. In such an environment, analytic code, instead of a database management system, controls the processing performed by the nodes. Data is colocated by pre-distributing it to the grid nodes, and the analytic code on each node loads the local data into memory. Each node may be assigned a particular task such as a portion of a processing project, or to organize or control other nodes within the grid.

[0079] Figure 4 illustrates a communications grid computing system 400 including a variety of control and worker nodes, according to embodiments of the present technology.

[0080] Communications grid computing system 400 includes three control nodes and one or more worker nodes. Communications grid computing system 400 includes control nodes 402, 404, and 406. The control nodes are communicatively connected via communication paths 451, 453, and 455. Therefore, the control nodes may transmit information (e.g., related to the communications grid or notifications), to and receive information from each other. Although communications grid computing system 400 is shown in Figure 4 as including three control nodes, the communications grid may include more or less than three control nodes.

[0081] Communications grid computing system (or just “communications grid”) 400 also includes one or more worker nodes. Shown in Figure 4 are six worker nodes 410-420.

[0082] Although Figure 4 shows six worker nodes, a communications grid according to embodiments of the present technology may include more or less than six worker nodes. The number of worker nodes included in a communications grid may be dependent upon how large the project or data set is being processed by the communications grid, the capacity of each worker node, the time designated for the communications grid to complete the project, among others. Each worker node within the communications grid 400 may be connected (wired or wirelessly, and directly or indirectly) to control nodes 402-406. Therefore, each worker node may receive information from the control nodes (e.g., an instruction to perform work on a project) and may transmit information to the control nodes (e.g., a result from work performed on a project). Furthermore, worker nodes may communicate with each other (either directly or indirectly). For example, worker nodes may transmit data between each other related to a job being performed or an individual task within a job being performed by that worker node. However, in certain embodiments, worker nodes may not, for example, be connected (communicatively or otherwise) to certain other worker nodes. In an embodiment, worker nodes may only be able to communicate with the control node that controls it, and may not be able to communicate with other worker nodes in the communications grid, whether they are other worker nodes controlledby the control node that controls the worker node, or worker nodes that are controlled by other control nodes in the communications grid.

[0083] A control node may connect with an external device with which the control node may communicate (e.g., a grid user, such as a server or computer, may connect to a controller of the grid). For example, a server or computer may connect to control nodes and may transmit a project or job to the node. The project may include a data set. The data set may be of any size. Once the control node receives such a project including a large data set, the control node may distribute the data set or projects related to the data set to be performed by worker nodes.

[0084] Alternatively, for a project including a large data set, the data set may be received or stored by a machine other than a control node (e.g., a HADOOP® standard-compliant data node employing the HADOOP® Distributed File System, or HDFS).

[0085] Control nodes may maintain knowledge of the status of the nodes in the grid (i.e., grid status information), accept work requests from clients, subdivide the work across worker nodes, and coordinate the worker nodes, among other responsibilities. Worker nodes may accept work requests from a control node and provide the control node with results of the work performed by the worker node. A grid may be started from a single node (e.g., a machine, computer, server, etc.). This first node may be assigned or may start as the primary control node that will control any additional nodes that enter the grid.

[0086] When a project is submitted for execution (e.g., by a client or a controller of the grid) it may be assigned to a set of nodes. After the nodes are assigned to a project, a data structure (i.e., a communicator) may be created. The communicator may be used by the project for information to be shared between the project codes running on each node. A communication handle may be created on each node. A handle, for example, is a reference to the communicator that is valid within a single process on a single node, and the handle may be used when requesting communications between nodes.

[0087] A control node, such as control node 402, may be designated as the primary control node. A server, computer or other external device may connect to the primary control node. Once the control node receives a project, the primary control node may distribute portions of the project to its worker nodes for execution. For example, when a project is initiated on communications grid 400, primary control node 402 controls the work to be performed for the project in order to complete the project as requested or instructed. The primary control node may distribute work to the worker nodes based on various factors, such as which subsets or portions of projects may be completed most efficiently and in the correct amount of time. For example, a worker node may perform analysis on a portion of data that is already local (e.g., stored on) the worker node. The primary control node also coordinates and processes the results of the work performed by each worker node after each worker node executes and completes its job. For example, the primary control node may receive a result from one or more worker nodes, and the control node may organize (e.g., collect and assemble) the resultsreceived and compile them to produce a complete result for the project received from the end user.

[0088] Any remaining control nodes, such as control nodes 404 and 406, may be assigned as backup control nodes for the project. In an embodiment, backup control nodes may not control any portion of the project. Instead, backup control nodes may serve as a backup for the primary control node and take over as primary control node if the primary control node were to fail. If a communications grid were to include only a single control node, and the control node were to fail (e.g., the control node is shut off or breaks) then the communications grid as a whole may fail, and any project or job being run on the communications grid may fail and may not complete. While the project may be run again, such a failure may cause a delay (severe delay in some cases, such as overnight delay) in completion of the project. Therefore, a grid with multiple control nodes, including a backup control node, may be beneficial.

[0089] To add another node or machine to the grid, the primary control node may open a pair of listening sockets, for example. A socket may be used to accept work requests from clients, and the second socket may be used to accept connections from other grid nodes. The primary control node may be provided with a list of other nodes (e.g., other machines, computers, servers) that will participate in the grid, and the role that each node will fill in the grid. Upon startup of the primary control node (e.g., the first node on the grid), the primary control node may use a network protocol to start the server process on every other node in the grid.

[0090] Command line parameters, for example, may inform each node of one or more pieces of information, such as: the role that the node will have in the grid, the host name of the primary control node, the port number on which the primary control node is accepting connections from peer nodes, among others. The information may also be provided in a configuration file, transmitted over a secure shell tunnel, recovered from a configuration server, among others. While the other machines in the grid may not initially know about the configuration of the grid, that information may also be sent to each other node by the primary control node. Updates of the grid information may also be subsequently sent to those nodes.

[0091] For any control node other than the primary control node added to the grid, the control node may open three sockets. The first socket may accept work requests from clients, the second socket may accept connections from other grid members, and the third socket may connect (e.g., permanently) to the primary control node. When a control node (e.g., primary control node) receives a connection from another control node, it first checks to see if the peer node is in the list of configured nodes in the grid. If it is not on the list, the control node may clear the connection. If it is on the list, it may then attempt to authenticate the connection. If authentication is successful, the authenticating node may transmit information to its peer, such as the port number on which a node is listening for connections, the host name of the node, information about how to authenticate the node, among other information. When a node, such as the new control node, receives information about another active node, it will check to see if italready has a connection to that other node. If it does not have a connection to that node, it may then establish a connection to that control node.

[0092] Any worker node added to the grid may establish a connection to the primary control node and any other control nodes on the grid. After establishing the connection, it may authenticate itself to the grid (e.g., any control nodes, including both primary and backup, or a server or user controlling the grid). After successful authentication, the worker node may accept configuration information from the control node.

[0093] When a node joins a communications grid (e.g., when the node is powered on or connected to an existing node on the grid or both), the node is assigned (e.g., by an operating system of the grid) a universally unique identifier (UIIID). This unique identifier may help other nodes and external entities (devices, users, etc.) to identify the node and distinguish it from other nodes. When a node is connected to the grid, the node may share its unique identifier with the other nodes in the grid. Since each node may share its unique identifier, each node may know the unique identifier of every other node on the grid. Unique identifiers may also designate a hierarchy of each of the nodes (e.g., backup control nodes) within the grid. For example, the unique identifiers of each of the backup control nodes may be stored in a list of backup control nodes to indicate an order in which the backup control nodes will take over for a failed primary control node to become a new primary control node. However, a hierarchy of nodes may also be determined using methods other than using the unique identifiers of the nodes. For example, the hierarchy may be predetermined or may be assigned based on other predetermined factors.

[0094] The grid may add new machines at any time (e.g., initiated from any control node).

[0095] Upon adding a new node to the grid, the control node may first add the new node to its table of grid nodes. The control node may also then notify every other control node about the new node. The nodes receiving the notification may acknowledge that they have updated their configuration information.

[0096] Primary control node 402 may, for example, transmit one or more communications to backup control nodes 404 and 406 (and, for example, to other control or worker nodes within the communications grid). Such communications may be sent periodically, at fixed time intervals, between known fixed stages of the project’s execution, among other protocols. The communications transmitted by primary control node 402 may be of varied types and may include a variety of types of information. For example, primary control node 402 may transmit snapshots (e.g., status information) of the communications grid so that backup control node 404 always has a recent snapshot of the communications grid. The snapshot or grid status may include, for example, the structure of the grid (including, for example, the worker nodes in the grid, unique identifiers of the nodes, or their relationships with the primary control node) and the status of a project (including, for example, the status of each worker node’s portion of the project). The snapshot may also include analysis or results received from worker nodes in thecommunications grid. The backup control nodes may receive and store the backup data received from the primary control node. The backup control nodes may transmit a request for such a snapshot (or other information) from the primary control node, or the primary control node may send such information periodically to the backup control nodes.

[0097] As noted, the backup data may allow the backup control node to take over as primary control node if the primary control node fails without requiring the grid to start the project over from scratch. If the primary control node fails, the backup control node that will take over as primary control node may retrieve the most recent version of the snapshot received from the primary control node and use the snapshot to continue the project from the stage of the project indicated by the backup data. This may prevent failure of the project as a whole.

[0098] A backup control node may use various methods to determine that the primary control node has failed. In one example of such a method, the primary control node may transmit (e.g., periodically) a communication to the backup control node that indicates that the primary control node is working and has not failed, such as a heartbeat communication. The backup control node may determine that the primary control node has failed if the backup control node has not received a heartbeat communication for a certain predetermined period of time. Alternatively, a backup control node may also receive a communication from the primary control node itself (before it failed) or from a worker node that the primary control node has failed, for example because the primary control node has failed to communicate with the worker node.

[0099] Different methods may be performed to determine which backup control node of a set of backup control nodes (e.g., backup control nodes 404 and 406) will take over for failed primary control node 402 and become the new primary control node. For example, the new primary control node may be chosen based on a ranking or “hierarchy” of backup control nodes based on their unique identifiers. In an alternative embodiment, a backup control node may be assigned to be the new primary control node by another device in the communications grid or from an external device (e.g., a system infrastructure or an end user, such as a server or computer, controlling the communications grid). In another alternative embodiment, the backup control node that takes over as the new primary control node may be designated based on bandwidth or other statistics about the communications grid.

[0100] A worker node within the communications grid may also fail. If a worker node fails, work being performed by the failed worker node may be redistributed amongst the operational worker nodes. In an alternative embodiment, the primary control node may transmit a communication to each of the operable worker nodes still on the communications grid that each of the worker nodes should purposefully fail also. After each of the worker nodes fail, they may each retrieve their most recent saved checkpoint of their status and re-start the project from that checkpoint to minimize lost progress on the project being executed.

[0101] Figure 5 illustrates a flow chart showing an example process 500 for adjusting a communications grid or a work project in a communications grid after a failure of a node,according to embodiments of the present technology. The process may include, for example, receiving grid status information including a project status of a portion of a project being executed by a node in the communications grid, as described in operation 502. For example, a control node (e.g., a backup control node connected to a primary control node and a worker node on a communications grid) may receive grid status information, where the grid status information includes a project status of the primary control node or a project status of the worker node. The project status of the primary control node and the project status of the worker node may include a status of one or more portions of a project being executed by the primary and worker nodes in the communications grid. The process may also include storing the grid status information, as described in operation 504. For example, a control node (e.g., a backup control node) may store the received grid status information locally within the control node.

[0102] Alternatively, the grid status information may be sent to another device for storage where the control node may have access to the information.

[0103] The process may also include receiving a failure communication corresponding to a node in the communications grid in operation 506. For example, a node may receive a failure communication including an indication that the primary control node has failed, prompting a backup control node to take over for the primary control node. In an alternative embodiment, a node may receive a failure that a worker node has failed, prompting a control node to reassign the work being performed by the worker node. The process may also include reassigning a node or a portion of the project being executed by the failed node, as described in operation 508. For example, a control node may designate the backup control node as a new primary control node based on the failure communication upon receiving the failure communication. If the failed node is a worker node, a control node may identify a project status of the failed worker node using the snapshot of the communications grid, where the project status of the failed worker node includes a status of a portion of the project being executed by the failed worker node at the failure time.

[0104] The process may also include receiving updated grid status information based on the reassignment, as described in operation 510, and transmitting a set of instructions based on the updated grid status information to one or more nodes in the communications grid, as described in operation 512. The updated grid status information may include an updated project status of the primary control node or an updated project status of the worker node. The updated information may be transmitted to the other nodes in the grid to update their stale stored information.

[0105] Figure 6 illustrates a portion of a communications grid computing system 600 including a control node and a worker node, according to embodiments of the present technology.

