32 results about "Cloud computing systems" patented technology

The embodiment of the present application provides a method and device for installing components, which are used for a cloud computing system, and the method comprises the following steps: receiving parameter information, wherein the parameter information at least comprises component information of a target component to be installed; determining a target node for installing the target component in each node of the cloud computing system according to the parameter information, and establishing a connection with the target node; searching for an installation package corresponding to the target component in a preset installation source according to current version information of the cloud computing system and the component information of the target component; and transmitting the installation package to the target node, so that the target node realizes automatic installation of the target component by running the installation package. Through the present application, the target component can be automatically installed under the cloud computing system, so that the labor cost can be reduced and the efficiency of installing components can be improved.

Example techniques relate to cloud queue synchronization. An example implementation may involve a playback device receiving, from a cloud computing system, data representing a play message comprising: (i) a play pointer representing a particular media item; (ii) an indication of one or more media items, the one or more media items including the particular media item; and (iii) a universal resource identifier (URI) representing a location of the particular media item at one or more servers of a streaming audio service. The playback device queues the one or more media items in a local queue stored in data storage of the playback device, streams the particular media item, and plays back the particular media item. After beginning playback of the particular media item, the playback device sends a playback started message comprising the play pointer, the play pointer representing the particular media item as the currently playing media item.

The embodiment of the application provides a service optimization method, system, device, computer program product and storage medium. A new decision basis, i.e., stability description information in the unit of cloud resources, is introduced in the service optimization process. The stability description information in the embodiment of the application is in the unit of cloud resources, so that the fine granularity of the stability description information can reach the cloud resource level. Accordingly, in the process of service optimization, the stability of the cloud computing system itself can be improved, and different cloud resources can more accurately avoid stability problems, so that the influence of the stability problems in the cloud computing system on the cloud resources is reduced, and the service stability perceived by the user side can be effectively improved.

Embodiments of the present disclosure relate to systems, devices, articles and methods for determining ball location based on player location data. The system includes a local system, a cloud computing system and a remote system. The system collects location data of players during a tracked event, refines and pre-processes the data which is used to train a machine learning model which determines the region of the ball. Embodiments further include methods to determine ball possession. The ball region and possession data can be used to determine the ball location throughout an event and analyze ball possession.

This invention provides a method, system, and computer storage medium for providing a network management engine in a cloud computing system. [Solution] A method for managing a network in a network management system via a network performance management engine, wherein a client communicates packets associated with an application gateway and a network packet management extension engine 502A, receives a response packet associated with the packet based on the communication of the packet 504A, extracts packet latency data associated with the response packet 506A, and transmits packet latency data 508A.

Embodiments of the present disclosure relate to systems, devices, articles and methods for calculating sports performance metrics using positional data. The system includes a local and a cloud computing system. A plurality of player sensor tags and wireless data transceivers measure the location of players during a tracked event. The location data are processed either in real-time by a local edge computing device or post processed by a cloud computing device using mathematical and sports-physics models. The processed data are used to determine acceleration and g-force which in turn determine performance metrics that can be used to provide tactical insight.

Methods, systems, and computer storage media for providing a network management engine in a cloud computing system are described. Network management engine is an end-to-end system that oversees, monitors, and optimizes the full lifecycle of network traffic, from the initial client request to the final data response. By capturing, analyzing, and managing network packets, it ensures seamless data flow and tracks key performance metrics to identify and resolve latency or congestion issues. Network management engine supports monitoring, and fault detection for efficient data flow, capturing metrics, and analyzing performance. Network management engine includes network packet management extension engine and network performance management engine. Network packet management extension engine is a specialized engine designed for calculating and analyzing packet latency at various stages of a network path. Network performance management engine is a specialized engine that identifies and manages deviations from expected network behavior, particularly in areas where latency may occur.

