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

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.

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 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 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 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.

The invention discloses a model deployment method and device, computer equipment and a storage medium, and the method comprises the steps: determining a target model from a model square through responding to a model deployment request; obtaining model support information of the target model based on the dynamic mapping table; obtaining computing power resource index data of each computing node corresponding to the model support information; and deploying the target model based on the computing power resource index data of each computing node and the dynamic mapping table. According to the method, insufficient or excessive computing power resources caused by computing power resource estimation deviation of a user can be avoided; according to the method, the computing power resources can be accurately and flexibly distributed and deployed for the target model, the situation that the computing power resources are insufficient can be avoided, and when the computing nodes break down, the computing power resource distribution of the target model can be dynamically adjusted according to the computing power source index data of each computing node, so that the overall resource utilization rate is improved.

The invention discloses a GPU Box hardware decoupling system based on a Retimer card and a PCIe Switch chip, and belongs to the technical field of computer hardware architecture and high-speed interconnection. According to the system, a Retimer card and a PCIe Switch chip are integrated in an independent GPU Box, and a decoupling link of a CPU server and a GPU acceleration card is constructed; the Retimer card realizes 30-meter long-distance PCIe signal transmission and breaks through physical distance limitation; the PCIe Switch chip pools GPU resources through a dynamic routing and MRIOV technology, supports flexible allocation of computing power by multiple servers, and realizes Peer-to-Peer direct connection communication between GPUs. Aiming at a large model reasoning scene, the system optimizes KV cache bandwidth allocation and video memory and memory cooperative scheduling, so that the 100B parameter model reasoning throughput is greatly improved; and meanwhile, the usability of the system is greatly improved through fault isolation and hot plug design. According to the method, the problems of physical binding of the CPU and the GPU, limited transmission distance, rigid resource allocation and the like in a traditional architecture are solved, and the method is suitable for large-scale AI calculation and distributed GPU cluster deployment.

The invention belongs to the technical field of computers, and particularly relates to a data center server resource allocation method and system based on dynamic load balancing, and the method comprises the steps: collecting a physical resource state, instance constraint and network flow through a multi-dimensional probe, constructing a dynamic weighted load scoring model, and introducing a migration penalty term to correct node load evaluation; solving maximum weight matching of the bipartite graph with constraint by combining a Hungary algorithm, and realizing global optimal mapping of the migration instance and a target node; a memory, a CPU and network resources are pre-configured before migration, a delay recovery mechanism is triggered after migration, and the service quality is guaranteed. The system comprises a resource acquisition module, a score generation module, a matching solution module, a pre-configuration module, a self-adaptive period adjustment module and the like. The cluster resource utilization rate is obviously improved by 28%, hotspots are reduced by 76%, the SLA default rate is reduced by 92%, meanwhile, energy is saved by 15%, and collaborative optimization of efficiency and service quality is achieved.

The invention provides a multi-system redundant computer processing method and system and electronic equipment, and belongs to the technical field of computers, and the method comprises the following steps: S101, obtaining eight different nodes; s102, establishing a communication channel, encrypting the communication channel, and establishing an on-off channel; s103, acquiring a task feature vector, acquiring a resource feature vector, performing a dynamic clone selection algorithm, acquiring an optimal resource scheduling scheme, and performing resource scheduling; s104, detecting the computer computing node and the computer total node, and if any one node fails, starting a redundancy switching mechanism; s105, the real-time resource use condition of each computer computing node is monitored in real time, and when the load of at least one computer computing node reaches 85%, a redundant resource scheduling mechanism is started; s106, during operation, recording the related information in real time by the backup database; according to the method, resource allocation is more reasonable, and the data processing speed is increased.

The invention relates to the technical field of cloud computing and server hosting technology, and discloses a single machine room fault recovery laas cluster processing system, comprising a fault detection module which is responsible for monitoring the health state of each node in a cluster in real time, analyzing system logs and performance indexes, and quickly identifying potential faults; the fault evaluation module is used for receiving the output of the fault detection module, evaluating a fault influence range and severity and providing a basis for a fault recovery strategy; the resource scheduling module is used for dynamically adjusting resource allocation according to a fault assessment result and selecting an optimal healthy node for service migration; and the service migration module is responsible for smoothly migrating the service on the fault node to the healthy node, and reducing the service interruption time by adopting a live migration technology. The invention provides a single-computer-room fault recovery laas cluster processing system, and solves the problems that a cluster processing system in the prior art is high in single-point fault risk, delayed in fault detection response, inflexible in resource scheduling and insufficient in cross-computer-room cooperative capability.

