773 results about "Resource allocation (computer)" patented technology

The invention provides a computing resource optimization method and system for analysis tasks, and belongs to the technical field of computers.The computing resource optimization method comprises the steps that the priority of each analysis task is determined according to feature information of each analysis task and current available computing resource state information of the system, and a priority queue is generated; obtaining a task load prediction value according to the historical task load data and the real-time system state data; according to the type of the analysis task, the data scale and the data source position, the analysis task with the real-time requirement higher than a preset standard is allocated to an edge node to be executed, and resource allocation of the edge node is dynamically adjusted according to a task load predicted value; and dynamically allocating available resources from the computing resource pool according to the priority queue and the task load prediction value so as to execute the plurality of parallel analysis tasks. According to the resource management method based on task priority dynamic adjustment, task load prediction and edge computing optimization scheduling, real-time prediction and dynamic self-adaptive scheduling of computing resources are achieved.

The invention relates to the technical field of computer deep learning, in particular to a deep learning task hybrid deployment method and system. The method comprises the steps that S1, a user submits a task through a Kubernetes native interface; s2, a task scheduling optimizer analyzes tasks according to resource requirements and service levels and allocates the tasks to proper nodes; s3, the SLO manager tracks real-time data of various performance indexes of the system and compares the real-time data with a preset service grade target; s4, the Koordlet component executes a specific resource allocation operation on the target node; and S5, the traffic safety monitor monitors the data streams among the nodes in real time. According to the method, a mixed deployment strategy for different types of resources is provided by analyzing the periodical rule of deep learning task resource use, dynamic resource sharing of online and offline tasks is achieved, and meanwhile it is ensured that the system can still run stably and efficiently under the high-load condition.

The invention discloses a high-efficiency computer software system resource scheduling method, system and device and a medium, and the method comprises the steps: deploying a hardware counter in an operating system, and monitoring the CPU utilization rate, memory occupation, disk I / O, network bandwidth, the use condition of data aggregation system resources and the resource demand of each process or thread in real time; based on monitoring data, analyzing a current resource supply and demand state by adopting an advanced algorithm, and predicting a future resource demand trend; according to an analysis result, a resource allocation strategy is dynamically adjusted, it is ensured that a high-priority task obtains needed resources in time, and meanwhile fairness of a low-priority task is considered; data feedback after scheduling execution is collected, a scheduling algorithm is continuously optimized, and the scheduling efficiency is improved; the system, the equipment and the medium are used for realizing the high-efficiency computer software system resource scheduling method. The problems that an existing computer system is low in resource utilization rate, poor in key task stability and high in operation and maintenance cost are solved.

The invention discloses a resource allocation method and device of a server, a storage medium and electronic equipment, and relates to the technical field of computers.The method comprises the steps that firstly, hardware state data of the server, task queue data of a data processing task corresponding to the server and timestamp data of the data processing task are obtained; inputting the hardware state data, the task queue data and the timestamp data into a target resource prediction model, and predicting a resource utilization rate corresponding to a data processing task executed by the server within a preset time period; based on the resource utilization rate, generating a resource allocation strategy corresponding to the server; and executing real-time dynamic allocation of the computing resources of the server according to the resource allocation strategy. Compared with the prior art, the resource allocation can be flexibly adjusted according to the actual load change, the resource utilization rate is remarkably improved, the resource waste is reduced, the task execution efficiency and the service quality are guaranteed, and the intelligent management level and the operation efficiency of the system are improved.

The invention relates to the technical field of computers, and discloses a computing power resource multi-dimensional scheduling method and system based on dynamic weight, and the method comprises a data perception step, a weight generation step, an intelligent decision-making step and a scheduling optimization step. The system corresponds to the method. The method comprises the following steps: a data sensing step: collecting multi-dimensional state parameters of computing power nodes and carrying out feature modeling to construct a global feature space; a weight generation step: dynamically adjusting the weight of each dimension based on a machine learning model and a rule engine; an intelligent decision-making step of screening candidate nodes in the global feature space and evaluating priorities, generating an optimal node cluster and performing resource dynamic slice distribution; and a scheduling optimization step: monitoring an execution effect and performing closed-loop feedback so as to iteratively optimize a weight strategy and decision logic. The problems that in the prior art, the sensing dimension is single, and decision-making weight is rigid are solved, and multi-dimensional accurate sensing, dynamic weight decision making and elastic resource allocation of computing power resources are achieved.