[0106] Communications grid 600 computing system includes one control node (control node 602) and one worker node (worker node 610) for purposes of illustration, but may include more worker and / or control nodes. The control node 602 is communicatively connected to worker node 610via communication path 650. Therefore, control node 602 may transmit information (e.g., related to the communications grid or notifications), to and receive information from worker node 610 via path 650.

[0107] Similar to in Figure 4, communications grid computing system (or just “communications grid”) 600 includes data processing nodes (control node 602 and worker node 610). Nodes 602 and 610 include multi-core data processors. Each node 602 and 610 includes a grid-enabled software component (GESC) 620 that executes on the data processor associated with that node and interfaces with buffer memory 622 also associated with that node. Each node 602 and 610 includes database management software (DBMS) 628 that executes on a database server (not shown) at control node 602 and on a database server (not shown) at worker node 610.

[0108] Each node also includes a data store 624. Data stores 624, similar to network-attached data stores 110 in Figure 1 and data stores 235 in Figure 2, are used to store data to be processed by the nodes in the computing environment. Data stores 624 may also store any intermediate or final data generated by the computing system after being processed, for example in non-volatile memory. However, in certain embodiments, the configuration of the grid computing environment allows its operations to be performed such that intermediate and final data results can be stored solely in volatile memory (e.g., RAM), without a requirement that intermediate or final data results be stored to non-volatile types of memory. Storing such data in volatile memory may be useful in certain situations, such as when the grid receives queries (e.g., ad hoc) from a client and when responses, which are generated by processing large amounts of data, need to be generated quickly or on-the-fly. In such a situation, the grid may be configured to retain the data within memory so that responses can be generated at different levels of detail and so that a client may interactively query against this information.

[0109] Each node also includes a user-defined function (UDF) 626. The UDF provides a mechanism for the DBMS 628 to transfer data to or receive data from the database stored in the data stores 624 that are managed by the DBMS. For example, UDF 626 can be invoked by the DBMS to provide data to the GESC for processing. The UDF 626 may establish a socket connection (not shown) with the GESC to transfer the data. Alternatively, the UDF 626 can transfer data to the GESC by writing data to shared memory accessible by both the UDF and the GESC.

[0110] The GESC 620 at the nodes 602 and 620 may be connected via a network, such as network 108 shown in Figure 1. Therefore, nodes 602 and 620 can communicate with each other via the network using a predetermined communication protocol such as, for example, the Message Passing Interface (MPI). Each GESC 620 can engage in point-to-point communication with the GESC at another node or in collective communication with multiple GESCs via the network. The GESC 620 at each node may contain identical (or nearly identical) software instructions. Each node may be capable of operating as either a control node or a worker node. The GESC at the control node 602 can communicate, over a communication path652, with a client device 630. More specifically, control node 602 may communicate with client application 632 hosted by the client device 630 to receive queries and to respond to those queries after processing large amounts of data.

[0111] DBMS 628 may control the creation, maintenance, and use of database or data structure (not shown) within a nodes 602 or 610. The database may organize data stored in data stores 624. The DBMS 628 at control node 602 may accept requests for data and transfer the appropriate data for the request. With such a process, collections of data may be distributed across multiple physical locations. In this example, each node 602 and 610 stores a portion of the total data managed by the management system in its associated data store 624.

[0112] Furthermore, the DBMS may be responsible for protecting against data loss using replication techniques. Replication includes providing a backup copy of data stored on one node on one or more other nodes. Therefore, if one node fails, the data from the failed node can be recovered from a replicated copy residing at another node. However, as described herein with respect to Figure 4, data or status information for each node in the communications grid may also be shared with each node on the grid.

[0113] Figure 7 illustrates a flow chart showing an example method 700 for executing a project within a grid computing system, according to embodiments of the present technology. As described with respect to Figure 6, the GESC at the control node may transmit data with a client device (e.g., client device 630) to receive queries for executing a project and to respond to those queries after large amounts of data have been processed. The query may be transmitted to the control node, where the query may include a request for executing a project, as described in operation 702. The query can contain instructions on the type of data analysis to be performed in the project and whether the project should be executed using the grid-based computing environment, as shown in operation 704.

[0114] To initiate the project, the control node may determine if the query requests use of the grid-based computing environment to execute the project. If the determination is no, then the control node initiates execution of the project in a solo environment (e.g., at the control node), as described in operation 710. If the determination is yes, the control node may initiate execution of the project in the grid-based computing environment, as described in operation 706. In such a situation, the request may include a requested configuration of the grid. For example, the request may include a number of control nodes and a number of worker nodes to be used in the grid when executing the project. After the project has been completed, the control node may transmit results of the analysis yielded by the grid, as described in operation 708. Whether the project is executed in a solo or grid-based environment, the control node provides the results of the project, as described in operation 712.

[0115] As noted with respect to Figure 2, the computing environments described herein may collect data (e.g., as received from network devices, such as sensors, such as network devices 204-209 in Figure 2, and client devices or other sources) to be processed as part of a dataanalytics project, and data may be received in real time as part of a streaming analytics environment (e.g., ESP). Data may be collected using a variety of sources as communicated via different kinds of networks or locally, such as on a real-time streaming basis. For example, network devices may receive data periodically from network device sensors as the sensors continuously sense, monitor and track changes in their environments. More specifically, an increasing number of distributed applications develop or produce continuously flowing data from distributed sources by applying queries to the data before distributing the data to geographically distributed recipients. An event stream processing engine (ESPE) may continuously apply the queries to the data as it is received and determines which entities should receive the data. Client or other devices may also subscribe to the ESPE or other devices processing ESP data so that they can receive data after processing, based on for example the entities determined by the processing engine. For example, client devices 230 in Figure 2 may subscribe to the ESPE in computing environment 214. In another example, event subscription devices 1024a-c, described further with respect to Figure 10, may also subscribe to the ESPE. The ESPE may determine or define how input data or event streams from network devices or other publishers (e.g., network devices 204-209 in Figure 2) are transformed into meaningful output data to be consumed by subscribers, such as for example client devices 230 in Figure 2.

[0116] Figure 8 illustrates a block diagram including components of an Event Stream Processing Engine (ESPE), according to embodiments of the present technology. ESPE 800 may include one or more projects 802. A project may be described as a second-level container in an engine model managed by ESPE 800 where a thread pool size for the project may be defined by a user. Each project of the one or more projects 802 may include one or more continuous queries 804 that contain data flows, which are data transformations of incoming event streams. The one or more continuous queries 804 may include one or more source windows 806 and one or more derived windows 808.

[0117] The ESPE may receive streaming data over a period of time related to certain events, such as events or other data sensed by one or more network devices. The ESPE may perform operations associated with processing data created by the one or more devices. For example, the ESPE may receive data from the one or more network devices 204-209 shown in Figure 2. As noted, the network devices may include sensors that sense different aspects of their environments, and may collect data over time based on those sensed observations. For example, the ESPE may be implemented within one or more of machines 220 and 240 shown in Figure 2. The ESPE may be implemented within such a machine by an ESP application. An ESP application may embed an ESPE with its own dedicated thread pool or pools into its application space where the main application thread can do application-specific work and the ESPE processes event streams at least by creating an instance of a model into processing objects.The engine container is the top-level container in a model that manages the resources of the one or more projects 802. In an illustrative embodiment, for example, there may be only one ESPE 800 for each instance of the ESP application, and ESPE 800 may have a unique engine name. Additionally, the one or more projects 802 may each have unique project names, and each query may have a unique continuous query name and begin with a uniquely named source window of the one or more source windows 806. ESPE 800 may or may not be persistent.

[0118] Continuous query modeling involves defining directed graphs of windows for event stream manipulation and transformation. A window in the context of event stream manipulation and transformation is a processing node in an event stream processing model. A window in a continuous query can perform aggregations, computations, pattern-matching, and other operations on data flowing through the window. A continuous query may be described as a directed graph of source, relational, pattern matching, and procedural windows. The one or more source windows 806 and the one or more derived windows 808 represent continuously executing queries that generate updates to a query result set as new event blocks stream through ESPE 800. A directed graph, for example, is a set of nodes connected by edges, where the edges have a direction associated with them.

[0119] An event object may be described as a packet of data accessible as a collection of fields, with at least one of the fields defined as a key or unique identifier (ID). The event object may be created using a variety of formats including binary, alphanumeric, XML, etc. Each event object may include one or more fields designated as a primary identifier (ID) for the event so ESPE 800 can support operation codes (opcodes) for events including insert, update, upsert, and delete. Upsert opcodes update the event if the key field already exists; otherwise, the event is inserted. For illustration, an event object may be a packed binary representation of a set of field values and include both metadata and field data associated with an event. The metadata may include an opcode indicating if the event represents an insert, update, delete, or upsert, a set of flags indicating if the event is a normal, partial-update, or a retention generated event from retention policy management, and a set of microsecond timestamps that can be used for latency measurements.

[0120] An event block object may be described as a grouping or package of event objects. An event stream may be described as a flow of event block objects. A continuous query of the one or more continuous queries 804 transforms a source event stream made up of streaming event block objects published into ESPE 800 into one or more output event streams using the one or more source windows 806 and the one or more derived windows 808. A continuous query can also be thought of as data flow modeling.

[0121] The one or more source windows 806 are at the top of the directed graph and have no windows feeding into them. Event streams are published into the one or more source windows 806, and from there, the event streams may be directed to the next set of connected windowsas defined by the directed graph. The one or more derived windows 808 are all instantiated windows that are not source windows and that have other windows streaming events into them. The one or more derived windows 808 may perform computations or transformations on the incoming event streams. The one or more derived windows 808 transform event streams based on the window type (that is operators such as join, filter, compute, aggregate, copy, pattern match, procedural, union, etc.) and window settings. As event streams are published into ESPE 800, they are continuously queried, and the resulting sets of derived windows in these queries are continuously updated.

[0122] Figure 9 illustrates a flow chart showing an example process including operations performed by an event stream processing engine, according to some embodiments of the present technology. As noted, the ESPE 800 (or an associated ESP application) defines how input event streams are transformed into meaningful output event streams. More specifically, the ESP application may define how input event streams from publishers (e.g., network devices providing sensed data) are transformed into meaningful output event streams consumed by subscribers (e.g., a data analytics project being executed by a machine or set of machines).

[0123] Within the application, a user may interact with one or more user interface windows presented to the user in a display under control of the ESPE independently or through a browser application in an order selectable by the user. For example, a user may execute an ESP application, which causes presentation of a first user interface window, which may include a plurality of menus and selectors such as drop-down menus, buttons, text boxes, hyperlinks, etc. associated with the ESP application as understood by a person of skill in the art. As further understood by a person of skill in the art, various operations may be performed in parallel, for example, using a plurality of threads.

[0124] At operation 900, an ESP application may define and start an ESPE, thereby instantiating an ESPE at a device, such as machine 220 and / or 240. In an operation 902, the engine container is created. For illustration, ESPE 800 may be instantiated using a function call that specifies the engine container as a manager for the model.

[0125] In an operation 904, the one or more continuous queries 804 are instantiated by ESPE 800 as a model. The one or more continuous queries 804 may be instantiated with a dedicated thread pool or pools that generate updates as new events stream through ESPE 800. For illustration, the one or more continuous queries 804 may be created to model business processing logic within ESPE 800, to predict events within ESPE 800, to model a physical system within ESPE 800, to predict the physical system state within ESPE 800, etc. For example, as noted, ESPE 800 may be used to support sensor data monitoring and management (e.g., sensing may include force, torque, load, strain, position, temperature, air pressure, fluid flow, chemical properties, resistance, electromagnetic fields, radiation, irradiance, proximity, acoustics, moisture, distance, speed, vibrations, acceleration, electrical potential, or electrical current, etc.).ESPE 800 may analyze and process events in motion or “event streams.” Instead of storing data and running queries against the stored data, ESPE 800 may store queries and stream data through them to allow continuous analysis of data as it is received. The one or more source windows 806 and the one or more derived windows 808 may be created based on the relational, pattern matching, and procedural algorithms that transform the input event streams into the output event streams to model, simulate, score, test, predict, etc. based on the continuous query model defined and application to the streamed data.

[0126] In an operation 906, a publish / subscribe (pub / sub) capability is initialized for ESPE 800. In an illustrative embodiment, a pub / sub capability is initialized for each project of the one or more projects 802. To initialize and enable pub / sub capability for ESPE 800, a port number may be provided. Pub / sub clients can use a host name of an ESP device running the ESPE and the port number to establish pub / sub connections to ESPE 800.

[0127] Figure 10 illustrates an ESP system 1000 interfacing between publishing device 1022 and event subscribing devices 1024a-c, according to embodiments of the present technology. ESP system 1000 may include ESP device or subsystem 1001, event publishing device 1022, an event subscribing device A 1024a, an event subscribing device B 1024b, and an event subscribing device C 1024c. Input event streams are output to ESP device 851 by publishing device 1022. In alternative embodiments, the input event streams may be created by a plurality of publishing devices. The plurality of publishing devices further may publish event streams to other ESP devices. The one or more continuous queries instantiated by ESPE 800 may analyze and process the input event streams to form output event streams output to event subscribing device A 1024a, event subscribing device B 1024b, and event subscribing device C 1024c. ESP system 1000 may include a greater or a fewer number of event subscribing devices of event subscribing devices.