The embodiment of the invention provides a memory resource allocation method and electronic equipment, and relates to the field of computers.The method comprises the steps that a first resource reference value corresponding to a slot position is determined from a plurality of memory resource reference values corresponding to a plurality of equipment types, the slot position supports equipment insertion of the multiple equipment types, and the first resource reference value corresponds to the slot position; one device type corresponds to one resource reference value; adjusting the first resource reference value based on the physical attribute of the slot position and the performance attribute of the slot position to obtain a second resource reference value corresponding to the slot position, and pre-allocating a reference memory resource of the second resource reference value to the slot position; when it is detected that the target device is effectively inserted into the slot position, determining a target resource demand value corresponding to the target device according to the register information of the target device; and allocating a target memory resource of the target resource demand value to the target device from the reference memory resources.

The invention relates to a tensor core resource allocation method, computer equipment, a readable storage medium and a computer program product. The method comprises the steps of determining a target queue corresponding to a first target calculation instruction from an execution queue based on a priority corresponding to the first target calculation instruction under the condition of determining that the first target calculation instruction from a target thread bundle completes data synchronization, and writing the first target calculation instruction into the target queue, the execution queues comprise at least two execution queues corresponding to different priorities; under the condition that the tensor core resource is idle, the calculation instruction stored in the queue to be allocated is written into the tensor core resource to execute corresponding calculation, and the queue to be allocated is the queue with the highest priority in the execution queues storing the calculation instruction. By adopting the method, the resource utilization rate can be improved.

The invention belongs to the technical field of computer server management, and particularly discloses a laboratory collaborative environment-oriented user and data full-life-cycle server management system, which comprises a container virtualization layer, a server management layer and a server management layer, dynamic resource allocation is supported through a state saving-resource reallocation-environment recovery mechanism; the heterogeneous transaction coordination layer adopts a Saga transaction mode to coordinate a heterogeneous system so as to realize cross-system transaction consistency; the data collaboration ecological layer establishes typed management of a personal data volume, a shared software environment volume and a public data set volume, and supports software environment encapsulation sharing and versioning management; and the metadata management layer realizes real-time update and high-performance query of metadata based on a Redis hot update architecture. According to the method, the problems of inflexible resource allocation, incomplete environment isolation, dilemma in authority management and difficulty in data collaboration and recovery in the prior art are solved.

The invention relates to a computer room equipment management method and system based on cloud computing, and the method achieves the dynamic monitoring and risk assessment of a construction process through constructing a computer room component model of a real object size and integrating a design value and actual measurement data. Firstly, a system calculates a direct risk factor (reflecting actual construction deviation) and an indirect risk factor (quantifying influences of associated components) of each component, and a comprehensive checking list is established; secondly, importance parameter correction risk factors are introduced, external influence coefficients are calculated in combination with environmental data, and an elastic construction period model is constructed to achieve dynamic adjustment; when component auditing fails, the system automatically marks the associated component and calculates a risk increment, and an auditing list is updated through iteration until a complete component path is formed; according to the method, a physical connection relationship, a risk conduction mechanism and environmental factors are innovatively incorporated into a unified analysis framework, and the problems of risk prevention and control lagging, resource allocation imbalance and the like in a traditional management mode are solved.

The invention belongs to the field of computer operating systems, and aims to provide a computing resource allocation method, system and equipment for a multi-core system and a medium. According to the method, the computing resources with the specified size are divided to serve as the reserved computing resources, the specified computing resource occupancy rate of each service in the multi-core system is calculated, and when the specified computing resource occupancy rate of any service is larger than the computing resource occupancy rate threshold value, certain computing resources are allocated to the service in the reserved computing resources. And normal operation of the business is facilitated. Based on the method and the device, each service can occupy the reserved computing resources according to the own specified computing resource occupancy rate, so that the jitter of other services caused by overhigh sudden specified computing resource occupancy rate of one service can be prevented, and the problem of mutual interference among the computing resources of the services is further avoided; and the stability of the multi-core system is improved.

Disclosed herein is a computer implemented method of deployment and resource allocation of microservices of a distributed computing environment. The distributed computing environment comprises a microservice deployment scheduler and one or more computing nodes. The microservice deployment scheduler comprises a reinforcement learning based dynamic workload orchestration module. The method comprises receiving microservice constraints descriptive of a microservice computing task by the microservice deployment scheduler. The method further comprises receiving node specific properties from the one or more computing nodes by the microservice deployment scheduler. The node specific properties are descriptive of a computing capacity and / or computing capabilities of the one or more computing nodes. The method further comprises orchestrating operation of the one or more computing nodes by the microservice deployment scheduler by inputting the microservice constraints and the node specific properties into the reinforcement learning based dynamic workload orchestration module.