5501 results about "Workload" patented technology

The invention relates to the technical field of crowdsourcing task allocation for multi-person cooperation of complex tasks, and discloses a crowdsourcing task allocation method for multi-person cooperation of complex tasks, which comprises the following steps: acquiring all persons in a talent pool and marking and numbering the persons in sequence, thereby facilitating subsequent management and information retrieval, acquiring a skill label of each person, and improving the efficiency of crowdsourcing task allocation. Multiple skills are considered, so that task allocation is more accurate, through skill label classification, the system can quickly match task requirements and personnel skills, obtain a historical task list to provide reference for task disassembly and estimation, and decompose tasks into small sub-tasks, and the system can accurately estimate task load and personnel allocation. According to the method, archives of the split tasks are recorded to provide data reference for follow-up, the average workload and the completion time of the historical tasks provide a basis for task allocation, uncertainty is reduced through data-driven estimation, and it is ensured that task load estimation is flexible and accurate through a similarity evaluation mechanism, so that the overall task execution quality and efficiency are improved.

Various aspects of methods, systems, and use cases for multi-entity (e.g., multi-tenant) edge computing deployments are disclosed. Among other examples, various configurations and features enable the management of resources (e.g., controlling and orchestrating hardware, acceleration, network, processing resource usage), security (e.g., secure execution and communication, isolation, conflicts), and service management (e.g., orchestration, connectivity, workload coordination), in edge computing deployments, such as by a plurality of edge nodes of an edge computing environment configured for executing workloads from among multiple tenants.

Disclosed are systems and methods for a hybrid cloud-edge computing platform using decentralized networks for solving resource-intensive computation tasks such as machine learning and video processing tasks. In some respects, the cloud-edge computing platform comprises a coordinator cluster and worker nodes partitioned into shards, which include cloud-based networks and edge networks, and which cater to a wide range of computation requirements. The coordinator cluster manages workloads by selecting the appropriate shard and worker nodes to execute tasks based on computed efficiency scores. Worker nodes may be incentivized with rewards such as cryptocurrency tokens for successful completion of tasks. The disclosed architecture also supports the pipelining of multiple tasks in a workload, where the output of one task is used as input for another task. In some respects, the computing architecture includes a blockchain-supported edge network, which enables verifying compute tasks and managing reward distribution for worker nodes using smart contracts.

A method, computer program product, and computing system for processing workload data associated with processing a plurality of requests for an artificial intelligence (AI) model on a processing unit. A maximum number of key-value (KV) cache blocks available for the workload data is determined by simulating the workload data using a simulation engine. A token utilization for the workload data is determined based upon, at least in part, the maximum number of KV cache blocks available for the workload data. Processing unit resources are allocated for the processing unit based upon, at least in part, the token utilization.

A computer system executes a database management system (DBMS). The DBMS manages a database comprised of DBMS resources. The DBMS receives a request to be executed. The request is a DBMS action to be executed using the DBMS resources. The request includes a predicate specifying a maximum cost for executing the request, and a deadline, specifying a deadline by which the request is to be completed in its execution. The DBMS determines a plurality of workloads under which the request is qualified to execute. Each workload of the plurality of workloads includes a respective set of requests that have common characteristics. Each workload of the plurality of workloads includes a respective cost criterion and a respective elapsed time criterion. The DBMS selects a selected workload from among the plurality of workloads. The selected workload has a selected cost criterion and a selected elapsed time criterion. The DBMS begins execution of the request using the selected workload.

A system for dynamically scaling microservices in cloud environments, including: a predictive scaling module that uses machine learning algorithms to analyze historical and real-time workload data and predict resource requirements; an auto-scaling decision engine configured to determine and execute dynamic scaling actions based on predictions and current system performance; a cloud orchestration module that deploys and manages microservice instances in cloud environments to ensure optimal resource allocation; an intelligent load balancer that dynamically redistributes incoming requests across microservice instances to avoid bottlenecks and maintain service reliability; and a real-time monitoring module configured to continuously collect system performance data, detect workload fluctuations, and provide feedback for adaptive scaling.