Systems and methods disclosed herein automatically evaluate, select, and coordinate artificial intelligence (AI)-based agents for collaborative distributed task execution based on dynamic, multi-attribute scoring and resource allocation models. The system obtains a task specification request defining a computational requirement set, a performance metric set, and an available resource set for one or more tasks to be executed by a network of AI-based agents. A first AI model set generates domain-specific test datasets and validates prospective agents by comparing agent-generated fingerprints against predetermined hash values stored on a distributed or federated ledger. A second AI model set constructs a multi-dimensional scoring data structure for each agent by using historical performance metrics to compute weighted composite scores. The system selects a subset of AI-based agents, ranks the agents, and allocates resources proportional to each agent's composite score. A third AI model set coordinates and executes distributed computer-executable workflows across the selected agents.

The invention provides a PCIe resource dynamic configuration method and device, and relates to the technical field of computer hardware resource management. The method comprises the following steps of: establishing communication connection with the pluggable equipment through an expansion interface, and reading equipment identification information pre-stored in a characteristic value storage module of the pluggable equipment by utilizing an I2C (Inter-Integrated Circuit) interface; identifying mainboard PCIe resource allocation parameters and the equipment identification information; displaying configurable PCIe resource parameters on a system configuration interface, and providing configuration options which can be operated by a user; according to the device identification information and a configuration option selected by a user, a pre-stored PCIe configuration mapping table is inquired, a corresponding PCIe resource configuration instruction is generated, and the PCIe configuration mapping table comprises the device identification information and the corresponding relation between the configuration option and the PCIe resource configuration instruction; and configuring PCIe resource allocation of the mainboard according to the PCIe resource allocation instruction.

The invention discloses an industrial personal computer and multi-graphics card collaborative parallel computation acceleration system, which relates to the technical field of industrial resource allocation and parallel computation, and comprises a resource monitoring and predicting module, a resource management module and a prediction type resource preparation module, the task splitting and collaborative execution module comprises a task splitting module, a cross-node collaborative module and a collaborative operation engine; the intelligent scheduling and dynamic resource allocation module comprises an intelligent scheduler, a dynamic resource allocation module and a conflict avoidance module. According to the method, the GPU video memory utilization rate, the core utilization rate, the temperature, the video memory fragment rate, the available video memory total amount, the CPU core total utilization rate, the load condition and the idle core number index are collected in real time through a resource monitoring module, and the video memory capacity, the GPU core occupancy rate and the CPU load requirement are predicted in advance before a task is submitted in combination with a gradient boosting decision tree and a neural network prediction model; and resources are reserved, so that the scheduling delay is remarkably reduced, and the scheduling hit rate is improved.

The invention discloses a high-availability testing method and system for task scheduling of a computing power host management platform, belongs to the technical field of high-availability testing of computers, and aims to solve the technical problems that in the prior art, high-concurrency scene testing coverage rate is insufficient, cross-domain scheduling, failover and self-healing scene verification are imperfect, and testing efficiency is high. According to the technical scheme, the method structurally comprises the steps of hybrid load testing, wherein the elastic capacity expansion and contraction capacity and the performance stability of a system are verified by simulating a long-time stable load and burst flow scene; cross-regional intelligent scheduling: tasks are dynamically scheduled based on cost, time delay and regional strategies, and optimal resource allocation and service continuity are ensured; resource fault migration recovery: simulating node-level or region-level faults, and verifying the automatic detection, migration and recovery capabilities of the system; monitoring and data collection: collecting affairs, resources and stability indexes in real time, and providing multi-dimensional data support for verification; and analysis and optimization: identifying a bottleneck based on test data to generate optimization suggestions, and continuously improving the high availability of the system.