Methods, systems, and computer storage media for providing a network management engine in a cloud computing system are described. Network management engine is an end-to-end system that oversees, monitors, and optimizes the full lifecycle of network traffic, from the initial client request to the final data response. By capturing, analyzing, and managing network packets, it ensures seamless data flow and tracks key performance metrics to identify and resolve latency or congestion issues. Network management engine supports monitoring, and fault detection for efficient data flow, capturing metrics, and analyzing performance. Network management engine includes network packet management extension engine and network performance management engine. Network packet management extension engine is a specialized engine designed for calculating and analyzing packet latency at various stages of a network path. Network performance management engine is a specialized engine that identifies and manages deviations from expected network behavior, particularly in areas where latency may occur.

One implementation of the present disclosure is a system including a cloud computing system. The cloud computing system is configured to receive a request from an application indicating a piece of field equipment and a period of time. The cloud computing system is also configured to retrieve appended field data from a memory storage, wherein the appended field data comprises field data, a second checksum, and a second timestamp. The cloud computing system is also configured to identify the second timestamp and the second checksum of the appended field data. The cloud computing system is also configured to receive a first timestamp and a first checksum from the edge device. The cloud computing system is also configured to provide the field data to the application, responsive to a determination that the first checksum matches the second checksum and the first timestamp matches the second timestamp.

The embodiment of the invention provides a service optimization method, system and device, a computer program product and a storage medium. A new decision basis is introduced in the service optimization process, namely, the tolerance of clients to various stability problems existing in the cloud computing system. According to the embodiment of the invention, cascade connection between various stability problems existing in the cloud computing system and the tolerance determined in the embodiment can be realized, so that in the process of service optimization for the target customer, the stability of the cloud computing system can be improved on the basis of improving the stability of the cloud computing system, and the service optimization efficiency is improved. The target customer is helped to avoid the concerned stability problem as much as possible in the cloud computing system, so that the influence of the stability problem on the target customer is avoided as much as possible, and the service stability felt by the target customer can be further improved.

In some implementations, a cloud computing system may deploy a virtual server to execute an application associated with an application licensor system. The cloud computing system may identify a virtual network interface that corresponds to the virtual server. The cloud computing system may associate a key received from the application licensor system to the virtual network interface to allow the application to be executed based on the virtual network interface. The cloud computing system may associate the virtual server and the virtual network interface. The cloud computing system may execute the application on the virtual server based on the key that is associated with the virtual network interface and based on associating the virtual server and the virtual network interface.

A method of conducting a virtual clinic (VC) session between a patient and a clinician to facilitate medical services using a VC cloud computing system is provided. The method comprises receiving video data from a patient controller (PC) device of the patient, the video data including patient gait data of the patient acquired by the PC device during the VC session, identifying, from the received video data, at least one fixed edge in an environment surrounding the patient, determining, based on the at least one identified fixed edge, an amount of camera tilt of the PC device relative to the patient, normalizing anatomical landmark data of the patient gait data by applying a transformation to the anatomical landmark data to compensate for the determined amount of camera tilt, and providing the normalized anatomical landmark data to one or more trained computation models to classify one or more patient gait characteristics.

Systems and methods for correlating performance on bare metal systems with virtualized instances such as those commonly used in cloud computing systems are disclosed. Performance data may be collected from different bare metal and cloud instances. The performance data may be used to predict the performance of an application on another system, even if the particular performance counter of interest is unavailable on the system. Using measured and estimated performance counters (e.g., instructions counters), multiple measured and unmeasured but predicted instances can be compared and sorted to assist the user in making an informed decision when selecting where to run their instance and what configuration to use.

Embodiments of the present disclosure relate to systems, devices, articles and methods for determining ball location based on player location data. The system includes a local system, a cloud computing system and a remote system. The system collects location data of players during a tracked event, refines and pre-processes the data which is used to train a machine learning model which determines the region of the ball. Embodiments further include methods to determine ball possession. The ball region and possession data can be used to determine the ball location throughout an event and analyze ball possession.