[0128] Publish-subscribe is a message-oriented interaction paradigm based on indirect addressing. Processed data recipients specify their interest in receiving information from ESPE 800 by subscribing to specific classes of events, while information sources publish events to ESPE 800 without directly addressing the receiving parties. ESPE 800 coordinates the interactions and processes the data. In some cases, the data source receives confirmation that the published information has been received by a data recipient.

[0129] A publish / subscribe API may be described as a library that enables an event publisher, such as publishing device 1022, to publish event streams into ESPE 800 or an event subscriber, such as event subscribing device A 1024a, event subscribing device B 1024b, and event subscribing device C 1024c, to subscribe to event streams from ESPE 800. For illustration, one or more publish / subscribe APIs may be defined. Using the publish / subscribe API, an event publishing application may publish event streams into a running event stream processor project source window of ESPE 800, and the event subscription application may subscribe to an event stream processor project source window of ESPE 800.The publish / subscribe API provides cross-platform connectivity and endianness compatibility between ESP application and other networked applications, such as event publishing applications instantiated at publishing device 1022, and event subscription applications instantiated at one or more of event subscribing device A 1024a, event subscribing device B 1024b, and event subscribing device C 1024c.

[0130] Referring back to Figure 9, operation 906 initializes the publish / subscribe capability of ESPE 800. In an operation 908, the one or more projects 802 are started. The one or more started projects may run in the background on an ESP device. In an operation 910, an event block object is received from one or more computing device of the event publishing device 1022.

[0131] ESP subsystem 1001 may include a publishing client 1002, ESPE 800, a subscribing client A 1004, a subscribing client B 1006, and a subscribing client C 1008. Publishing client 1002 may be started by an event publishing application executing at publishing device 1022 using the publish / subscribe API. Subscribing client A 1004 may be started by an event subscription application A, executing at event subscribing device A 1024a using the publish / subscribe API. Subscribing client B 1006 may be started by an event subscription application B executing at event subscribing device B 1024b using the publish / subscribe API. Subscribing client C 1008 may be started by an event subscription application C executing at event subscribing device C 1024c using the publish / subscribe API.

[0132] An event block object containing one or more event objects is injected into a source window of the one or more source windows 806 from an instance of an event publishing application on event publishing device 1022. The event block object may be generated, for example, by the event publishing application and may be received by publishing client 1002. A unique ID may be maintained as the event block object is passed between the one or more source windows 806 and / or the one or more derived windows 808 of ESPE 800, and to subscribing client A 1004, subscribing client B 1006, and subscribing client C 1008 and to event subscription device A 1024a, event subscription device B 1024b, and event subscription device C 1024c. Publishing client 1002 may further generate and include a unique embedded transaction ID in the event block object as the event block object is processed by a continuous query, as well as the unique ID that publishing device 1022 assigned to the event block object.

[0133] In an operation 912, the event block object is processed through the one or more continuous queries 804. In an operation 914, the processed event block object is output to one or more computing devices of the event subscribing devices 1024a-c. For example, subscribing client A 1004, subscribing client B 1006, and subscribing client C 1008 may send the received event block object to event subscription device A 1024a, event subscription device B 1024b, and event subscription device C 1024c, respectively.

[0134] ESPE 800 maintains the event block containership aspect of the received event blocks from when the event block is published into a source window and works its way through thedirected graph defined by the one or more continuous queries 804 with the various event translations before being output to subscribers. Subscribers can correlate a group of subscribed events back to a group of published events by comparing the unique ID of the event block object that a publisher, such as publishing device 1022, attached to the event block object with the event block ID received by the subscriber.

[0135] In an operation 916, a determination is made concerning whether or not processing is stopped. If processing is not stopped, processing continues in operation 910 to continue receiving the one or more event streams containing event block objects from the, for example, one or more network devices. If processing is stopped, processing continues in an operation 918. In operation 918, the started projects are stopped. In operation 920, the ESPE is shutdown.

[0136] As noted, in some embodiments, big data is processed for an analytics project after the data is received and stored. In other embodiments, distributed applications process continuously flowing data in real-time from distributed sources by applying queries to the data before distributing the data to geographically distributed recipients. As noted, an event stream processing engine (ESPE) may continuously apply the queries to the data as it is received and determines which entities receive the processed data. This allows for large amounts of data being received and / or collected in a variety of environments to be processed and distributed in real time. For example, as shown with respect to Figure 2, data may be collected from network devices that may include devices within the internet of things, such as devices within a home automation network. However, such data may be collected from a variety of different resources in a variety of different environments. In any such situation, embodiments of the present technology allow for real-time processing of such data.

[0137] Aspects of the current disclosure provide technical solutions to technical problems, such as computing problems that arise when an ESP device fails which results in a complete service interruption and potentially significant data loss. The data loss can be catastrophic when the streamed data is supporting mission critical operations such as those in support of an ongoing manufacturing or drilling operation. An embodiment of an ESP system achieves a rapid and seamless failover of ESPE running at the plurality of ESP devices without service interruption or data loss, thus significantly improving the reliability of an operational system that relies on the live or real-time processing of the data streams. The event publishing systems, the event subscribing systems, and each ESPE not executing at a failed ESP device are not aware of or effected by the failed ESP device. The ESP system may include thousands of event publishing systems and event subscribing systems. The ESP system keeps the failover logic and awareness within the boundaries of out-messaging network connector and out-messaging network device.

[0138] In one example embodiment, a system is provided to support a failover when event stream processing (ESP) event blocks. The system includes, but is not limited to, an out-messaging network device and a computing device. The computing device includes, but is not limited to, a processor and a computer-readable medium operably coupled to the processor. The processor is configured to execute an ESP engine (ESPE). The computer-readable medium has instructions stored thereon that, when executed by the processor, cause the computing device to support the failover. An event block object is received from the ESPE that includes a unique identifier. A first status of the computing device as active or standby is determined. When the first status is active, a second status of the computing device as newly active or not newly active is determined. Newly active is determined when the computing device is switched from a standby status to an active status. When the second status is newly active, a last published event block object identifier that uniquely identifies a last published event block object is determined. A next event block object is selected from a non-transitory computer-readable medium accessible by the computing device. The next event block object has an event block object identifier that is greater than the determined last published event block object identifier. The selected next event block object is published to an out-messaging network device. When the second status of the computing device is not newly active, the received event block object is published to the out-messaging network device. When the first status of the computing device is standby, the received event block object is stored in the non-transitory computer-readable medium.

[0139] Figure 11 is a flow chart of an example of a process for generating and using a machinelearning model according to some aspects. Machine learning is a branch of artificial intelligence that relates to mathematical models that can learn from, categorize, and make predictions about data. Such mathematical models, which can be referred to as machine-learning models, can classify input data among two or more classes; cluster input data among two or more groups; predict a result based on input data; identify patterns or trends in input data; identify a distribution of input data in a space; or any combination of these. Examples of machinelearning models can include (i) neural networks; (ii) decision trees, such as classification trees and regression trees; (iii) classifiers, such as Naive bias classifiers, logistic regression classifiers, ridge regression classifiers, random forest classifiers, least absolute shrinkage and selector (LASSO) classifiers, and support vector machines; (iv) clusterers, such as k-means clusterers, mean-shift clusterers, and spectral clusterers; (v) factorizers, such as factorization machines, principal component analyzers and kernel principal component analyzers; and (vi) ensembles or other combinations of machine-learning models. In some examples, neural networks can include deep neural networks, feed-forward neural networks, recurrent neural networks, convolutional neural networks, radial basis function (RBF) neural networks, echo state neural networks, long short-term memory neural networks, bi-directional recurrent neural networks, gated neural networks, hierarchical recurrent neural networks, stochastic neural networks, modular neural networks, spiking neural networks, dynamic neural networks,cascading neural networks, neuro-fuzzy neural networks, transformer networks, large language models (LLMs), agents of LLMs, multi-modal models, or any combination of these.

[0140] Different machine-learning models may be used interchangeably to perform a task. Examples of tasks that can be performed at least partially using machine-learning models include various types of scoring; bioinformatics; cheminformatics; software engineering; fraud detection; customer segmentation; generating online recommendations; adaptive websites; determining customer lifetime value; search engines; placing advertisements in real time or near real time; classifying DNA sequences; affective computing; performing natural language processing and understanding; object recognition and computer vision; robotic locomotion; playing games; optimization and metaheuristics; detecting network intrusions; medical diagnosis and monitoring; or predicting when an asset, such as a machine, will need maintenance.

[0141] Any number and combination of tools can be used to create machine-learning models. Examples of tools for creating and managing machine-learning models can include SAS® Enterprise Miner, SAS® Rapid Predictive Modeler, and SAS® Model Manager, SAS Cloud Analytic Services (CAS) ®, SAS Viya ® of all which are by SAS Institute Inc. of Cary, North Carolina.

[0142] Machine-learning models can be constructed through an at least partially automated (e.g., with little or no human involvement) process called training. During training, input data can be iteratively supplied to a machine-learning model to enable the machine-learning model to identify patterns related to the input data or to identify relationships between the input data and output data. With training, the machine-learning model can be transformed from an untrained state to a trained state. Input data can be split into one or more training sets and one or more validation sets, and the training process may be repeated multiple times. The splitting may follow a k-fold cross-validation rule, a leave-one-out-rule, a leave-p-out rule, or a holdout rule. An overview of training and using a machine-learning model is described below with respect to the flow chart of Figure 11.

[0143] In block 1102, training data is received. In some examples, the training data is received from a remote database or a local database, constructed from various subsets of data, or input by a user. The training data can be used in its raw form for training a machine-learning model or pre-processed into another form, which can then be used for training the machine-learning model. For example, the raw form of the training data can be smoothed, truncated, aggregated, clustered, or otherwise manipulated into another form, which can then be used for training the machine-learning model.

[0144] In block 1104, a machine-learning model is trained using the training data. The machine-learning model can be trained in a supervised, unsupervised, or semi-supervised manner. In supervised training, each input in the training data is correlated to a desired output. This desired output may be a scalar, a vector, or a different type of data structure such as text or an image. This may enable the machine-learning model to learn a mapping between theinputs and desired outputs. In unsupervised training, the training data includes inputs, but not desired outputs, so that the machine-learning model has to find structure in the inputs on its own. In semi-supervised training, only some of the inputs in the training data are correlated to desired outputs.

[0145] In block 1106, the machine-learning model is evaluated. For example, an evaluation dataset can be obtained, for example, via user input or from a database. The evaluation dataset can include inputs correlated to desired outputs. The inputs can be provided to the machine-learning model and the outputs from the machine-learning model can be compared to the desired outputs. If the outputs from the machine-learning model closely correspond with the desired outputs, the machine-learning model may have a high degree of accuracy. For example, if 90% or more of the outputs from the machine-learning model are the same as the desired outputs in the evaluation dataset, the machine-learning model may have a high degree of accuracy. Otherwise, the machine-learning model may have a low degree of accuracy. The 90% number is an example only. A realistic and desirable accuracy percentage is dependent on the problem and the data.

[0146] In some examples, if, at 1108, the machine-learning model has an inadequate degree of accuracy for a particular task, the process can return to block 1104, where the machine-learning model can be further trained using additional training data or otherwise modified to improve accuracy. However, if, at 1108. the machine-learning model has an adequate degree of accuracy for the particular task, the process can continue to block 1110.

[0147] In block 1110, new data is received. In some examples, the new data is received from a remote database or a local database, constructed from various subsets of data, or input by a user. The new data may be unknown to the machine-learning model. For example, the machine-learning model may not have previously processed or analyzed the new data.

[0148] In block 1112, the trained machine-learning model is used to analyze the new data and provide a result. For example, the new data can be provided as input to the trained machinelearning model. The trained machine-learning model can analyze the new data and provide a result that includes a classification of the new data into a particular class, a clustering of the new data into a particular group, a prediction based on the new data, or any combination of these.

[0149] In block 1114, the result is post-processed. For example, the result can be added to, multiplied with, or otherwise combined with other data as part of a job. As another example, the result can be transformed from a first format, such as a time series format, into another format, such as a count series format. Any number and combination of operations can be performed on the result during post-processing.