The invention discloses a BIM rule specification extraction method based on a large model, and the method comprises the following steps: S1, BIM standard document content extraction: extracting all contents in a document from a BIM standard document, and carrying out the corresponding processing, thereby converting the contents into a form which can be used for the subsequent semantic understanding of the large model and the BIM rule specification extraction; s2, semantic disassembly of BIM standard document content: based on the content extracted in the step S1, extracting entities and relationships thereof in the content; s3, BIM rule specification fusion and processing: based on the extracted entities and the relationships thereof, performing knowledge fusion and knowledge processing to form BIM rule specifications; and S4, BIM rule specification regularization and semantization: through BIM rule specification regularization, realizing system integration of BIM rule specifications from the BIM rule specification knowledge graph to a BIM platform. According to the method, the BIM rule specification is efficiently, rapidly and accurately extracted from the BIM standard, so that the time cost and the error possibility of manual reading and rule document arrangement are greatly reduced, the heavy workload of manual reading understanding and BIM standard execution is solved, the BIM efficiency in an actual engineering project and the BIM standard system implementation efficiency are improved, and the BIM standard system implementation efficiency is improved. And the accuracy of standardizing the BIM rule to an actual engineering project is improved.

Systems and methods are disclosed relating to distributed machine learning model resource allocation for building management systems. For example, a system can include at least one of an orchestrator or an optimizer to receive data regarding a workload to be performed using one or more machine learning models. The system can process the data to select one or more computing resources to deploy in order to perform the workload. The system can evaluate, using one or more trigger conditions, one or more characteristics of at least one of the data or the workload to determine to perform the workload.

An illustrative method includes receiving, by a data platform configured to monitor the compute environment, runtime workload data collected by an agent deployed to the compute environment, wherein the runtime workload data comprises user space data collected from a user space of the compute environment and kernel space data collected from a kernel space of the compute environment. The method further includes performing, by the data platform, a monitoring operation based on the user space data and the kernel space data of the runtime workload data.

A method for rightsizing an application, including analyzing, via processing circuitry, metrics from containers; determining, via the processing circuitry, a resource allocation and a target resource utilization for an application workload based on the metrics; and configuring, via the processing circuitry, the application workload based on the resource allocation, the target resource utilization, and the metrics.

Various approaches to efficiently allocating and utilizing hardware resources in data centers while maintaining compliance with a service level objective (SLO) specified for a computational workload is translated into a hardware-level SLO to facilitate direct enforcement by the hardware processor, e.g., using a feedback control loop or model-based mapping of the hardware-level SLO to allocations of microarchitecture resources of the processor. In some embodiments, a computational model of the hardware behavior under resource contention is used to predict the application performance (e.g., as measured in terms of the hardware-level SLO) to be expected under certain contention scenarios. Scheduling of workloads among the compute nodes within the data center may be based on such predictions. In further embodiments, configurations of microservices are optimized to minimize hardware resources while meeting a specified performance goal.

The invention provides an intelligent collaborative management method and system for network security operation and maintenance work, and relates to the technical field of network security, and the method comprises the steps: obtaining operation and maintenance work order information containing a work order priority and a processing time limit, and converting the operation and maintenance work order information into a work order feature vector; inputting the skill score, the historical completion rate and the workload of the operation and maintenance personnel into a multi-objective optimization model, and calculating a work order matching degree score and an emergency degree score; performing state coding on the work order feature vector, the personnel feature and the constraint condition by adopting a multi-layer perceptron, extracting spatial correlation by utilizing a double Q network, and calculating a target Q value; and generating a work order distribution scheme based on a Pareto optimal solution algorithm, monitoring a processing state in real time through a task collaborative scheduling model, and generating a collaborative scheduling strategy. The intelligent level of work order distribution is improved, collaborative management of operation and maintenance tasks is realized, and the operation and maintenance efficiency is improved.