The invention relates to the technical field of cloud computing and edge computing, and discloses a low-delay and high-reliability distributed integrated optimization method for a cloud collaborative computer system, which comprises the following steps of: S1, acquiring node state data of a plurality of collaborative nodes, processing the node state data based on a reinforcement learning model, and generating a task allocation strategy and a data routing strategy; s2, receiving local resource optimization parameters generated by each cooperative node based on federated learning, and aggregating the local resource optimization parameters to generate a global resource allocation strategy; and S3, obtaining a task allocation log and a resource scheduling log recorded by the block chain network, and verifying execution states of task allocation and resource scheduling based on the smart contract. According to the invention, the DQN, FedProx and block chain technologies are integrated, the cooperation of task allocation, resource optimization and fault self-healing is realized, the performance of the cloud system is optimized, and the problems of task allocation and resource scheduling separation and low fault recovery efficiency of a traditional cloud system are solved.

The invention relates to the technical field of computer networks, and discloses a method for locating reasons for network dial test quality index abnormity, which comprises the following steps of: multi-source data acquisition and network topology modeling: generating state information of network equipment and links by acquiring performance data of each equipment and flow data of the links in a network, and establishing a network topology model; establishing a network topological graph model; shortest path calculation and abnormal path identification based on the graph theory: calculating a shortest path from source equipment to target equipment by using a shortest path algorithm in the graph theory, and identifying abnormal links and equipment in the path; and load evaluation and bottleneck analysis based on an information theory. Through combination of multi-source data acquisition, graph theory optimization, information theory evaluation, game theory resource allocation, deep reinforcement learning and multi-target optimization, network paths and loads are dynamically adjusted, abnormal sources are accurately positioned, and network performance, stability and adaptive capacity are improved.

This disclosure provides systems, methods, devices, and apparatuses, including computer programs encoded on computer storage media, for network slice feasibility assessment for a latency-based service level agreement (SLA). Some aspects relate to providing on-demand approval or rejection of a network slice request at a device in accordance with a latency threshold associated with an SLA of the requested network slice. In some implementations, the device may receive load information associated with the requested network slice. The device may select, in accordance with the latency threshold and the load information, a resource allocation for the network slice. In some implementations, the device may output an indication to accept or reject the network request according to the resource allocation. In some implementations, the device may use one or more machine learning (ML) models to select the acceptance or rejection of the network slice request, to select the resource allocation, or both.

The invention belongs to the crossing field of industrial control and edge computing, and particularly relates to an industrial personal computer data security dynamic protection method and system based on AI edge computing. Firstly, industrial control data encryption and standardization preprocessing are achieved through a hardware-level trusted execution environment; secondly, performing real-time anomaly detection on the encrypted data stream based on an AI model, generating a threat score by combining industrial control protocol feature analysis, and forming an edge layer intelligent detection capability; the authentication mode is dynamically switched based on the threat score, and self-adaptive strategy adjustment is realized in a fog layer; then optimizing edge resource allocation according to the threat index and the equipment load, and constructing a cloud elastic scheduling mechanism; and finally, a security system covering data encryption, real-time detection, dynamic protection and resource optimization is formed through grading component cooperation, and the method is suitable for real-time threat detection and self-adaptive response in an industrial internet scene.

The invention discloses a computing power resource allocation method, a storage medium and a computer program product. Relates to the technical field of communication. The method comprises the following steps: acquiring a target task demand of a target account; determining target weight values respectively corresponding to the plurality of computing power factors based on the target task demand; based on computing power factor sets corresponding to the multiple computing power resources and target weight values corresponding to the multiple computing power factors, scoring results corresponding to the multiple computing power resources are determined, and the computing power factor sets comprise computing power factor measurement parameters corresponding to the corresponding computing power resources in the multiple computing power factors; and according to the scoring results corresponding to the plurality of computing power resources, determining a target computing power resource allocated to the target account from the plurality of computing power resources. According to the method and the device, the technical problems of low allocation efficiency and poor flexibility when a unified cloud platform is utilized to manage and allocate various computing power resources in related technologies are solved.

The invention discloses a memory resource processing method, a memory resource processing device, a memory resource processing system and electronic equipment, and relates to the technical field of computers. The method comprises the following steps: firstly, determining a memory resource reservation strategy according to extension equipment configuration information, and finally, performing memory resource reservation according to the strategy corresponding to each hardware equipment, so that the technical problem of unreasonable memory resource allocation of the hardware equipment in the prior art can be solved; therefore, the situation that the extension equipment connected with the downlink port of the Switch switching equipment in the hardware equipment cannot be normally used due to insufficient memory resources is avoided, and the technical effect of improving the performance and the stability of the computing equipment is achieved.