The disclosure includes a risk inheritance system for actively preventing unauthorized and compromising activity within a cloud computing system by causing user risk scores to be inherited across downstream cloud entities within the cloud computing system. The risk inheritance system ensures that users with risky user risk scores are unable to circumvent the security measures of the cloud computing system through propagation events. For instance, the risk inheritance system assigns user risk scores to be inherited from a cloud entity of a user to another cloud entity, including other users and service principals, based on detecting the user initiating a propagation event. This way, the risk inheritance system improves the efficiency of the cloud computing system by ensuring that cloud entities are assigned accurate user risk scores.

A method includes a manager gateway receiving a baseboard management controller configuration over a WebSocket connection that supports communication over a wide area network (WAN) between the controller in a device-side private network and a system management application that runs in a cloud-side private network within a public cloud computing system, wherein the manager gateway connects the cloud-side private network to the WAN. The manager gateway creates, in response to receiving the configuration for the controller, a virtual network interface controller (vNIC) having a cloud-side local network address and creating a record in a cloud-side DNS Service to map the controller to the vNIC. When the manager gateway receives a request message over the cloud-side private network from the application at the virtual network interface controller, the manager gateway relays the request message to the controller over the WebSocket connection that the cloud-side DNS Service maps to the vNIC.

Embodiments of the present disclosure relate to systems, devices, articles and methods for calculating sports performance metrics using positional data. The system includes a local and a cloud computing system. A plurality of player sensor tags and wireless data transceivers measure the location of players during a tracked event. The location data are processed either in real-time by a local edge computing device or post processed by a cloud computing device using mathematical and sports-physics models. The processed data are used to determine acceleration and g-force which in turn determine performance metrics that can be used to provide tactical insight.

This XR cloud computing system for providing a real-time XR content service by utilizing a microservice-based cloud native in a multi-cloud environment comprises: a computing virtualization unit for supporting the multi-cloud environment, virtualizing computing resources, and managing microservices; a content loading unit for loading large-capacity XR content; a content rendering unit for rendering the XR content in the cloud environment; a content encoding unit for performing an optimization operation in order to transmit the rendered XR content with ultra-low latency; a content interaction unit for transmitting the XR content and allowing interaction with an XR device of a user; a cache unit for accelerating processing speeds of the modules; and an XR device SDK unit for decoding the received XR content and rendering the decoded XR content on a device of the user.

Methods, systems, and computer-readable storage media for receiving a third request to be processed wherein the third request is received after a first request and a second request, determining a first quality of service (QoS) value associated with the third request, determining an insert position for the third request at least partially based on the first quality of service (Qos) value where the insert position is between the first and second positions of the at least one request queue, inserting the third request into the at least one request queue at the insert position, and distributing the first, second and third requests queued in the at least one request queue for processing by one or more processors within a cloud computing environment.

An artificial intelligence chat bot can be used to configure a cloud computing system. For example, a system can determine, using a first machine learning model, a target functionality for a distributed computing system based on an input provided by a user in a natural language format expressing the target functionality. The system can determine, using a second machine learning model, a recommended adjustment to an existing confirmation of the distributed computing system based on an incompatibility between the target functionality and a configuration setting of a software service executed by the distributed computing system. The system can output a response to the natural language input comprising the recommendation to the user in the natural language format. The system can automatically deploy or reconfigure the distributed computing system on the recommendation.

This disclosure describes a preventative breach detection system that detects and addresses potential resource breaches in candidate code-based infrastructure templates before deployment in a production environment within a cloud computing system. For example, the preventative breach detection system provides a preemptive security framework that utilizes hybrid pattern-based data structures, security pattern analysis, and resource vulnerability patterns to determine the security posture of a candidate code-based infrastructure based on its potential impact on the surrounding infrastructure in the production environment. By doing so, the preventative breach detection system quickly detects potential new resource breaches or changes in the security threat levels that may occur before the candidate code-based infrastructure is introduced into the production environment of the cloud computing system.