[0150] A more specific example of a machine-learning model is the neural network 1200 shown in Figure 12. The neural network 1200 is represented as multiple layers of neurons 1208 that can exchange data between one another via connections 1255 that may be selectively instantiated thereamong. The layers include an input layer 1202 for receiving input dataprovided at inputs 1222, one or more hidden layers 1204, and an output layer 1206 for providing a result at outputs 1277. The hidden layer(s) 1204 are referred to as hidden because they may not be directly observable or have their inputs or outputs directly accessible during the normal functioning of the neural network 1200. Although the neural network 1200 is shown as having a specific number of layers and neurons for exemplary purposes, the neural network 1200 can have any number and combination of layers, and each layer can have any number and combination of neurons.

[0151] The neurons 1208 and connections 1255 thereamong may have numeric weights, which can be tuned during training of the neural network 1200. For example, training data can be provided to at least the inputs 1222 to the input layer 1202 of the neural network 1200, and the neural network 1200 can use the training data to tune one or more numeric weights of the neural network 1200. In some examples, the neural network 1200 can be trained using backpropagation. Backpropagation can include determining a gradient of a particular numeric weight based on a difference between an actual output of the neural network 1200 at the outputs 1277 and a desired output of the neural network 1200. Based on the gradient, one or more numeric weights of the neural network 1200 can be updated to reduce the difference therebetween, thereby increasing the accuracy of the neural network 1200. This process can be repeated multiple times to train the neural network 1200. For example, this process can be repeated hundreds or thousands of times to train the neural network 1200.

[0152] In some examples, the neural network 1200 is a feed-forward neural network. In a feedforward neural network, the connections 1255 are instantiated and / or weighted so that every neuron 1208 only propagates an output value to a subsequent layer of the neural network 1200. For example, data may only move one direction (forward) from one neuron 1208 to the next neuron 1208 in a feed-forward neural network. Such a "forward" direction may be defined as proceeding from the input layer 1202 through the one or more hidden layers 1204, and toward the output layer 1206.

[0153] In other examples, the neural network 1200 may be a recurrent neural network. A recurrent neural network can include one or more feedback loops among the connections 1255, thereby allowing data to propagate in both forward and backward through the neural network 1200. Such a "backward" direction may be defined as proceeding in the opposite direction of forward, such as from the output layer 1206 through the one or more hidden layers 1204, and toward the input layer 1202. This can allow for information to persist within the recurrent neural network. For example, a recurrent neural network can determine an output based at least partially on information that the recurrent neural network has seen before, giving the recurrent neural network the ability to use previous input to inform the output.

[0154] In some examples, the neural network 1200 operates by receiving a vector of numbers from one layer; transforming the vector of numbers into a new vector of numbers using a matrix of numeric weights, a nonlinearity, or both; and providing the new vector of numbers to asubsequent layer ("subsequent" in the sense of moving "forward") of the neural network 1200. Each subsequent layer of the neural network 1200 can repeat this process until the neural network 1200 outputs a final result at the outputs 1277 of the output layer 1206. For example, the neural network 1200 can receive a vector of numbers at the inputs 1222 of the input layer 1202. The neural network 1200 can multiply the vector of numbers by a matrix of numeric weights to determine a weighted vector. The matrix of numeric weights can be tuned during the training of the neural network 1200. The neural network 1200 can transform the weighted vector using a nonlinearity, such as a sigmoid tangent or the hyperbolic tangent. In some examples, the nonlinearity can include a rectified linear unit, which can be expressed using the equation y = max(x, 0) where y is the output and x is an input value from the weighted vector. The transformed output can be supplied to a subsequent layer (e.g., a hidden layer 1204) of the neural network 1200. The subsequent layer of the neural network 1200 can receive the transformed output, multiply the transformed output by a matrix of numeric weights and a nonlinearity, and provide the result to yet another layer of the neural network 1200 (e.g., another, subsequent, hidden layer 1204). This process continues until the neural network 1200 outputs a final result at the outputs 1277 of the output layer 1206.

[0155] As also depicted in Figure 12, the neural network 1200 may be implemented either through the execution of the instructions of one or more routines 1244 by central processing units (CPUs), or through the use of one or more neuromorphic devices 1250 that incorporate a set of memristors (or other similar components) that each function to implement one of the neurons 1208 in hardware. Where multiple neuromorphic devices 1250 are used, they may be interconnected in a depth-wise manner to enable implementing neural networks with greater quantities of layers, and / or in a width-wise manner to enable implementing neural networks having greater quantities of neurons 1208 per layer.

[0156] The neuromorphic device 1250 may incorporate a storage interface 1299 by which neural network configuration data 1293 that is descriptive of various parameters and hyper parameters of the neural network 1200 may be stored and / or retrieved. More specifically, the neural network configuration data 1293 may include such parameters as weighting and / or biasing values derived through the training of the neural network 1200, as has been described. Alternatively, or additionally, the neural network configuration data 1293 may include such hyperparameters as the manner in which the neurons 1208 are to be interconnected (e.g., feedforward or recurrent), the trigger function to be implemented within the neurons 1208, the quantity of layers and / or the overall quantity of the neurons 1208. The neural network configuration data 1293 may provide such information for more than one neuromorphic device 1250 where multiple ones have been interconnected to support larger neural networks.

[0157] Other examples of the present disclosure may include any number and combination of machine-learning models having any number and combination of characteristics. The machinelearning model(s) can be trained in a supervised, semi-supervised, or unsupervised manner, orany combination of these. The machine-learning model(s) can be implemented using a single computing device or multiple computing devices, such as the communications grid computing system 400 discussed above.

[0158] Implementing some examples of the present disclosure at least in part by using machine-learning models can reduce the total number of processing iterations, time, memory, electrical power, or any combination of these consumed by a computing device when analyzing data. For example, a neural network may more readily identify patterns in data than other approaches. This may enable the neural network and / or a transformer model to analyze the data using fewer processing cycles and less memory than other approaches, while obtaining a similar or greater level of accuracy.

[0159] Some machine-learning approaches may be more efficiently and speedily executed and processed with machine-learning specific processors (e.g., not a generic CPU). Such processors may also provide an energy savings when compared to generic CPUs. For example, some of these processors can include a graphical processing unit (GPU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), an artificial intelligence (Al) accelerator, a neural computing core, a neural computing engine, a neural processing unit, a purpose-built chip architecture for deep learning, and / or some other machine-learning specific processor that implements a machine learning approach or one or more neural networks using semiconductor (e.g., silicon (Si), gallium arsenide(GaAs)) devices. These processors may also be employed in heterogeneous computing architectures with a number of and / or a variety of different types of cores, engines, nodes, and / or layers to achieve various energy efficiencies, processing speed improvements, data communication speed improvements, and / or data efficiency targets and improvements throughout various parts of the system when compared to a homogeneous computing architecture that employs CPUs for general purpose computing.

[0160] Figure 13 illustrates various aspects of the use of containers 1336 as a mechanism to allocate processing, storage and / or other resources of a processing system 1300 to the performance of various analyses. More specifically, in a processing system 1300 that includes one or more node devices 1330 (e.g., the aforedescribed grid system 400), the processing, storage and / or other resources of each node device 1330 may be allocated through the instantiation and / or maintenance of multiple containers 1336 within the node devices 1330 to support the performance(s) of one or more analyses. As each container 1336 is instantiated, predetermined amounts of processing, storage and / or other resources may be allocated thereto as part of creating an execution environment therein in which one or more executable routines 1334 may be executed to cause the performance of part or all of each analysis that is requested to be performed.

[0161] It may be that at least a subset of the containers 1336 are each allocated a similar combination and amounts of resources so that each is of a similar configuration with a similarrange of capabilities, and therefore, are interchangeable. This may be done in embodiments in which it is desired to have at least such a subset of the containers 1336 already instantiated prior to the receipt of requests to perform analyses, and thus, prior to the specific resource requirements of each of those analyses being known.

[0162] Alternatively, or additionally, it may be that at least a subset of the containers 1336 are not instantiated until after the processing system 1300 receives requests to perform analyses where each request may include indications of the resources required for one of those analyses. Such information concerning resource requirements may then be used to guide the selection of resources and / or the amount of each resource allocated to each such container 1336. As a result, it may be that one or more of the containers 1336 are caused to have somewhat specialized configurations such that there may be differing types of containers to support the performance of different analyses and / or different portions of analyses.

[0163] It may be that the entirety of the logic of a requested analysis is implemented within a single executable routine 1334. In such embodiments, it may be that the entirety of that analysis is performed within a single container 1336 as that single executable routine 1334 is executed therein. However, it may be that such a single executable routine 1334, when executed, is at least intended to cause the instantiation of multiple instances of itself that are intended to be executed at least partially in parallel. This may result in the execution of multiple instances of such an executable routine 1334 within a single container 1336 and / or across multiple containers 1336.

[0164] Alternatively, or additionally, it may be that the logic of a requested analysis is implemented with multiple differing executable routines 1334. In such embodiments, it may be that at least a subset of such differing executable routines 1334 are executed within a single container 1336. However, it may be that the execution of at least a subset of such differing executable routines 1334 is distributed across multiple containers 1336.

[0165] Where an executable routine 1334 of an analysis is under development, and / or is under scrutiny to confirm its functionality, it may be that the container 1336 within which that executable routine 1334 is to be executed is additionally configured assist in limiting and / or monitoring aspects of the functionality of that executable routine 1334. More specifically, the execution environment provided by such a container 1336 may be configured to enforce limitations on accesses that are allowed to be made to memory and / or I / O addresses to control what storage locations and / or I / O devices may be accessible to that executable routine 1334. Such limitations may be derived based on comments within the programming code of the executable routine 1334 and / or other information that describes what functionality the executable routine 1334 is expected to have, including what memory and / or I / O accesses are expected to be made when the executable routine 1334 is executed. Then, when the executable routine 1334 is executed within such a container 1336, the accesses that areattempted to be made by the executable routine 1334 may be monitored to identify any behavior that deviates from what is expected.

[0166] Where the possibility exists that different executable routines 1334 may be written in different programming languages, it may be that different subsets of containers 1336 are configured to support different programming languages. In such embodiments, it may be that each executable routine 1334 is analyzed to identify what programming language it is written in, and then what container 1336 is assigned to support the execution of that executable routine 1334 may be at least partially based on the identified programming language. Where the possibility exists that a single requested analysis may be based on the execution of multiple executable routines 1334 that may each be written in a different programming language, it may be that at least a subset of the containers 1336 are configured to support the performance of various data structure and / or data format conversion operations to enable a data object output by one executable routine 1334 written in one programming language to be accepted as an input to another executable routine 1334 written in another programming language.

[0167] As depicted, at least a subset of the containers 1336 may be instantiated within one or more VMs 1331 that may be instantiated within one or more node devices 1330. Thus, in some embodiments, it may be that the processing, storage and / or other resources of at least one node device 1330 may be partially allocated through the instantiation of one or more VMs 1331, and then in turn, may be further allocated within at least one VM 1331 through the instantiation of one or more containers 1336.

[0168] In some embodiments, it may be that such a nested allocation of resources may be carried out to affect an allocation of resources based on two differing criteria. By way of example, it may be that the instantiation of VMs 1331 is used to allocate the resources of a node device 1330 to multiple users or groups of users in accordance with any of a variety of service agreements by which amounts of processing, storage and / or other resources are paid for each such user or group of users. Then, within each VM 1331 or set of VMs 1331 that is allocated to a particular user or group of users, containers 1336 may be allocated to distribute the resources allocated to each VM 1331 among various analyses that are requested to be performed by that particular user or group of users.

[0169] As depicted, where the processing system 1300 includes more than one node device 1330, the processing system 1300 may also include at least one control device 1350 within which one or more control routines 1354 may be executed to control various aspects of the use of the node device(s) 1330 to perform requested analyses. By way of example, it may be that at least one control routine 1354 implements logic to control the allocation of the processing, storage and / or other resources of each node device 1300 to each VM 1331 and / or container 1336 that is instantiated therein. Thus, it may be the control device(s) 1350 that effects a nested allocation of resources, such as the aforedescribed example allocation of resources based on two differing criteria.As also depicted, the processing system 1300 may also include one or more distinct requesting devices 1370 from which requests to perform analyses may be received by the control device(s) 1350. Thus, and by way of example, it may be that at least one control routine 1354 implements logic to monitor for the receipt of requests from authorized users and / or groups of users for various analyses to be performed using the processing, storage and / or other resources of the node device(s) 1330 of the processing system 1300. The control device(s) 1350 may receive indications of the availability of resources, the status of the performances of analyses that are already underway, and / or still other status information from the node device(s) 1330 in response to polling, at a recurring interval of time, and / or in response to the occurrence of various preselected events. More specifically, the control device(s) 1350 may receive indications of status for each container 1336, each VM 1331 and / or each node device 1330. At least one control routine 1354 may implement logic that may use such information to select container(s) 1336, VM(s) 1331 and / or node device(s) 1330 that are to be used in the execution of the executable routine(s) 1334 associated with each requested analysis.