The invention discloses a software configuration item test case automatic generation method based on a large language model, and relates to the technical field of software testing. In order to solve the problems that in the prior art, the flexibility and transportability of a system are limited, the system is difficult to adapt to constantly changing software requirements and designs, and manual intervention is still needed to generate and analyze a test sequence. According to the automatic generation method for the software configuration item test case based on the large language model, through intelligent text segmentation, prompt word construction and test case generation, the efficiency and accuracy of test case generation are improved, the pertinence and coverage of the test case are ensured, and the workload of manually compiling the test case is remarkably reduced; a knowledge graph of software configuration items is constructed, so that the understanding of the model on correlation and similarity among texts is enhanced, and the quality of test case generation is improved; and through a real-time feedback and optimization mechanism, the automation level and the test efficiency of software testing are effectively improved.

Rebalancing in a fleet of storage systems using data science including generating, by the cloud-based rebalancing system, a plurality of workload migration scenarios to address a detected workload imbalance among a plurality of workloads in a fleet of storage systems; determining, by the cloud-based rebalancing system, a plurality of movement vectors for each workload migration scenario, wherein each of the plurality of movement vectors describes a consideration factor for migrating a workload of the plurality of workloads within the fleet of storage systems; and generating, by the cloud-based rebalancing system, at least one rebalancing proposal based on the plurality of movement vectors for each workload migration scenario.

Systems, computer program products, and methods are described herein for application anomaly detection using advanced computational AI machine learning modeling. In this way, performance metric data is extracted and the variances are derived by comparing the performance metrics of the application workload for the current time period against the same for a previous period. The AI model is trained at regular intervals by using the derived performance metrics data to identify only the candidate workloads which are degrading or underperforming at an early state, while avoiding reporting workloads that are not causing impact to performance stability of applications across an entity database network. The system monitors application workload across a relational database of an entity for degraded application performance and identifies changes in application workload performance and applies the anomaly detection artificial intelligence machine learning model.

The technology disclosed relates to detection of data traffic in computing environments, such as cloud environments. Example systems and methods detect a plurality of workloads in a virtual network in a computing environment and deploy a plurality of probe agents to the plurality of workloads. Each respective probe agent detects network traffic on a respective workload of the plurality of workloads, scans a data packet that is at least one of sent or received by the respective workload, generates a data classification relative to the data packet, and generates a scan result that includes packet payload information and an indication of the data classification. The scan results are received from the plurality of probe agents and a computing action is performed based on scan results.

Provided in embodiments of the present disclosure are a method for scheduling workloads of a data center in an electricity market environment, and a device. In the embodiments of the present disclosure, a multi-stage random optimization solution that allows a data center to participate in a multi-level electricity market is provided. The types and data dependency relationship of workloads are taken into consideration, and a load scheduling model, in a time dimension, for the workloads is constructed; on the basis of electricity source information of the data center, a multi-stage electricity cost optimization model is constructed, so as to comprehensively consider the exogenic uncertainty of an electricity price and carbon emissions and the endogenic uncertainty of the workloads; on the basis of the multi-stage electricity cost optimization model, multi-stage optimization solving is performed on the load scheduling model, so as to obtain a load scheduling scheme and an electricity purchase strategy. Therefore, a more accurate and more reliable estimation is made concerning the electricity flexibility of the data center, such that the electricity cost of the data center can be reduced, support is provided for the reduction of carbon emissions, and the consumption of renewable energy sources is encouraged, thereby achieving the energy use flexibility of the data center in the multi-level electricity market environment.

An efficiency engine identifies container sizes for containers of a workload and allocates the containers across server clusters and nodes based on peak resource usage requirements of the containers. Runtime feedback signals are generated from monitors within the containers indicative of a quality of service and resource usage. A decision engine can identify a bin packing action to take based upon the runtime feedback signals, and a control plane can perform the identified bin packing actions to adjust bin packing based upon the runtime feedback signals. Also, adaptive adjustment can be performed based on feedback signals and using a prediction engine.

A process includes determining, by a recommendation engine, a security risk profile for a container environment. The container environment includes a plurality of pods that are to be deployed on an infrastructure that includes a plurality of nodes. Determining the security risk profile includes determining an infrastructure context characterizing the infrastructure and determining a workload context characterizing a workload associated with the container environment. The process includes determining, by the recommendation engine, a recommendation of a security policy for the container environment based on the security risk profile. The security policy includes a security control. The process includes deploying an agent to the infrastructure to manage compliance of the container environment with the security control.