The invention discloses a distributed processing method and device for charging energy data in an intelligent grid and computer equipment. The method comprises the following steps: constructing a distributed network composed of a plurality of edge computing nodes, and collecting state information of charging piles and user demand information to obtain initial data; constructing a self-adaptive energy scheduling algorithm; predicting a future charging demand trend by using a charging demand prediction model based on the initial data, and feeding back a prediction result to the energy scheduling algorithm; the physical environment around the charging pile is monitored in real time through environment sensor data collected by the edge computing node; an energy scheduling algorithm is adjusted in a charging demand peak period, a charging demand prediction model is optimized through road condition information and vehicle driving data collected by an edge calculation node in real time, and a power distribution strategy is adjusted in real time. By implementing the method provided by the invention, the charging network can be efficiently managed, the resource allocation can be optimized, and the charging efficiency and the user experience can be improved.

In accordance with example implementations, a process includes associating a secondary computer system with a primary computer system. The secondary computer system includes a first virtual machine, which is hosted by a secondary set of physical computer nodes. The primary computer system has a primary role, and the primary role corresponds to a minimum physical resource allocation. The secondary computer system is associated with a secondary role, and the second computer system is provisioned with a second physical resource allocation, which is less than the minimum physical resource allocation. The process includes, responsive to the primary computer system failing and without rebooting the secondary computer system, preparing the secondary computer system to take over the primary role. The preparation includes adding at least one physical computer node to the secondary set of physical computer nodes to cause the second physical resource allocation to meet or exceed the minimum physical resource allocation. The process includes, responsive to the primary computer system failing and without rebooting the secondary computer system, causing the secondary computer system to take over the primary role.

The invention discloses a parallel processor dynamic resource allocation system and method based on reconfigurable hardware, and relates to the technical field of computer chips, and the system comprises a workload monitoring module which is responsible for monitoring the workload type and the resource demand of a task processed by a parallel processor in real time, and determining the task type and the resource demand by identifying the task type and the resource demand; a monitoring result is fed back to the resource allocation control module; the resource allocation control module is responsible for generating a corresponding resource allocation control signal based on feedback information of the workload monitoring module and dynamically configuring the reconfigurable hardware module; the reconfigurable hardware module is responsible for realizing efficient adaptation to diversified tasks through a plurality of reconfigurable units formed by programmable logic devices according to different working load dynamic reconfiguration functions and connection modes; and the data caching and transmission module is responsible for cross-module data circulation and global data storage. Different task requirements can be accurately adapted, and the resource utilization rate is improved.

The invention discloses a big data processing system based on AI cloud computing training, and the system comprises a data collection module which collects multi-source image data through a cloud interface, and generates a standardized data set after distributed cleaning and feature extraction; the data prediction module constructs a resource prediction model, and outputs a resource availability prediction vector in combination with a federated average algorithm and a space-time diagram neural network; the dynamic scheduling module adopts a double-queue architecture and an NSGA-II algorithm to optimize resource allocation and reduce task delay and energy consumption; the fusion decision module integrates the data and the scheduling result, generates a decision through an AI model, dynamically adjusts task migration, and improves the cloud image processing efficiency; therefore, the number of the image data fragments can be automatically adjusted according to the real-time load and the storage capacity of the cloud computing nodes, load imbalance among the nodes is avoided, the efficiency of image data cleaning, normalization and feature extraction is greatly improved, the data processing time delay is reduced, and the overall throughput is improved.

The invention discloses a resource allocation method and device, and electronic equipment, and relates to the technical field of computers, and the method comprises the steps: obtaining the size of a first computing resource required by a to-be-processed task, adjusting at least one computing resource block of a target computing resource node corresponding to the to-be-processed task according to the size of the first computing resource, therefore, flexible allocation of the computing resource blocks is realized, the computing resource blocks are enabled to be matched with resources required by the task, then the to-be-processed task is processed based on the at least one adjusted computing resource block, a processing result of the to-be-processed task is obtained, the problem of mismatching of the fixedly divided computing resource blocks and the to-be-processed task is avoided, and the processing efficiency of the to-be-processed task is improved. And the utilization rate of computing resources is improved. Therefore, the technical problem that the utilization rate of the computing resources is low can be solved, and the technical effect of improving the utilization rate of the computing resources is achieved.