[0170] As further depicted, in some embodiments, the one or more control routines 1354 may be executed within one or more containers 1356 and / or within one or more VMs 1351 that may be instantiated within the one or more control devices 1350. It may be that multiple instances of one or more varieties of control routine 1354 may be executed within separate containers 1356, within separate VMs 1351 and / or within separate control devices 1350 to better enable parallelized control over parallel performances of requested analyses, to provide improved redundancy against failures for such control functions, and / or to separate differing ones of the control routines 1354 that perform different functions. By way of example, it may be that multiple instances of a first variety of control routine 1354 that communicate with the requesting device(s) 1370 are executed in a first set of containers 1356 instantiated within a first VM 1351 , while multiple instances of a second variety of control routine 1354 that control the allocation of resources of the node device(s) 1330 are executed in a second set of containers 1356 instantiated within a second VM 1351. It may be that the control of the allocation of resources for performing requested analyses may include deriving an order of performance of portions of each requested analysis based on such factors as data dependencies thereamong, as well as allocating the use of containers 1336 in a manner that effectuates such a derived order of performance.

[0171] Where multiple instances of control routine 1354 are used to control the allocation of resources for performing requested analyses, such as the assignment of individual ones of the containers 1336 to be used in executing executable routines 1334 of each of multiple requested analyses, it may be that each requested analysis is assigned to be controlled by just one of the instances of control routine 1354. This may be done as part of treating each requested analysis as one or more "ACID transactions" that each have the four properties of atomicity, consistency, isolation and durability such that a single instance of control routine 1354 is given full controlover the entirety of each such transaction to better ensure that either all of each such transaction is either entirely performed or is entirely not performed. As will be familiar to those skilled in the art, allowing partial performances to occur may cause cache incoherencies and / or data corruption issues.

[0172] As additionally depicted, the control device(s) 1350 may communicate with the requesting device(s) 1370 and with the node device(s) 1330 through portions of a network 1399 extending thereamong. Again, such a network as the depicted network 1399 may be based on any of a variety of wired and / or wireless technologies and may employ any of a variety of protocols by which commands, status, data and / or still other varieties of information may be exchanged. It may be that one or more instances of a control routine 1354 cause the instantiation and maintenance of a web portal or other variety of portal that is based on any of a variety of communication protocols, etc. (e.g., a restful API). Through such a portal, requests for the performance of various analyses may be received from requesting device(s) 1370, and / or the results of such requested analyses may be provided thereto. Alternatively, or additionally, it may be that one or more instances of a control routine 1354 cause the instantiation of and maintenance of a message passing interface and / or message queues. Through such an interface and / or queues, individual containers 1336 may each be assigned to execute at least one executable routine 1334 associated with a requested analysis to cause the performance of at least a portion of that analysis.

[0173] Although not specifically depicted, it may be that at least one control routine 1354 may include logic to implement a form of management of the containers 1336 based on the Kubernetes container management platform promulgated by Cloud Native Computing Foundation of San Francisco, CA, USA. In such embodiments, containers 1336 in which executable routines 1334 of requested analyses may be instantiated within "pods" (not specifically shown) in which other containers may also be instantiated for the execution of other supporting routines. Such supporting routines may cooperate with control routine(s) 1354 to implement a communications protocol with the control device(s) 1350 via the network 1399 (e.g., a message passing interface, one or more message queues, etc.). Alternatively, or additionally, such supporting routines may serve to provide access to one or more storage repositories (not specifically shown) in which at least data objects may be stored for use in performing the requested analyses.

[0174] Resource management computing systems generate allowable resource allocations (e.g., available rooms, seats, storage facilities, or vehicles for rent). Allowable resource allocations can exceed current resources available. For example, hotel properties use resource management systems to manage overbooking recommendations for room types. A recommendation could be for instance an allowable quantity of overbooking for a particular room type.Figure 14 illustrates a block diagram of a system 1400 for overriding a resource allocation (e.g., by a resource management system, RMS). System 1400 includes a computing device 1450. The computing device 1450 has a computer-readable medium 1460 and a processor 1454. Computer-readable medium 1460 is an electronic holding place or storage for information so processor 1454 can access the information. Computer-readable medium 1460 can include, but is not limited to, any type of random access memory (RAM), any type of read only memory (ROM), any type of flash memory, etc. such as magnetic storage devices (e.g., hard disk, floppy disk, magnetic strips), optical disks (e.g., compact disc (CD), digital versatile disc (DVD)), smart cards, flash memory devices, etc.

[0175] Processor 1454 executes instructions (e.g., stored at the computer-readable medium 1460). The instructions can be carried out by a special purpose computer, logic circuits, or hardware circuits. In one or more embodiments, processor 1454 is implemented in hardware and / or firmware. Processor 1454 executes an instruction, meaning it performs or controls the operations called for by that instruction. The term “execution” is the process of running an application or the carrying out of the operation called for by an instruction. The instructions can be written using one or more programming language, scripting language, assembly language, etc. Processor 1454 in one or more embodiments can retrieve a set of instructions from a permanent memory device and copy the instructions in an executable form to a temporary memory device that is generally some form of RAM, for example. In one or more embodiments, computing device 1450 is a computing system because Processor 1454 operably couples with components of computing device 1450 (e.g., input and / or output interface 1452 and with computer-readable medium 1460) to receive, to send, and to process information.

[0176] In one or more embodiments, computing device 1450 is a control computing system using processor 1454 and computer-readable medium 1460 to control an RMS 1410. Resource management systems are present in many industries. For example, hospitality industry systems use resource management systems to allocate hotel rooms, vehicles rentals, parking spots, airline seats, cruise services, food storage. As another example, network industry systems allocate network resources in computer networks and cloud computing services. RMS 1410 controls a schedule 1420 with generated resource allocation(s) 1422 for different time blocks according to a schedule. In some embodiments, the resource allocations 1422 are computer-generated. Alternatively, or additionally, a user of the RMS 1410 can input resources allocation(s) 1422 into the RMS 1410. RMSs can allocate these types of resources by assigning them to an individual or providing vouchers for those types of items (e.g., on specific time blocks on the schedule).

[0177] RMS 1410 can use RMS resource parameter(s) 1430 to generate computer-generated resource allocation(s) 1422. For instance, an RMS in the agricultural industry systems could allocate resources through terms in option contracts and or storage / land usage agreements based on a variety of factors that could affect supply and demand such as weather conditions,number of animals, etc. Based on these factors an RMS could allocate a certain quantity of chickens that are available for purchase per day or reserve a certain amount of storage locations for storage for forecasted eggs.

[0178] RMS 1410 is itself a computing system or a part of a computing system (e.g., computing device 1450). For example, computer-readable medium 1460 stores a set of computing instructions 1480 for executing the control computing system. In some embodiments, it stores RMS information 1490 that can include a separate set of computing instructions for executing the RMS (e.g., resource allocation recommendations). Alternatively, RMS 1410 is operated on a separate computing system (not shown) external to computing device 1450. Computer-readable medium 1460 can store RMS information 1490 received from the external computing system executing the RMS (e.g., received information indicating resource allocation(s) 1422). In embodiments, the RMS 1410 is external to computing device 1450, and computing device 1450 can receive information from RMS 1410 (e.g., RMS information 1490) and send information to RMS 1410 to control RMS 1410 (e.g., using input and / or output interface 1452). The computing device 1450 can receive RMS information 1490 from another computing system (e.g., RMS 1410) or from manual input by a user of the RMS 1410 via an input device (not shown) such as a keyboard or touch screen for entry of data. In embodiments, the computing device 1450 does not receive information from RMS 1410. Instead, computing device 1450 controls the RMS 1410 through one-sided transmission of information to a remote computing system for the RMS 1410. Regardless of whether the RMS 1410 is a part of the computing device 1450, the computing device 1450 can execute computing instructions for the RMS 1410 and the computing instructions 1480 separately (e.g., different programs or instruction sets store the different computing instructions).

[0179] In one or more embodiments, computing device 1450 controls RMS 1410 by overriding a resource allocation recommendation by RMS 1410 (e.g., by generating and / or sending an override indication 1440). For example, the computing device 1450 can use the computing instructions 1480 to generate a scaling factor 1484 for an initial resource allocation (e.g., resource allocation 1422A for a first time block of a schedule). The scaling factor can be based on control resource parameter(s) 1482 that are specific to the control computing system. For instance, the computing device 1450 can use the computing instructions 1480 to compute scaling factor 1484 based on the control resource parameter(s) 1482.

[0180] The control resource parameter(s) 1482 and RMS resource parameter(s) can be disjoint sets in that there is no parameter in common between them. For instance, the control resource parameter(s) 1482 can include parameter(s) that could be unknown to the RMS 1410 such as publicly available information about other providers. Public databases and websites share such information such as information relevant to rooms available at other hotels. The computing device 1450 can collect or receive this information to generate control resource parameter(s) 1482 to better forecast room reservations for hotel rooms controlled by the RMS 1410. Forexample, hotels information within a certain geographic region can be gathered to determine available supply at other hotels if a hotel cannot honor their room reservations due to overbooking. With overbooking the hotel has allowed for reservation of resources at the hotel above available supply on a particular occupancy day, and a customer could need to find another hotel, or a customer needs to be booked at another hotel (e.g., walked over) if reservations are not cancelled before the occupancy day. As another example, the control resource parameter(s) 1482 can include parameter(s) that are additional constraints not implemented by the RMS 1410. For instance, the RMS resource parameter(s) 1430 could be a part of an algorithm developed before an unknown event such as natural disaster, or a change in global trade or governmental policies. Rather than change the RMS 1410, the control resource parameter(s) 1482 can consider these additional constraints to determine whether to override resource allocations by RMS 1410.

[0181] Additionally, control resource parameter(s) 1482 could include, or be updated overtime to include, parameters that have aspects of commonality with parameters considered by the resource management system 1410 even though the parameters themselves are different. For example, resource management system 1410 can consider past resource use in resource allocation compared to the RMS 1410 resource allocation recommendations. In contrast, the computing instructions 1480 can be used to receive feedback based on actual resource use compared to the modified allocated resource use for a time block in the schedule 1420 for resource allocation 1422A. The computing device 1450 can use the computing instructions 1480 to update, according to the feedback, the control resource parameter(s) 1482 and generate an updated scaling factor according to the updated set of resource parameters for overriding resource allocations for a second time block of the schedule 1420 (e.g., resource allocation 1422B). The computing device 1450 can use machine learning algorithms to dynamically update computer functions using the control resource parameter(s) 1482.

[0182] Alternatively in embodiments, RMS resource parameter(s) 1430 and control resource parameter(s) 1482 can be mutually inclusive or intersecting sets with some or all parameters in common (e.g., to implement different weights to factors).

[0183] Regardless of whether control resource parameter(s) 1482 and RMS resource parameter(s) are disjoint sets, in embodiments, the control resource parameter(s) 1482 can be influenced by the RMS 1410 or a user of the RMS 1410. For instance, computing device 1410 can receive a control instruction according to a user of the RMS 1410 setting a value or an assessment for one or more of the control resource parameter(s) 1482. For example, control resource parameter(s) 1482 could include an aggressiveness factor controlling how aggressive the computing device 1450 should be at overriding resource allocation(s) 1422. A control instruction from the RMS 1410 or a user of the RMS 1410 can control the aggressiveness factor. As another example, a setting can control in what instances the computing device can generate an override indication (e.g., a threshold to meet).In embodiments, system 1400 includes one or more output devices (e.g., one or more display devices) for outputting override indication 1440 via input and / or output interfaces 1452. For example, a display device (not shown) can display a scaling factor 1484 or a modified computer-generated resource allocation (e.g., based on scaling factor 1484) in a graphical user interface. For example, if the RMS 1410 manages seats on a plane the resource allocation could be a limit for the quantity of seats allowed for overbooking on a particular flight according to a flight schedule. The RMS 1410 can display the quantity and / or use the quantity to control allowed overbooking within RMS system devices or components. By controlling the RMS 1410, the system 1400 can consider additional constraints or information not considered by the RMS 1410.

[0184] The system 1400 is configured to exchange information between devices in the system (e.g., via wired and / or wireless transmission). For example, a network (not shown) can connect one or more devices of system 1400 to one or more other devices of system 1400.

[0185] Alternatively, or additionally, the system 1400 is integrated into one device (e.g., computing device 1450) or integrated with other analytical tools. For example, one or more aspects of system 1400 can be integrated data with analytics software application and / or software architecture such as that offered by SAS Institute Inc. or JMP Statistical Discovery LLC of Cary, N.C., USA. Merely for illustration, the applications are implemented using or integrated with one or more SAS software tools such as JMP®, Base SAS, SAS® Enterprise Miner™, SAS / STAT®, SAS® High Performance Analytics Server, SAS® Visual Data Mining and Machine Learning, SAS® LASR™ SAS® In-Database Products, SAS® Scalable Performance Data Engine, SAS® Cloud Analytic Services, SAS / OR®, SAS / ETS®, SAS® Inventory Optimization, SAS® Inventory Optimization Workbench, SAS® Visual Analytics, SAS® Viya™, SAS In-Memory Statistics for Hadoop®, SAS® Forecast Server, and SAS / IML®.