The invention provides an automatic expansion and contraction method and system for a Kubernetes container based on double agents in the technical field of cloud computing resource management, and the method comprises the steps: S1, building an expansion and contraction model based on a double-agent module, a state space layering module and an attention experience playback module, and setting a reward function of the expansion and contraction model; s2, acquiring a large amount of historical workload data of the Kubernetes containers to construct a data set; s3, dividing the data set into a training set, a verification set and a test set based on a preset proportion, training the telescopic model through the training set, continuously optimizing the telescopic model through a reward function in the training process, verifying the trained telescopic model through the verification set, and testing the telescopic model passing the verification through the test set; and S4, the cloud computing resources of the Kubernetes container are scheduled through the telescopic model which passes the test. The method has the advantages that the resource utilization rate, efficiency and reliability of cloud computing resource scheduling of the Kubernetes container are greatly improved.

Systems and methods for generating fixed function shared accelerators (FFSAs) implement and / or comprise operations of receiving source code, the source code indicating a plurality of workloads to be performed by an electronic circuit; generating a plurality of abstract syntax trees (ASTs) based on the source code, wherein respective ones of the plurality of ASTs include a plurality of nodes corresponding to function instructions; generating a plurality of fingerprinting vectors corresponding to the plurality of ASTs, wherein respective ones of the plurality of fingerprinting vectors encode at least one of a number of nodes, a number of edges, a density, a computation intensity, an operands percentage, a control, or a data dependency; and providing the plurality of fingerprinting vectors to a machine learning (ML) model, wherein the ML model is configured to predict similarities between different ones of the plurality of workloads and to output at least one candidate FFSA.

A system, platform, apparatus, and associated methods for more efficiently pre-processing data for use in training a machine learning model, or for processing data for input to a trained model. Embodiments enable the dynamic allocation (or reallocation) of computational resources (e.g., processor cycles and / or memory) to the execution of a data pre-processing function or operation, and the reassignment of resources to a different data pre-processing function or operation as needed. This results in a more efficient approach to executing a set of pre-processing operations on data used to train a model or as input data for a trained model.

An illustrative method for operating an agent configuration may include providing an unprivileged agentless workload scanning configuration to collect non-runtime workload data associated with a workload deployed within a cloud environment, determining one or more properties of the workload based on the non-runtime workload data, and performing an operation with respect to a privileged agent configuration deployed within the cloud environment and configured to collect runtime workload data associated with the workload. Likewise, an illustrative method for operating an agentless configuration may include providing a privileged agent configuration to collect runtime workload data associated with a workload deployed within a cloud environment, determining one or more properties of the workload based on the runtime workload data, and performing an operation with respect to an unprivileged agentless scanning configuration deployed within the cloud environment and configured to collect non-runtime workload data associated with the workload.

An illustrative method includes accessing, by a data platform configured to monitor a compute environment, permissions data representative of a set of permissions that specify how an entity is entitled to interact with resources within the compute environment; collecting, by the data platform, runtime workload data representative of an actual interaction by the entity with the resources over a period of time; and performing, by the data platform based on the runtime workload data, an operation associated with modifying the set of permissions for the entity.

A sensor data input chiplet obtains sensor data from a sensor system. A central chiplet executes a functional safety program to dynamically compare and verify output of workloads being executed by a set of workload processing chiplets, where the workloads are executed across the set of workload processing chiplets based on the sensor data.

The invention discloses an automatic test method based on natural language processing, which comprises the following steps: S1, generating a test case: performing semantic analysis on a software requirement document or a test requirement by utilizing a natural language processing technology, extracting key information, and combining the extracted test information and a preset test case template to generate a test case; a preliminary test case is automatically generated; the key information comprises a test purpose, an operation step and an expected result; s2, analyzing the test case; s3, generating a test script; s4, executing the test script; and S5, analyzing a test result. According to the automatic test method based on natural language processing, the test requirements described by the natural language can be automatically analyzed, the test case is generated, the test is automatically executed, and the test report is finally generated, so that the software test efficiency is improved, the workload of manually compiling and executing the test case is reduced, and the test efficiency is improved. And the coverage rate and the accuracy of the test are improved, more software defects can be found, and the quality of software products is guaranteed.