53 results about "Workload management" patented technology

In some aspects, systems and methods are described herein for determining rightsizing adjustments to a cluster of cloud servers using a multi-dimensional operating ratio. The system collects usage data from a cloud server, comprising multiple dimensions of cloud computation usage. The system then processes the usage data using a cleansing process to generate processed usage data, wherein the cleansing process comprises outlier removal and seasonality adjustment. The system compares the processed usage data against a benchmark to generate a workload metric. The workload metric corresponds to values in the multiple dimensions of the cloud computation usage, indicating a distance from the expected usage data. Based on the workload metric and using a predictive model, generating expected capacity needs. Based on the expected capacity needs, determining a set of rightsizing changes, wherein the set of rightsizing changes comprises changes to capacities of the cloud server.

Disclosed are systems and methods that offload the work traditionally performed by a thick software client operating on each container instance of a cloud provider network with a thin and generalized agent that can be instructed in a piece-wise manner to perform operations as instructed by a workload manager executing on a control plane that is separate from the container instance. The workload manager, executing on the control plane of a cloud provider network, may precompute a set of operations and order of execution of those operations in the control plane and present those operations in a controlled manner to the agent that executes each operation as instructed. The agent executing on the data plane, rather than polling an orchestrator for work and then establishing the runtime environment based on a received Application Specification, awaits an operation or task that is pushed to the agent from the control plane.

The invention provides an IT system construction workload evaluation method and system, a medium and equipment, and belongs to the technical field of computer software, and the method comprises the steps: receiving IT system construction project information, and carrying out the analysis and decomposition to form function points; calculating an estimated workload by adopting a workload quantification model, and storing the estimated workload; according to the actual workload consumption condition, the workload data are adjusted and verified, and an actual workload summary sheet is generated; counting experts of corresponding technical field knowledge to form an expert database; and carrying out random extraction to form a review group, reviewing the adjusted workload, generating a review opinion, and confirming the review opinion to form final workload data. According to the method, the workload is split according to the function points for pre-estimation, filling and summarizing are performed according to the actual workload generated in the actual development process, then an expert performs review, and unified confirmation / complaint is performed; the workload management work is visualized, transparent, standardized and normalized, and the project settlement efficiency is improved.

Techniques are provided for discerning energy consumption and carbon footprint data associated with hardware-as-a-service resources, and managing workloads based on carbon cost metrics. A management interface collects energy consumption information associated with a plurality of servers processing workloads in a cloud computing environment. The management interface monitors energy consumption in the plurality of servers. An energy consumption manager calculates carbon cost metrics for the plurality of servers based on the energy consumption information; identifies a subset of the servers as being underutilized based on the carbon cost metrics; and calculates an overall carbon cost of the plurality of servers. A workload manager generates container platform operation labels for the plurality of servers when the overall carbon cost is below a threshold value, such that a workload scheduled on the container platform migrates to one of the servers based on the container platform operation labels.

The present technology enhances workload management of data storage systems by using an internal copy function and / or a zone-append technology. The internal copy function is used, e.g., in merge operations to move data between locations on a disk without using off-disk resources (e.g., processing or memory of a CPU). Zone-append technology uses nameless writes (e.g., write instruction without an assigned destination address on the disk) to combine IO units from different threads to be written to a common zone of a disk (e.g., a shingled magnetic recording (SMR) disk). Sequential addresses are assigned to IO units from different threads based on their order in the write queue, reducing the latency and seek time typically associated with random writes. The zone-append technology, e.g., uses sequential write operations within specified zones, allowing the disk to determine the actual write location and to report post-write logical block addresses (LBAs).

A server can include an interface configured to receive sensor data relating to a cyber-physical system (CPS), as well as a processor. The processor can be configured to determine a performance parameter of the CPS from the sensor data; select a resource allocation strategy based on the performance parameter; execute an algorithmic process on at least a portion of the sensor data according to the resource allocation strategy; and control a transmitter to send data, representing an output of the executed artificial neural network, to the robot.