[0186] One or more applications or computing instructions stored on computer-readable medium 1460 can be implemented as part of a Web application. For example, an application can be configured to receive hypertext transport protocol (HTTP) responses and to send HTTP requests. The HTTP responses may include web pages such as hypertext markup language (HTML) documents and linked objects generated in response to the HTTP requests. Each web page may be identified by a uniform resource locator (URL) that includes the location or address of the computing device that contains the resource to be accessed in addition to the location of the resource on that computing device. The type of file or resource depends on the Internet application protocol such as the file transfer protocol, HTTP, H.323, etc. The file accessed may be a simple text file, an image file, an audio file, a video file, an executable, a common gateway interface application, a Java applet, an extensible markup language (XML) file, or any other type of file supported by HTTP.

[0187] In one or more embodiments, fewer, different, and additional components can be incorporated into device(s) of system 1400. For instance, input and / or output interface 1452could be multiple different interface uses the same or different technology. They can be internal interfaces (e.g., if the computer-readable medium 1460 comprises instructions for both the resource management computing system and the control computing system). They can be external interfaces (e.g., if the resource management computing system and the control computing system are executed in different computing systems).

[0188] In one or more embodiments, a computing system (e.g., the system 1400 or computing device 1450) implements a method as described herein (e.g., a method shown in Figure 15A or Figure 15B) for overriding a computer-generated resource allocation. Figure 15A illustrates a flow diagram for a method 1500 of overriding a computer-generated resource allocation.

[0189] The method 1500 includes an operation 1501 for generating, based on a first set of resource parameters, a scaling factor for an initial resource allocation. For example, the initial resource allocation could be an allocation of a quantity of rooms that can be overbooked for a given time period by a resource management computing system (e.g., for hotel). The scaling factor can adjust the quantity of rooms to a different quantity for that time period. The method 1500 includes an operation 1502 for overriding, based on the scaling factor, the initial resource allocation to a modified computer-generated resource allocation. For example, a control computing system can override by scaling an initial resource allocation by a resource management computing system and sending to the resource management computing system, the scaled resource allocation. As another example, a control computing system can override by sending an indication of the scaling factor, or a way to retrieve the scaled resource allocation or scaling factor. In embodiments, by overriding the initial resource allocation, no changes need to be made to the system (e.g., a resource management computing system) making the initial allocation to consider additional constraints.

[0190] Figure 15B illustrates a flow diagram for a method 1550 of overriding a computergenerated resource allocation by an example resource management computing system. The method 1550 includes an operation 1552 for generating, by a control computing system, based on a first set of resource parameters (e.g., public information related to other providers), a scaling factor for an initial resource allocation. The initial resource allocation can allocate resources for a first time block of multiple time blocks. The multiple time blocks can be, for example, according to a schedule controlled by a resource management computing system. For example, if the resource management system allocates rooms for overbooking, the schedule may provide different overbooking allowances per day. The initial resource allocation is generated by the resource management computing system executing a second set of computing instructions separately from the first set of computing instructions. For example, the resource management computing system and control computing system can be separate devices remote from one another. Alternatively, they operate on a same computing device but through different program packages. The second set of computing instructions are operable to cause the resource management computing system to generate the initial resource allocationbased on a second set of resource parameters (e.g., ones considered by the resource management computing system such as supply and demand at locations controlled by the resource management computing system). In this example, the first set of resource parameters and the second set of resource parameters are disjoint sets (e.g., the resource management computing system considers factors available to the resource management computing system and the computing system considers factors unconsidered by the resource management computing system such as public information related to other providers). In other examples, they include overlapping parameters.

[0191] The method 1550 includes an operation 1553 for overriding, based on the scaling factor, the initial resource allocation to a modified computer-generated resource allocation for the first time block. In some examples, the operations 1552 and / or 1553 is proceeded by an operation 1551 for receiving, from the resource management computing system, at the control computing system, the initial resource allocation. For instance, the generation of the scaling factor or the overriding can be responsive to received information in operation 1551. For instance, operation 1553 can include generating the modified computer-generated resource accounting for scaling, based on the scaling factor, the initial resource allocation, and sending, from the control computing system, to the resource management computing system, the modified computergenerated resource allocation.

[0192] However, since the scaling factor in embodiments uses a different set of parameters (i.e., disjoint sets), a control computing system can determine the scaling factor without the initial resource allocation. In some cases, the control computing system can override the initial resource allocation by outputting an indication of the scaling factor to the resource management system for scaling at the resource management system or by outputting retrieval information for retrieving the scaling factor (e.g., a URL). Regardless of whether the control computing system has the initial resource allocation by a resource management computing system, it can modify the initial resource allocation (e.g., using a scaling factor). Embodiments are versatile and useful across various systems and service industries that implement resource recommendations (e.g., hospitality industries, agricultural industries, network and computing industries).

[0193] Figure 16 illustrates a system 1600 for modifying a resource management system output. In Figure 16, a resource management system 1601 implements initial resource recommendation(s) in an interim resource recommendation(s) stage 1602. An automatic scaling stage 1603 occurs (e.g., to generate a scaling factor and / or scale an interim resource recommendation). The automatic scaling stage 1603 can consider additional constraints 1605 the resource management system 1601 does not consider initially and / or that are uncontrolled by the resource management system 1601. Additional constraints 1605 could include, for example, public information concerning capacity of alternative resources or other providers of resources. For example, it may be less preferrable to allocate resources more than available resources at a location where there are not alternatives sources for those resources at otherlocations. Alternatively, or additionally, additional constraints 1650 could include customer provided information such as geographic area of customers. For instance, it may be less preferable to not provide for customers who are coming from a greater distance to use those resources. By providing post-processing of interim resource recommendation(s) 1602, embodiments avoid redesigning what can be an already complex optimization system.

[0194] Alternatively, or additionally, embodiments avoid rerunning optimization algorithms or altering existing optimization systems. For instance, in the hotel industry a resource management system can make a recommendation on the order of once or twice a day, whereas the automatic scaling stage 1603 can occur more frequently or dynamically (e.g., as new constraint information becomes available). This can provide lower complexity and improved efficiency in determining final recommendations as there is no need to run a full optimization as additional constraints become available.

[0195] The automatic scaling stage 1603 outputs final resource recommendation(s) in a final resource recommendation(s) stage 1604 which in some cases can override the interim resource recommendation(s). In embodiments, the automatic scaling stage 1603 can also receive feedback from the final resource recommendation(s) stage 1604 (e.g., a resource allocation compared to actual use of the resource allocation for a given period of time). The automatic scaling stage can then update (e.g., using a machine learning model) according to the feedback, the automatic scaling stage 1603 (e.g., updating resource parameters to generate an updated scaling factor). For example, the additional constraints 1605 could include an aggressiveness parameter for how aggressive to be at scaling an interim resource recommendation, and a machine learning model can be used to adjust this parameter based on actual resource use.

[0196] One type of resource allocation addressed by embodiments includes resource allocation over available resource (e.g., overbooking). Embodiments described herein tackle the challenges of managing overbooking recommendations in constrained conditions. Figures 17A-17B presents an example in optimizing reservation systems involving overbooking recommendations.

[0197] Figure 17A illustrates a resource management computing system 1700. This example resource management computing system allocates resources by generating overbooking recommendation(s) 1707. In this example, overbooking recommendation(s) 1707 are the output of an optimization algorithm performed by an optimization stage 1704. Resource management computing systems herein can also be referred to as a resource management system or an RMS. In some cases, a resource management computing system can be called a revenue management system such as when the system can output recommendations on pricing (e.g., pricing recommendations 1705) or forecast revenue (e.g., revenue forecast 1706). These output recommendations can be computer-generated based on input to the optimization stage 1704. Input to optimization stage 1704 can include, for example, configuration data 1701 suchas settings for optimization. Alternatively, or additionally, input to optimization stage 1704 can include inventory fact data 1702 such as the available inventory of resources controlled by the resource management computing system 1700. Alternatively, or additionally, input to optimization stage 1704 can include demand data 1703 (e.g., estimation and forecast data for demand for resources controlled by the resource management computing system 1700). After the optimization stage 1704 runs the optimization algorithm, typically a computing system performs no other adjustments to the overbooking recommendations, though some resource management computing systems can receive user input within the resource management computing system to adjust overbooking recommendations.

[0198] Figure 17B illustrates scaling system 1750 for modifying overbooking recommendations for a resource management computing system (e.g., resource management computing system in Figure 17A). For example, the scaling system 1750 can be a post-processing on the overbooking recommendations output from an optimization algorithm. For example, scaling system 1750 provides post-processing for optimization stage 1704 from resource management computing system 1700 in Figure 17A. The optimization stage 1704 still outputs overbooking recommendations, but these recommendations after optimization are only interim recommendations (e.g., interim overbooking recommendations 1751). In this example, postprocessing occurs in automatic scaling stage 1752 (e.g., using an autoscaling algorithm based on the market conditions).

[0199] Many hotel properties face a challenge in managing overbooking recommendations under conditions of limited market capacity. In this situation, it will be very unlikely that a property can walk an overbooked customer to another hotel because the whole market becomes constrained. Additionally, the cost of walking a customer often rises higher due to room availability shortage in other hotels. Scaling system 1750 manages these risks in Figure 17B by taking as additional information 1754 (e.g., other provider information).

[0200] In general, public information of the market is available (e.g., through third-party providers) and hence, a control computing system can perform automatic scaling based on provider information (e.g., in automatic scaling stage 1752). An available provider in the context of the hotel industry means a hotel property that has available rooms or percentage of rooms to move an overbooked customer (i.e. , walk over). An unavailable provider has no available rooms and / or only a certain percentage of rooms remaining. Based on market conditions, a computing system can feed public third-party provider information to an automatic scaling algorithm to process the interim overbooking recommendations to generate the final overbooking recommendations 1753.

[0201] In some example embodiments, the scaling system 1750 can be improved further by feeding back the final overbooking recommendations 1753 to the automatic scaling stage 1752. For instance, the automatic scaling algorithm can learn and improve scaling settings and parameters based on past overbooking recommendations and machine learning algorithms.Figure 18 illustrates a scaling factor graph 1800 for a scaling stage (e.g., automatic scaling stage 1752). The scaling factor graph 1800 in this example presents a linear autoscaling approach for the autoscaling factor F0Dwhen R0D< R'0D< 1 where

[0202] OD denotes occupancy date;

[0203] ROD is a defined as an unavailable provider ratio or threshold setting; and

[0204] RQDis an unavailable provider ratio currently observed by the system for the future OD and is defined as:

[0205] ' — _ Number of Unavailable Providers on OD

[0206] 0DT otal Number of Providers

[0207] The number of unavailable providers in this example is a quantity of providers with no longer available resources. The R0Dcan be set either by the RMS manager or set adaptively for a future OD. A machine learning algorithm can adapt settings of this threshold by training and learning from past overbooking recommendations and their effect on the property revenue. Alternatively, or additionally, a user of the RMS can set or change threshold settings.

[0208] In the example the R0Dis 0.4 and corresponds to the line 1810 in scaling factor graph 1800. In section 1822 of the scaling factor curve below the R0Dthe autoscaling factor F0Dis 1 because the R'0Dis less than or equal to the R0D. At values where R'0D> 1 , the F0D= 0.

[0209] In that sense R0Dis a threshold such that when the threshold is unmet (e.g., when less than or equal to 0.4), a control computing system need not change or scale an initial resource allocation and can refrain from doing so or simply set the scale to 1, so the original value remains unchanged. However, when the threshold is met, a control computing system generates a scaling factor. In section 1824 of the scaling factor curve, the scaling factor F0Dis computed as:

[0210] when R0D< R'0D< 1

[0211]

[0212] Figure 19A illustrates a table 1900 of computer-generated overbooking recommendations according to this linear approach. This shows an example of the difference between original recommendations by a resource management system and auto-scaled overbooking recommendations according to a control system for different days of a schedule with occupancy dates given in column 1910. Resource management computing systems can allocate a resource amount more than an available resource amount controlled by the resource management computing system (e.g., overbooking). Original overbooking recommendations are shown in column 1920. For instance, row 1902 shows an original booking recommendation of 20 rooms that can be overbooked above available rooms for an occupancy date of January 6, 2025. Column 1930 shows a provider ratio. In this example, the provider ratio is a ratio of a quantity of providers in a geographic area who no longer have resources available to a totalamount of providers in the geographic area. For instance, for row 1902 half of available provider options are taken. A generated scale factor is shown in column 1940. In this example, the scaling factor is a multiplier for an initial resource allocation in column 1920. A computing system overrides the initial resource allocation to a modified computer-generated resource allocation by multiplying the initial resource allocation by the scaling factor. An auto-scaled overbooking recommendation is shown in column 1950. In this case of row 1902, the original overbooking recommendations are scaled by 0.5. This scaling results in a total number of allowed rooms for overbooking of 10. This resource allocation is still more than available resources controlled by the resource management system, but it is less than the original resource allocation.