Embodiments of the present application provide a method for maintaining an edge cloud computing node, an operation and maintenance platform and a cloud computing system. The method comprises: in response to a failure maintenance event of the edge cloud computing node, modifying a state of a faulty device to a non-production state through a faulty device offline workflow, determining a target backup machine in a backup machine pool as a new device to be deployed through a new device deployment workflow, installing the target backup machine, adding the target backup machine to a cloud computing node cluster where the faulty device is located, marking a state of the target backup machine as a non-production state, after the faulty device is taken offline and the target backup machine is taken online, modifying the state of the target backup machine to a production state to end the new device deployment workflow; after the faulty device is maintained, testing the maintained faulty device through the faulty device offline workflow, and adding the faulty device that passes the test to the backup machine pool to end the faulty device offline workflow, thereby realizing an automatic maintenance scheme for the edge cloud computing node.

According to one aspect of the concepts and technologies disclosed herein, a cloud computing system can include a load adaptation architecture framework that performs operations for orchestrating and managing one or more services that may operate within at least one of layers 4 through 7 of the Open Systems Interconnection (“OSP”) communication model. The cloud computing system also can include a virtual resource layer. The virtual resource layer can include a virtual network function that provides, at least in part, a service. The cloud computing system also can include a hardware resource layer. The hardware resource layer can include a hardware resource that is controlled by a virtualization layer. The virtualization layer can cause the virtual network function to be instantiated on the hardware resource so that the virtual network function can be used to support the service.

An device edge may receive a machine learning model, and may receive data from an edge device associated with a RAN. The device edge may process the data, with the machine learning model, to generate edge analytic data associated with the edge device, and may process the edge analytic data, with the machine learning model, to detect a problem or to predict an issue associated with the edge device or a RAN device associated with the edge device. The device edge may provide particular edge analytic data associated with the problem or the issue to a cloud-computing system, and may perform one or more actions based on the problem or the issue associated with the edge device or the RAN device.

Methods, systems, and computer storage media are described that provide a network management engine in a cloud computing system. The network management engine is an end-to-end system responsible for overseeing, monitoring, and optimizing the entire lifecycle of network traffic from initial client request to final data response. By capturing, analyzing, and managing network packets, the network management engine can ensure seamless data flow and track key performance metrics to identify and resolve latency or congestion issues. The network management engine supports monitoring and fault detection for efficient data flow, capturing metrics, and analyzing performance. The network management engine includes a network packet management extension engine and a network performance management engine. The network packet management extension engine is a specialized engine designed to compute and analyze packet latency at various stages of a network path. The network performance management engine is a specialized engine that identifies and manages deviations from expected network behavior, specifically in areas where latency can occur.

One implementation of the present disclosure is a system including a cloud computing system. The cloud computing system is configured to receive a request from an application indicating a piece of field equipment and a period of time. The cloud computing system is also configured to retrieve appended field data from a memory storage, wherein the appended field data comprises field data, a second checksum, and a second timestamp. The cloud computing system is also configured to identify the second timestamp and the second checksum of the appended field data. The cloud computing system is also configured to receive a first timestamp and a first checksum from the edge device. The cloud computing system is also configured to provide the field data to the application, responsive to a determination that the first checksum matches the second checksum and the first timestamp matches the second timestamp.

An elevator installation includes an edge computing entity communicatively coupled to a modernized elevator component and communicatively coupled to an unmodernized elevator component through an overlay. The edge computing entity may receive a first signal indicating modernized elevator data from the modernized elevator component according to a first communication protocol, receive a second signal indicating unmodernized elevator data from the unmodernized elevator component through the overlay based on the overlay receiving the second signal from the unmodernized elevator component according to a second communication protocol and transmitting the second signal according to the first communication protocol, and transmit information associated with at least one of the modernized elevator data or the unmodernized elevator data to a cloud computing system to enable the cloud computing system to perform a predictive maintenance operation to determine whether the elevator installation is associated with at least one service need condition.