A workload management system includes a workload management tool configured to generate a workload context associated with a workload generated based on interactions of a user with workload initiation controls presented within a user interface (UI) of a client application. The workload context includes instructions for transmitting the workload context from a main browser session to a first background browser session; executing the workload within the first background session; and for configuring a first event handler within the main session to wait for a first event generated within the first background session in association with execution of the workload and, in response to receipt of the first event, transmit the client application an instruction to present workload status information in the user interface.

A computer-implemented method for managing one or more operations of a workload is disclosed. The method includes selecting a resource type for workload management on a platform. One or more operations of the selected resource to be managed are identified. A coordination time for execution of each identified operation is determined. A coordination time period between two consecutive coordinations is determined for each identified operation. A minimum number of processes for workload management of a given set of operations on the resource is computed, and the determined minimum number of processes is deployed to manage the workload.

Rendering workload management techniques in an information processing system are disclosed. For example, a method includes obtaining one or more rendering workloads from a client, and automatically managing execution of the one or more rendering workloads in accordance with at least a subset of resources of a plurality of computing platforms, wherein the plurality of computing platforms comprises at least one client computing platform and at least one cloud computing platform. The step of automatic execution management includes computing estimations that consider a resource utilization associated with at least one adjustment to a resolution of one or more identified regions of at least one rendering workload to assist a client in selecting from one or more of a plurality of candidate execution plans.

Rendering workload management techniques in an information processing system are disclosed. For example, a method includes obtaining one or more rendering workloads from a client, and automatically managing execution of the one or more rendering workloads in accordance with at least a subset of resources of a plurality of computing platforms, wherein the plurality of computing platforms comprises at least one client computing platform and at least one cloud computing platform. The step of automatic execution management includes computing estimations to assist a client in selecting from one or more of a plurality of candidate execution plans. The candidate execution plans are generated based on execution criteria for the one or more rendering workloads. At least one value of the execution criteria is generated by at least one language model based on an input from the client.

A computer implemented method manages computing workloads. A processor set monitors thermal data from sensors associated with a set of components in a computer system. The processor set manages computing workloads for the set of components using the thermal data and a policy defining component usage.

A server may include an interface that is configured to receive sensor data related to a cyber-physical system (CPS); and a processor. The processor may be configured to determine a performance parameter of the CPS from the sensor data; select a resource allocation strategy based on the performance parameter; execute an algorithmic process on at least a portion of the sensor data according to the resource allocation strategy; and control a transmitter to send data representing an output of the executed artificial neural network to the robot.

Various embodiments generally may relate to the field of wireless communications. For example, some embodiments may relate to enabling augmented computing as a service or network capability for sixth-generation (6G) networks. For example, some embodiments may be directed to the operation of a compute control client (Comp CC) at the UE side, and a compute control function (Comp CF) and compute service function (Comp SF) at the network side, which are referred to herein as “compute plane” functions to handle computing related control and user traffic.

Rendering workload management techniques in an information processing system are disclosed. For example, a method includes obtaining one or more rendering workloads from a client, and automatically managing execution of the one or more rendering workloads in accordance with at least a subset of resources of a plurality of computing platforms, wherein the plurality of computing platforms comprises at least one client computing platform and at least one cloud computing platform. The step of automatic execution management includes computing estimations that consider a resource utilization associated with embedding at least one digital watermark in at least a portion of at least one rendering workload to assist a client in selecting from one or more of a plurality of candidate execution plans.

A non-transitory, machine-readable storage medium containing instructions that, when executed, cause a system (400) to: Generating a training dataset (122) based on features of example workloads, wherein the training dataset (122) includes labels associated with the features of the example workloads, the labels being based on load indicators generated in a computing environment and relating to load conditions of the computing environment resulting from the execution of the example workloads; Grouping (502) selected workloads (106) into a plurality of workload clusters (118) based on features of the selected workloads (106), wherein a workload cluster (118) of the plurality of workload clusters (118) includes workloads (106) that are similar to each other according to a similarity criterion; Computation (506) of parameters (124) representing the contributions of each workload cluster (118) of the plurality of workload clusters (118) to a load condition in the computing environment, using a model (120) trained on the basis of the training data set (122), wherein the parameters (124) include a first parameter (124) representing a contribution of a first workload cluster (118) to the load condition and a second parameter (124) representing a contribution of a second workload cluster (118) to the load condition, wherein the first and second workload clusters (118) are part of the plurality of workload clusters (118); Selection of a workload cluster (118) from the multitude of workload clusters (118) based on different values ​​of the calculated parameters (124), wherein a value of a parameter (124) calculated for a selected workload cluster (118) indicates that the selected workload cluster (118) has a workload (106) that negatively affects the work performance of a workload (106) in another workload cluster (118); and Perform workload management (508) in the computer environment based on the calculated parameters (124), wherein workload management (508) includes: Determining a relative priority of the identified workload (106) over other workloads (106), and Restricting the resource utilization of the identified workload (106) in the selected workload cluster (118) in response to the determined relative priority.