The invention discloses a processor, a graphics card, equipment and a resource allocation method and device, and belongs to the technical field of computer resource management. The processor comprises a thread group assembling unit, a thread group scheduling execution unit and at least two processing units, the thread group assembling unit is used for assembling the tasks into one or more thread groups based on the attribute information of the tasks in a task assembling stage; and allocating computing resources for one or more thread groups with the thread group granularity, wherein each thread group is allocated with a processing unit; the thread group scheduling execution unit is used for scheduling one or more thread groups to the processing unit corresponding to each thread group for execution according to a unified scheduling rule, and the unified scheduling rule is a scheduling rule shared by different types of tasks; and the at least two processing units are used for executing the scheduled thread groups. And the computing resources are allocated according to the thread group granularity, so that the execution efficiency of the processor is improved.

The present invention extends to methods, systems, and computer program products for optimizing resource allocation in view of predicted network traffic patterns and predicted power consumption. Network packets defining a network traffic flow can be received at a platform over time. Metrics can be derived from one or more applications executing on resources of the platform and processing data contained in the network data packets. Model training data can be formulated from the metrics. A resource adjustment model can be trained using the model training data. Executing the model can be automated to adjust resource allocation at the platform. Additional network packets defining an additional network traffic flow can be received at a platform over time. Data contained in the additional network packets can be processed using the adjusted resource allocation.

The invention relates to the technical field of environment monitoring, in particular to an intelligent water quality monitoring method and system for collaborative sampling of a bionic fish school and an unmanned aerial vehicle and computer equipment. The method comprises the following steps: dividing monitoring grid units by using GIS data, establishing a layered communication architecture, dynamically deploying a bionic fish school to carry out real-time water quality monitoring, and scheduling and optimizing resource allocation through a fuzzy rule; historical pollution event data and real-time sensing data are integrated to construct a water quality evaluation index system, and a Nash equilibrium strategy is adopted to determine an optimal weight to generate a dynamic pollution situation cloud chart; constructing an inferior fingerprint database to realize rapid identification of pollution events and trigger a two-stage response mechanism; and finally, combining the bionic fish school sampling data and the unmanned aerial vehicle pre-diffusion coordinate to dynamically simulate the effect of the treatment measure. Air-water integrated efficient monitoring is achieved, the accuracy and timeliness of water quality anomaly detection are improved, and comprehensive technical support is provided for water area environment protection.

The invention discloses a computer resource allocation management system and method based on data analysis, and relates to the technical field of computer resource management.The method comprises the steps that data are collected in real time, and historical data are collected; constructing a spatio-temporal data warehouse based on the real-time data and the historical data; extracting static features and dynamic features of the task, and combining the static features and the dynamic features to generate a high-dimensional task feature vector; constructing a resource semantic knowledge graph, and defining a computing resource entity, a relationship and a constraint rule; combining the spatio-temporal data warehouse and the resource semantic knowledge graph to construct a demand prediction model, and outputting probability distribution prediction of future resource demands; and in combination with real-time data, calculating resource use efficiency and a load balancing index in real time, and dynamically adjusting a resource allocation decision. The demand prediction model is constructed based on the spatio-temporal data warehouse and the resource semantic knowledge graph, resource allocation is carried out actively, resource shortage or idle is avoided, and the resource utilization rate is improved.

The invention discloses a server power consumption management method and device, electronic equipment and a storage medium, and relates to the technical field of computers, the future load change trend of a server can be predicted according to the load data of the server at the current moment, and a power consumption management strategy of the server is generated according to the load data and the load change trend at the current moment. The power consumption management strategy is utilized to adjust the operation parameters of at least one component in the multiple components, and the power consumption management strategy is predicted in advance and adjusted in real time, so that the load fluctuation of the server can be dynamically adapted, and insufficient resource allocation of the server under different load conditions due to a fixed static power consumption management strategy is avoided. Therefore, the technical problems that the resource allocation of the server is unbalanced and the energy efficiency is low due to the fact that the power consumption management strategy cannot be dynamically adjusted in related technologies can be solved, and the purposes of optimizing the resource allocation of the server and improving the energy efficiency of the server are achieved.