[0213] In some cases, scaling could override a resource allocation by restricting overbooking all together. For instance, row 1904 shows a restriction of overbooking such that 0 rooms are allowed to be overbooked. In row 1904 the scaling factor in column 1940 is set to 0 to prevent further resource allocation for an occupancy date time block associated with an initial computergenerated resource allocation in column 1920 and a modified computer-generated resource in column 1940. In some cases, a control system does not modify resource management system resource allocations or scales them by 1, e.g., in rows 1906.

[0214] In this example, the recommendations are related to resource allocation for rooms, but the resource allocations could be for other resources. For example, the overbooking recommendations in column 1910 could be rental vehicles and occupancy dates are days a given vehicle is rented. As another example, the overbooking recommendations in column 1910 could be the number of seats on a plane and column 1910 could be associated with a time block based on a flight departure time and / or flight number. In each situation the scale factor in column 1940 would reduce or leave unchanged the quantity of rooms, seats, or vehicles for a given time block initially allocated by a resource management system. The auto-scaled overbooking recommendation in column 1950 would allocate a quantity of items for temporary use in a period of time for the given time block. Other approaches could be used (e.g., approaches that would increase resource allocation or decrease resource allocations in a nonlinear approach as will be described in more detail with respect to other examples).

[0215] Additionally, resource management system recommendations and control computing systems can be at different frequencies. For example, Figure 19B shows a table 1960 of computer-generated overbooking recommendations for an occupancy day of January 17, 2025, shown in column 1962. In this example, a resource management system generates the resource allocations on January 1, 2025 at different time points with time stamps shown in column 1961. The RMS updates their recommendations less frequently in this example (e.g., 6-hour increments). Column 1663 shows the initial resource allocations that do not include modifications by a control system. The original overbooking recommendations shown in column 1663 only changed at two time points - 8AM and 2PM. However, as shown in the provider ratiocolumn 1964, provider information can change dramatically between those time periods. In this example, a control computing system employing an autoscaling stage updates at a higher frequency than the RMS (e.g., every 3 hours). Column 1965 shows the resulting scale factor and column 1966 shows the auto-scaled overbooking recommendations are very different than the original overbooking recommendations for these different time stamps (including restricting all overbooking at 11AM). Embodiments can capture this variability without needing to rerun computationally expensive optimization algorithms or alter existing optimization systems that provide original overbooking recommendations.

[0216] Figures 19A-19B illustrates computer-generated overbooking recommendations according to the linear approach in Figure 18. A control computing system can use other linear or non-linear approaches (e.g., non-increasing function). For example, in embodiments a control computing system uses multiple parameters to compute F0D. For example, a control computing system can generate the scaling factor based on a function of an aggressiveness factor such as an aggressiveness scale p. An example non-linear function is shown:

[0217] >

[0218]

[0219] Using an aggressiveness scale provides a more flexible type of decreasing function. For example, if 0<p< 1 then this function is convex, which means overbooking reduction is more aggressive in the beginning. If > 1 on the other hand, this function is concave which means overbooking reduction is more aggressive later. The value of p can control the aggressiveness of autoscaling. As with the unavailable provider ratio / threshold R0D, this p value can be also either pre-set by the RMS manager, or an adaptive setting approach of p via a machine learning algorithm, i.e. , p adaptation can be via the RMS with feedback. Furthermore, p can be dependent on the OD where in this case the RMS could substitute p with0Din the above function for F0D. Functions and thresholds and other parameters for the function can be further configured by the user or auto set based on machine learning. Using these different decreasing function approaches, a control computing system can scale down overbooking recommendations based on market conditions (e.g., as capacity of alternative resources becomes more and more limited).

[0220] The computing system can receive updates to parameters such as threshold and aggressiveness factors and generate a scaling factor responsive to the updates. The scaling factor can scale a computer-generated resource allocation based on different parameters. For instance, the initial allocation could be based on demand and supply for the resource whereas the function can scale based on aggressiveness and provider information, which a resourcemanagement system does not consider in the initial allocation. Modified or scaled resource allocation can be displayed in a graphical user interface in different ways.

[0221] Figures 20A-20B illustrate graphical user interfaces for room reservations. In Figure 20A, the graphical user interface 2000 shows a modified computer-generated resource allocation with respect to specific rooms in overbooking limit fields 2020. For example, overbooking limit field 2020A provides a quantity of king-bed rooms that can be overbooked. Overbooking limit field 2020B provides a quantity of queen-bed rooms that can be overbooked. Settings control 2010 allows for change of parameters for control of the resource allocation and / or its display.

[0222] For instance, Figure 20B shows a graphical user interface 2040 with different setting options for the display of the graphical user interface 2000. For example, display options 2050 control how to display in a graphical user interface override recommendations for a resource management computing system. For instance, in embodiments, a control computing system overrides an initial resource allocation by outputting indication(s) to the resource management computing system to display in a graphical user interface of the resource management computing system. In this example, the overbooking limit fields 2020 in Figure 20A show a modified computer-generated resource allocation because this selection is set by the display options 2050. Additionally, or alternatively, the display options 2050 provide for selection of displaying a scaling factor in a graphical user interface. This could provide useful information at a glance regarding how much scaling is occurring. Alternatively, or additionally, the display options 2050 provide for selection of displaying a retrieval link for retrieving the scaling factor or the modified computer-generated resource allocation. This can be useful in situations where users of the resource management system frequently view the graphical user interface, or a control computing system updates the scaling factor frequently. In situations like this, the user may prefer a generally static graphical user interface view and only want to retrieve this information in certain circumstances (e.g., when booking a reservation).

[0223] A graphical user interface in embodiments can also allow for adjustments to one or more functions or algorithms for controlling the modifications to resource allocations. For instance, a control computing system can use a function for post-processing with a threshold and an aggressiveness factor that is specific to each room type. A resource management system can set the initial overbooking allocation for each room (e.g., based on a computer-generated value or a user updating textboxes 2060) in graphical user interface 2040. Graphical user interface 2040 displays an editable text box 2080A to allow for changing a threshold value for a scaling factor for king rooms. An editable text box 2080B allows for changing a threshold value for a scaling factor for a queen room. By changing one or more values in the editable text box(s) 2080 the computing system can receive an update to the threshold and generate the scaling factor responsive to the update to the threshold (e.g., changing the function for determining the scaling factor).Additionally, graphical user interface 2040 allows for a user to set an aggressiveness factor 2090 for each of the room types. For instance, a user may want to be more aggressive with overbooking a king room because there may be more king bed options in the market or consumers may be more willing to accept a queen bedroom because perhaps there are two beds in that room. Providing specific functions per room type allows overbooking recommendations to capture more market constraints specific to resource types.

[0224] In some embodiments, the user can also set how often the control computing system overrides, or determines whether to override, an initial allocation. For example, textboxes 2070 allow a user to set a frequency for updating or modifying an initial allocation. This frequency can also be specific to the resource allocation (e.g., queen rooms in this example are updated more frequently). Alternatively, or additionally, a control system can also generate multiple different scaling factors specific to different resource types managed by the resource management system. These settings can be without regard to whether the resource management system sets different initial allocations for these room types. Figures 20A and 20B provide an example of graphical user interfaces relevant to hotel reservations, but the teachings are applicable to other resource allocations and industries using a resource management computing system.

[0225] For example, Figure 21 illustrates a graphical user interface 2100 for a resource management system managing rental reservations. In this example, the overbooking limit for sports utility vehicles (SUV) and compact vehicles is initially set to the same overbooking limit by the resource management system in field 2110 and field 2112, respectively. As shown in Figure 21 , the scale generated for each of these resource types can be different because a control computing system controlling the resource management system can multiple different scaling factors specific to different resource types managed by the resource management system. For example, the scaling factor in field 2120 is associated with a first resource type related to SUVs, and the scaling factor in field 2122 is associated with a second resource type related to compact vehicles. It can be advantageous in certain situations to have different overbooking limits for different resource types. For instance, in some embodiments, the resource management system does not take into account the different resource types (e.g., having a same overbooking limit based on factors such as total vehicle supply). The control computing system can better accommodate specific supply of resource types. Alternatively, or additionally, provider supply of different resource types may be different, and the scaling factor can better accommodate options for walking over customers if the vehicle is still overbooked on the day of arrival. As shown in graphical user interface 2100, the SUV resource has already overbooked seven vehicles and with the scaling factor limit for compact resources that would be more than would be allowed with the scaling factor in field 2122.

[0226] In embodiments, the control system can override the initial computer-generated resource allocation based on determining the initial computer-generated resource allocation is specific toa given resource type (e.g., displaying the scale in association with the appropriate resource type as shown or displaying a modified overbooking limit as in Figure 20A). Settings control 2130 provides a way for navigating toward a settings window similar to graphical user interface 2040 in Figure 20B for changing the display of graphical user interface 2250.

[0227] In some embodiments, the resource management system allocates resources using a distribution of rewards, values, or constraints in a given time period. For instance, rather than assigning or reserving a vehicle to a particular individual as in Figure 21, instead a certain quantity of offers for reserving a vehicle can be distributed or displayed (e.g., responsive to booking a hotel room). Figure 22A illustrates a graphical user interface 2200 for a resource management system automatically allocating resources for voucher distribution. In this example, the control computing system can control the voucher limit for distribution of vouchers. Graphical user interface 2200 shows initial voucher limits for different voucher types in fields 2202. In this case the control computing system overrides this voucher limit by restricting a computing system distribution of vouchers. Rather than display the scaling of this limit, the graphical user interface displays resource specific control links 2204 for checking the scaling in fields should a user of the resource management system want to inspect the operations of the control computing system acting on the resource management system. In other embodiments, the specific scaling factor or modified limit can be displayed in graphical user interface 2200 as described with respect to Figures 21A and Figure 22. Settings control 2210 provides a way for navigating toward a settings window similar to graphical user interface 2040 in Figure 20B for changing the display of graphical user interface 2200.

[0228] As another example, a resource management system can allocate resources by limiting the option or constraints on resource availability. Figure 22B illustrates a graphical user interface 2250 for options related to purchase of livestock (e.g., chickens) or agricultural product (e.g., eggs). For example, a supplier can provide restaurants with an option for a quantity of chickens the restaurant can purchase. That option can be for a period of time such as per day, per week or per month. Limits can be set for specific periods of time to ensure that the supplier has enough livestock or livestock product available given factors such as time needed to replenish supply. For example, it may take time for resources to become available (e.g., to harvest supply or grow supply). In this case the limit may not be an overbooking but a constraint to ensure a pipeline of supply. Here field 2252 provides a chicken option showing the current resources ordered and a limit control 2254 to navigate toward limit information.

[0229] Alternatively, or additionally, the resource availability could be option or constraints on items needed for resources (e.g., storage of resources). For instance, egg options could be related to options needed for rental of refrigeration space of gathered eggs. In this case field 2256 shows current ordered storage needs for eggs and limit control 2258 is displayed for navigating toward limit information. Settings control 2260 provides a way for navigating towarda settings window similar to graphical user interface 2040 in Figure 20B for changing the display of graphical user interface 2250.

[0230] Figure 23 illustrates a graphical user interface 2300 for setting thresholds for overbooking rooms. Control setting 2390 allows a user to control post-processing of overbooking recommendations by a resource management system. If the control setting 2390 is unchecked a control system for the resource management system will refrain from overriding recommendations of the resource management system pertaining to overbooking. Here the control setting 2390 is checked allowing a user to also set a threshold for a scaling factor for all the resource types (e.g., superior king, superior queen, patio king, patio queen, and mountain view king rooms). In this example, when the threshold is unmet (e.g., when <80% of providers no longer had available resources), then the computing system can refrain from changing a computer-generated resource allocation for overbooking. When the threshold is met, a control computing system will reduce overbooking (e.g., by generating a scaling factor). Generating a scaling factor can be responsive to meeting the threshold or a change in the threshold (e.g., editing the text box to change the 80% to a different percentage). In other embodiments, a default threshold or a control system could determine a threshold (e.g., using machine learning). Alternatively, or additionally, thresholds could be per resource type. The graphical user interface 2300 can also display other information or access points for information relevant to resource management such as “room class”, “walk”, “room type”, “upgrade path”, “group information”, “special-use room type” information. Selecting “distribute unsold capacity” allows the computing system to automatically provide unsold resources to other providers.