A high-throughput data transformation and upserting system for distributed computing environments, comprising: a data ingestion module configured to receive structured, semi-structured, and unstructured data from one or more distributed data sources, including databases, cloud platforms, enterprise systems, streaming infrastructures, and external computing devices; a transformation module configured to convert the received data into predefined processing formats through data mapping, normalization, filtering, aggregation, and schema matching operations performed across multiple distributed computing nodes; and an upsertion module configured to insert new records and update existing records in one or more distributed databases while maintaining synchronization and consistency of the records across distributed computing resources.a synchronization module configured to coordinate the management of distributed transactions, replication activities, and consistency operations between interconnected databases and compute nodes; a workload management module configured to distribute processing workloads across distributed compute resources to improve throughput efficiency, scalability, and resource utilization for data-intensive processing operations; and a monitoring module configured to detect duplicate records, synchronization conflicts, operational failures, processing delays, and data inconsistencies related to distributed transformation and upsertion activities.

Managing cloud workloads is provided. It is determined whether a set of software vulnerabilities exists in an in-use software package corresponding to a cloud workload based on a search of a set of software vulnerability databases. In response to determining that a set of software vulnerabilities exists in an in-use software package corresponding to the cloud workload based on the search, a set of target software update times to apply a set of software updates corresponding to the set of software vulnerabilities in the in-use software package is determined based on customer preference settings and compliance objectives to at least one of minimize disruption and maximize security of the cloud workload. The set of software updates is applied to the in-use software package of the cloud workload to mitigate the set of software vulnerabilities in the in-use software package in accordance with the set of target software update times.

Provided herein are various enhancements for deployment of workloads or jobs on a computing cluster. In one example implementation, a method includes identifying a container pod deployment request having a pod specification, and responsive to the container pod reaching a pending state for insufficient resources to support deployment of the container pod on a computing cluster, identifying resources indicated in the pod specification and determining one or more physical computing components to attach to a target node. The method also includes attaching the one or more physical computing components to the target node, where a change in resources available in the target node is detected by a workload manager that deploys the container pod.

A computer-implemented method, according to one embodiment, includes generating a temporal mapping of positions of a first satellite with respect to predetermined portions of land of Earth and predetermined bodies of water of the Earth. The method further includes determining, based on the temporal mapping, a first job for the first satellite to offload to a first target device during a first period of time that a footprint coverage region of the first satellite is within a predetermined proximity to a first of the bodies of water. In response to a determination that the footprint coverage region of the first satellite is within the predetermined proximity to the first body of water, the first satellite is caused to offload the first job to the first target device.

Methods, systems, and computer storage media for providing workload management using a workload management engine in an artificial intelligence (AI) system. In particular, workload management includes adaptive policies that adjust neural network models employed by processing units (e.g., NPUs / GPUs / TPUs) based on dynamic properties of workloads, workload management factors, and workload management logic. The workload management engine provides workload management logic to support policy decisions that are optimized for the processor. In operation, a plurality of states of workload management factors are identified. A task associated with a workload processing unit is identified. A neural network model is selected from a plurality of neural network models based on the task and the plurality of states of the workload processing unit. The plurality of neural network models includes a full neural network model and a simplified neural network model. The task is caused to be performed using the identified neural network model.

Provided herein are various enhancements for deployment of workloads or jobs on a computing cluster. In one example implementation, a method includes identifying a container pod deployment request having a pod specification, and responsive to the container pod reaching a pending state for insufficient resources to support deployment of the container pod on a computing cluster, identifying resources indicated in the pod specification and determining one or more physical computing components to attach to a target node. The method also includes attaching the one or more physical computing components to the target node, where a change in resources available in the target node is detected by a workload manager that deploys the container pod.