The invention discloses a system and a method for dynamic combination memory resource allocation of a multi-level exchanger, and relates to the technical field of computers, which comprises the following steps: dynamically acquiring and analyzing type data of a target configuration space when a server is started, identifying a first hardware resource connected with a current peripheral interconnection link by an identification engine according to the type data, and when the first hardware resource comprises the multi-level switch, identifying the second hardware resource connected with the multi-level switch, and determining which devices need to be subjected to resource reservation according to the identification result of the second hardware resource without checking each possible connection condition one by one. And the first memory resource is allocated for the multi-level switch and the second hardware resource based on the second identification result, so that the situation that when the peripheral interconnection link has too many switches, the server starts to consume too much time for resource allocation is avoided, the time consumption during server starting is reduced, the starting efficiency of the server is improved, and the user experience is improved. And the stationarity and the stability of memory resource allocation are further ensured.

The invention provides a multi-core parallel simulation method and device and electronic equipment, and relates to the technical field of computer simulation, and the method comprises the steps: determining all simulation entities in a simulation environment; all simulation entities in the simulation environment are respectively packaged into independently running entity processes, and a message manager module, an object manager module and a time manager module are respectively packaged into independently running management processes; running each entity process and the management process in parallel on respective pre-allocated operation cores, and monitoring each operation core; according to the resource use data and the task completion progress of the entity process and the management process on each operation core, determining an overloaded operation core; and dynamically adjusting the execution sequence and resource allocation of the entity process and the management process on the overloaded operation core. According to the invention, through real-time monitoring and dynamic adjustment, load balancing is effectively realized, and the situation that some cores are overloaded and other cores are idle is avoided.

The invention discloses a computing system and a control method, device, equipment, medium and product thereof, and relates to the technical field of computers.A host device, a first switch and an acceleration computing device are divided, the first switch comprises a plurality of interconnected switching controllers, and uplink ports of the switching controllers are connected with the host device; the downlink port is connected with the acceleration computing device, the binding relation of the physical link layer between the mainboard and the acceleration card is removed, the port configuration and the resource allocation path of the exchange controller can be flexibly adjusted, and the acceleration computing resources can be allocated according to needs. The first board card provided with the first slot and the cable adapter plate are arranged in the acceleration computing device, so that the acceleration computing device with high-density deployment is realized. Therefore, the method solves the problem that the expansion capability of acceleration computing resources of a multi-card system reaches a bottleneck, and lays a foundation for expanding large-scale computing power by a single host and constructing a multi-host shared large-scale computing resource pool.

The present disclosure relates to the technical field of computer technology and cloud computing. Disclosed are a resource allocation method, an electronic device and a computer readable storage medium. The method comprises: acquiring tag information, wherein the tag information is used for characterizing behavior attributes of a target object; determining a target virtual resource view associated with the tag information, wherein the target virtual resource view is used for displaying a virtual resource management relationship in a target application scenario; on the basis of a preset scheduling policy and the target virtual resource view, selecting a target physical server corresponding to the tag information, wherein the preset scheduling policy is used for determining scheduling relationships between different physical servers; and using the target physical server to allocate a corresponding target cloud server instance to the tag information. The present disclosure solves the technical problems in the related art of waste of resources and reduction of service stability and response speed caused by mutual contention of virtual resources between instances.

Embodiments of the present application relate to the technical field of computer storage, and provide a resource allocation method and apparatus, an electronic device, and a storage medium. The method comprises: in response to a memory acquisition request sent by a target host, a memory management unit in a resource allocation system acquiring demanded memory capacity and a memory starting address carried by the memory acquisition request; on the basis of a memory allocation unit in the resource allocation system, selecting from among at least two memory modules a target memory module matching the demanded memory capacity; and on the basis of a memory address router in the memory allocation unit and the memory starting address, performing address translation on the target memory module, and allocating to the target host the target memory module having undergone address translation, thereby achieving dynamic resource allocation of the target host. In this way, the interaction between a memory management unit and a memory allocation unit ensures rapid allocation of idle memory modules in a system, thereby improving the utilization rate of memory resources, and reducing costs of usage of the memory resources.