[0231] Figure 24 illustrates a graphical user interface 2400 for displaying multiple time periods of overbooking information in a calendar viewer 2410 in view of the threshold set in Figure 23. Each of multiple time blocks of a schedule for resource allocation are shown in calendar viewer 2410 (e.g., section 2420 shows a check-in day for an equivalent occupancy period of time that does not overlap with occupancy time periods of other check-in days). In this example, a resource management system allocates resources at a location of a hotel managed by a resource management system. For example, the resource management system allows for hotel room reservation and overbookings at different time blocks shown on a calendar viewer 2410. For instance, section 2420 shows recommended rooms to overbook (OVBK) above hotel capacity for that date (e.g., 6 room limit). This recommended number in some embodiments can be the RMS resource allocation or a modified resource allocation of an RMS allocation modified by the control system. For instance, in section 2420 this was the RMS allocation. Whereas in section 2430 the computer icon on a given day indicates that autoscaling feature is in effect for that date, meaning that the threshold of unavailable providers set in graphical user interface 2300 of Figure 23 is met, and the system begins to scale down overbooking. Other information can be shown from the resource management system such as an Occupancy Forecast (OF) indicating how busy the hotel is expected to be for that date (e.g., 96.4% full insection 2420) and the bookings expected to cancel or not show up (e.g., Wash of 4.3% in section 2420).

[0232] Autoscaling in embodiments described herein can be based on received indication of public databases comprises resource conditions for one or more other locations not managed by the resource management computing system (e.g., other hotels or similar occupancy information can be gathered from online booking sites like ones operated by Travelscape, LLC and Expedia, Inc. in Seattle, Washington; Kayak Software Corporation in Stamford, Connecticut; Trivago in Germany; and Booking.com B.V. in the Netherlands).

[0233] In embodiments a user can also override resource management system information (e.g., initial or controlled resource management system information). For example, a location icon indicates a user override of resource management system information (e.g., location icon 2440). For instance, in section 2430 the user overrode the wash indication value for a particular occupancy day based on their own expectations of cancellations or “no shows” for that date. Users can use the controls 2450 for modifying resource allocations. For instance, a “multiple days overrides” allows a user to apply a setting change to multiple days. A “what if” option allows a user to explore scenarios for modifying RMS allocations. A “ceiling default” option allows a user to set a ceiling or a maximum overbooking limit for a day of the week.

[0234] The graphical user interface 2400 was shown with respect to room occupancy merely as an example, the allocated resources in the calendar viewer 2410 could be related to other resource allocations such as seats (e.g., on a plane or auditorium), rental vehicles, food storage or food options for each time block of the calendar viewer 2410. Embodiments are useful for controlling a variety of resources allocations managed by resource management computing systems.

Claims

CLAIMSWhat is claimed is:

1. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, the computer-program product including a first set of computing instructions operable to cause a control computing system to:generate, based on a first set of resource parameters, a scaling factor for an initial resource allocation,wherein the initial resource allocation allocates resources for a first time block of multiple time blocks;wherein the multiple time blocks are according to a schedule controlled by a resource management computing system;wherein the initial resource allocation is generated by the resource management computing system executing a second set of computing instructions separately from the first set of computing instructions;wherein the second set of computing instructions are operable to cause the resource management computing system to generate the initial resource allocation based on a second set of resource parameters;wherein the first set of resource parameters and the second set of resource parameters are disjoint sets; andoverride, based on the scaling factor, the initial resource allocation to a computergenerated resource allocation for the first time block, wherein the computergenerated resource allocation is a modification of the initial resource allocation.

2. The computer-program product of claim 1 , wherein the first set of computing instructions are operable to cause the control computing system to:receive, from the resource management computing system, at the control computing system, the initial resource allocation; andoverride the initial resource allocation by:generating the computer-generated resource allocation accounting for scaling, based on the scaling factor, the initial resource allocation; and sending, from the control computing system, to the resource management computing system, the computer-generated resource allocation.

3. The computer-program product of claim 1 ,wherein the first set of computing instructions are operable to cause the control computing system to override the initial resource allocation by outputting at least one indication to the resource management computing system to display in a graphical user interface of the resource management computing system;wherein the at least one indication comprises one or more of:the scaling factor;the computer-generated resource allocation; andretrieval link for retrieving the scaling factor or the computer-generated resource allocation.

4. The computer-program product of claim 1,wherein the first set of computing instructions are operable to cause the control computing system to:receive an update to a threshold for the scaling factor; andgenerate the scaling factor responsive to the update to the threshold for the scaling factor;wherein when the threshold is unmet the control computing system refrains from changing the initial resource allocation; andwherein when the threshold is met the control computing system generates the scaling factor.

5. The computer-program product of claim 1 , wherein the first set of computing instructions are operable to cause the control computing system to:generate the scaling factor based on a function of an aggressiveness factor; receive an updated aggressiveness factor; andgenerate an updated scaling factor responsive to the updated aggressiveness factor.

6. The computer-program product of claim 1 ,wherein the resource management computing system is remote from the control computing system;wherein the first set of computing instructions are operable to cause the control computing system to receive a control instruction according to a user of the resource management computing system; andwherein a value or an assessment for a first one of the first set of resource parameters are controlled by the control instruction.

7. The computer-program product of claim 1 ,wherein the first set of resource parameters comprises multiple parameters; wherein the second set of resource parameters comprises multiple parameters; wherein the initial resource allocation is not generated based on any of the first set of resource parameters;wherein the scaling factor is not generated based on any of the second set of resource parameters; andwherein the computer-program product further comprises the second set of computing instructions.

8. The computer-program product of claim 1,wherein the first set of computing instructions are operable to cause the control computing system to:receive feedback based on actual resource use for the first time block; update, according to a machine learning computer model and the feedback, the first set of resource parameters to an updated set of resource parameters; andgenerate an updated scaling factor according to the updated set of resource parameters for overriding resource allocations for a second time block of the multiple time blocks.

9. The computer-program product of claim 1,wherein the resource management computing system generates a set of computergenerated resource allocations at a first frequency, wherein the set of computergenerated resource allocations comprises the initial resource allocation and does not comprise the computer-generated resource allocation;wherein the control computing system generates a set of scaling factors at a second frequency, wherein the set of scaling factors comprises the scaling factor; and wherein the second frequency is at a higher frequency than the first frequency.

10. The computer-program product of claim 1,wherein the resources allocated for the first time block are at a first location managed by the resource management computing system; andwherein the first set of computing instructions are operable to cause the control computing system to determine the first set of resource parameters by receiving an indication of public databases comprising resource conditions for one or more other locations not managed by the resource management computing system.

11. The computer-program product of claim 1 , wherein the second set of resource parameters comprises parameters based on:available inventory of set of resources controlled by the resource management computing system; anddemand for the set of resources controlled by the resource management computing system.

12. The computer-program product of claim 1, wherein the first set of resource parameters comprises parameters based on resources uncontrolled by the resource management computing system.

13. The computer-program product of claim 1, wherein the first set of computing instructions are operable to cause the control computing system to:generate multiple different scaling factors specific to different resource types managed by the resource management computing system, wherein the scaling factor is one of the multiple different scaling factors and is associated with a first resource type of the different resource types; andoverride the initial resource allocation based on determining the initial resource allocation is specific to the first resource type.

14. The computer-program product of claim 1,wherein the initial resource allocation allocates a first resource amount more than an available resource amount controlled by the resource management computing system; andwherein the computer-generated resource allocation allocates a second resource amount that is less than the first resource amount and still more than the available resource amount controlled by the resource management computing system.

15. The computer-program product of claim 1, wherein the initial resource allocation and the computer-generated resource allocation allocates an amount of items for temporary use in a same period of time during the first time block.

16. The computer-program product of claim 1, wherein the initial resource allocation and the computer-generated resource allocation allocates a distribution of rewards, values, or constraints during the first time block.

17. The computer-program product of claim 1,wherein the resource management computing system generates the initial resource allocation as an amount of rooms, seats, or vehicles for overbooking for a given time block of the multiple time blocks; andwherein the scaling factor reduces the amount of rooms, seats, or vehicles for overbooking for the given time block.

18. The computer-program product of claim 1,wherein the scaling factor is a multiplier for the initial resource allocation; and wherein the first set of computing instructions are operable to cause the control computing system to override the initial resource allocation to a computergenerated resource by multiplying the initial resource allocation by the scaling factor.

19. The computer-program product of claim 1, wherein when the scaling factor is zero, the first set of computing instructions are operable to cause the control computing system to override the initial resource allocation to a computer-generated resource by preventing further resource allocation for the first time block that is associated with the initial resource allocation and the computer-generated resource allocation.

20. The computer-program product of claim 1,wherein each of a set of the multiple time blocks of the schedule are displayed in a graphical user interface;wherein each member of the set of the multiple time blocks displayed in the graphical user interface represents an equivalent periods of time as any other member of the set and occurs in a non-overlapping time period of any other member of the set; andwherein the initial resource allocation allocates resources related to an amount of rooms, seats, food, or vehicles for each time block of the set of the multiple time blocks.

21. A computer-implemented method comprising:generating, by a control computing system, based on a first set of resource parameters, a scaling factor for an initial resource allocation,wherein the initial resource allocation allocates resources for a first time block of multiple time blocks;wherein the multiple time blocks are according to a schedule controlled by a resource management computing system;wherein the initial resource allocation is generated by the resource management computing system executing a second set of computing instructions separately from a first set of computing instructions executed by the control computing system;wherein the second set of computing instructions are operable to cause the resource management computing system to generate the initial resource allocation based on a second set of resource parameters; wherein the first set of resource parameters and the second set of resource parameters are disjoint sets; andoverriding, by the control computing system, based on the scaling factor, the initial resource allocation to a computer-generated resource allocation for the first time block.

22. The computer-implemented method of claim 21 ,wherein the method further comprises receiving, from the resource management computing system, at the control computing system, the initial resource allocation; andwherein the overriding the initial resource allocation comprises:generating the computer-generated resource allocation accounting for scaling, based on the scaling factor, the initial resource allocation; and sending, from the control computing system, to the resource management computing system, the computer-generated resource allocation.

23. The computer-implemented method of claim 21 ,wherein the overriding the initial resource allocation comprises outputting at least one indication to the resource management computing system to display in a graphical user interface of the resource management computing system; wherein the at least one indication comprises one or more of:the scaling factor;the computer-generated resource allocation; andretrieval link for retrieving the scaling factor or the computer-generated resource allocation.

24. The computer-implemented method of claim 21 ,wherein the computer-implemented method comprises receiving an update to a threshold for the scaling factor;wherein the generating the scaling factor comprises generating the scaling factor responsive to the update to the threshold for the scaling factor;wherein when the threshold is unmet the control computing system refrains from changing the initial resource allocation; andwherein when the threshold is met the control computing system generates the scaling factor.

25. The computer-implemented method of claim 21 ,wherein the generating the scaling factor comprises generating an initial scaling factor based on a function of an aggressiveness factor;wherein the computer-implemented method comprises:receiving an updated aggressiveness factor; andgenerating an updated scaling factor responsive to the updated aggressiveness factor.

26. The computer-implemented method of claim 21,wherein the resource management computing system is remote from the control computing system;wherein the computer-implemented method comprises receiving a control instruction according to a user of the resource management computing system; and wherein a value or an assessment for a first one of the first set of resource parameters are controlled by the control instruction.

27. The computer-implemented method of claim 21 ,wherein the computer-generated resource allocation allocates resources for a first period of time; andwherein the computer-implemented method comprises:receiving feedback based on actual resource use for the first period of time; updating, according to a machine learning computer model and the feedback, the first set of resource parameters to an updated set of resource parameters; andgenerating an updated scaling factor according to the updated set of resource parameters for overriding resource allocations for a second period of time.

28. The computer-implemented method of claim 21 ,wherein the resource management computing system generates a set of computergenerated resource allocations at a first frequency, wherein the set of computergenerated resource allocations comprises the initial resource allocation and does not comprise the computer-generated resource allocation;wherein the control computing system generates a set of scaling factors at a second frequency, wherein the set of scaling factors comprises the scaling factor; and wherein the second frequency is at a higher frequency than the first frequency.

29. The computer-implemented method of claim 21 ,wherein the resource management computing system manages resources at a first location; andwherein the computer-implemented method comprises determining the first set of resource parameters by receiving an indication of public databases comprising resource conditions for one or more other locations not managed by the resource management computing system.

30. A computing control system comprising processor and memory, the memory containing a first set of computing instructions executable by the processor wherein the computing control system is configured to:generate, based on a first set of resource parameters, a scaling factor for an initial resource allocation,wherein the initial resource allocation allocates resources for a first time block of multiple time blocks;wherein the multiple time blocks are according to a schedule controlled by a resource management computing system;wherein the initial resource allocation is generated by the resource management computing system executing a second set of computing instructions separately from a first set of computing instructions;wherein the second set of computing instructions are operable to cause the resource management computing system to generate the initial resource allocation based on a second set of resource parameters;wherein the first set of resource parameters and the second set of resource parameters are disjoint sets; andoverride, based on the scaling factor, the initial resource allocation to a computergenerated resource allocation for the first time block.

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