This invention provides a method and system for quantitatively assessing the overall work status of an organization, belonging to the field of organizational workload management technology. The method uses the quantitative assessment results of individual employee workloads as its underlying support. On the one hand, it calculates and statistically analyzes the overall workload compliance rate of the organization; on the other hand, it deeply analyzes the level distribution characteristics of each dimension, including workload, work status, work ability / efficiency, work attitude, and overtime, ultimately constructing a panoramic distribution view of the overall work status of the organization. This method effectively solves the problems of incomplete assessment scope, insufficient measurement accuracy, and lack of guidance in assessment results existing in traditional assessment models, providing standardized and quantifiable analytical basis for accurate judgment of the overall work status of the organization and scientific formulation of management decisions.

An adaptive modular processor system is disclosed comprising a plurality of processing cores configured to dynamically switch between operational modes including CPU mode, GPU mode, and AI processing mode. A workload management controller monitors computational workload requirements in real time and dynamically reconfigures the processing cores based on detected workload demands. The system further comprises a shared memory accessible by the processing cores and a power optimization unit configured to reduce energy consumption by selectively deactivating inactive cores. A decision unit may determine whether tasks are processed locally or offloaded to a remote computing resource based on system workload and network conditions. The processor dynamically reallocates computational resources during runtime to optimize performance and energy efficiency.

In some examples, a system monitors health metrics associated with a plurality of edge links connecting a collection of switches to electronic devices. The system predicts, based on a pattern of the health metrics, an unhealthy condition of a first edge link of the plurality of edge links. Based on the predicting, a workload manager triggers a maintenance mode for an electronic device connected to the first edge link to address the predicted unhealthy condition, wherein while the electronic device is in the maintenance mode the workload manager avoids scheduling any further workloads on the electronic device.

Systems and methods are disclosed for managing workload among server clusters is disclosed. According to certain embodiments, the system may include a memory storing instructions and a processor. The processor may be configured to execute the instructions to determine historical behaviors of the server clusters in processing a workload. The processor may also be configured to execute the instructions to construct cost models for the server clusters based at least in part on the historical behaviors. The cost model is configured to predict a processor utilization demand of a workload. The processor may further be configured to execute the instructions to receive a workload and determine efficiencies of processing the workload by the server clusters based at least in part on at least one of the cost models or an execution plan of the workload.

A workload management system monitors a tenant-specific workload for a tenant on a first set of cloud service instances hosted by a first cloud services infrastructure at a first cloud service location. The workload management system determines that a second cloud service location can host a predicted tenant-specific workload. The system obtains weather data for the cloud service locations. Based at least in part on the predicted tenant-specific workload and the weather data for the cloud service locations, the workload management system determines non-carbon costs of hosting and / or migrating the predicted tenant-specific workload while maintaining baseline performance criteria and a carbon cost of hosting and / or migrating. Based at least in part on the calculated costs, the system stores an indication that carbon emissions will be reduced by hosting the predicted tenant-specific workload at the second cloud service location and routes an ongoing workload to the second cloud service location.

Rendering workload management techniques in an information processing system are disclosed. For example, a method includes obtaining one or more rendering workloads from a client, and automatically managing execution of the one or more rendering workloads in accordance with at least a subset of resources of a plurality of computing platforms, wherein the plurality of computing platforms comprises at least one client computing platform and at least one cloud computing platform. The step of automatic execution management includes computing estimations that consider a resource utilization associated with at least one adjustment to a resolution of at least a portion of at least one rendering workload to assist a client in selecting from one or more of a plurality of candidate execution plans.

A memory controller, a memory system and a method of operating a memory controller controlling a memory device are described. The memory controller may include a workload manager in communication with the memory device in which data is written and is read, the workload manager configured to acquire an amount of write data written to the memory device during a preset reference time, calculate a workload parameter indicating a ratio of the amount of write data to a reference write amount, and store the workload parameter for the preset reference time, and a performance manager configured to control, based on the workload parameter, a certain background operation performed by the memory device during a period corresponding to the workload parameter.