389 results about "Low resource" patented technology

The invention discloses a computing power scheduling method and system based on dynamic load prediction and resource priority ranking. The computing power scheduling method comprises the following steps: collecting historical load data, task submission data and resource state data of each node in a computing power cluster; on the basis of the preprocessed multi-dimensional load feature data set, constructing an improved hybrid prediction model, optimizing model parameters through training, and predicting the load change trend of each computing power node in a future preset time period by using the trained model to obtain a node load prediction result; extracting a service level protocol parameter, a resource demand type and historical execution efficiency data of a to-be-scheduled task, and establishing a multi-dimensional resource priority evaluation index system; according to the computing power scheduling method, the problems of low resource utilization rate and high task response delay caused by low load prediction precision and mismatching of resource allocation and task priority in a traditional computing power scheduling method are solved, and the overall operation efficiency and service quality of a computing power cluster are improved.

The invention relates to the field of distributed machine learning, provides a big data-oriented distributed model training optimization method, and solves the problems of load imbalance, low resource utilization rate, large communication overhead, insufficient fault-tolerant efficiency and the like caused by data fragmentation staticization in the prior art. Load balancing is realized through intelligent clustering and overlapping control; the multi-dimensional heterogeneous resource evaluation model monitors calculation / storage / network indexes in real time, and realizes adaptive scheduling in combination with a task prediction and optimization algorithm; the hierarchical gradient synchronization mechanism adopts a tree-shaped parameter server and a dynamic compression technology, so that the communication traffic is reduced by 50%, and the precision loss is less than 0.8%; the incremental checkpoint system uses erasure code coding and parallel recovery to shorten the fault recovery time from 15 minutes to within 2 minutes, the resource utilization rate reaches 85% or above, the convergence speed is improved by 30%-40% in ResNet, BERT and other model training, and the large-scale training efficiency and the system stability are remarkably optimized.

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 provides a resource scheduling control method and system for a big data server, and the method comprises the steps: constructing a multi-dimensional resource portrait module, collecting the CPU, memory, network, storage I / O load and task queue length of each node in real time, and predicting a resource demand trend through a time sequence algorithm; extracting characteristics such as calculation intensity, data dependence, memory requirements, network transmission quantity and the like; adjusting the weight coefficients of the resource utilization rate, the task completion time and the energy consumption efficiency according to the system load and the historical effect; establishing a bipartite graph model by taking a resource trend as a node feature and a task vector as an edge feature, and calculating a matching score through graph convolution and a multi-objective optimization function; the scheduling scheme is synchronized by adopting a consistency algorithm; automatic rollback and reallocation are carried out when resources are detected to be insufficient; and optimizing a weight coefficient and a network parameter through reinforcement learning. Through the method, the system resource utilization rate can be improved, the task execution efficiency is improved, the overall scheduling effect stability is improved, and the system fault recovery time is shortened.

The invention relates to the technical field of digital computing and resource scheduling, in particular to a computing power resource intelligent scheduling and load balancing method. The method comprises the following steps: collecting performance indexes of a computing power node on different resource dimensions, and constructing a resource capability vector of the computing power node; extracting demand features of the calculation task on different resource dimensions, and constructing a task feature vector; matching the resource capability vector of the computing power node with the task feature vector to obtain an adaptation degree score; operation indexes of the computing power nodes are collected, comprehensive load scoring is carried out on the computing power nodes, and the current load states of the computing power nodes are obtained; and distributing calculation tasks based on the current load state of the computing power node and the adaptation degree score, and updating the current load state of the computing power node in real time. The problems that the resource utilization rate is low, the task response delay is high and the node load is unbalanced due to the fact that the computing power resources cannot be dynamically matched according to the task characteristics and the computing power node states in a traditional computing power resource scheduling method are solved.

The invention discloses a large model resource scheduling method and a model training method. The method relates to the technical field of artificial intelligence, and comprises the following steps: obtaining load historical data of a plurality of computing nodes corresponding to a large model in a historical time period, and determining current operation resources of the large model; inputting the load historical data into a load prediction model, and predicting the loads of the plurality of computing nodes in a preset time period by using the load prediction model to obtain load prediction data; determining deployment results of model instances deployed in the plurality of computing nodes based on the load prediction data; and performing resource scheduling based on the deployment result and the current running resource. Through the method and the device, the technical problem of low resource utilization rate of the large model caused by low resource scheduling accuracy of the large model in related technologies is solved.

The invention discloses a self-adaptive micro-service resource management method and device, and the method is applied to a resource-limited micro-service system, and comprises the steps: receiving a task, carrying out the modeling of the time sequence data and spatial dependency of a micro-service based on a space-time diagram neural network, and predicting the resource demands of a future time step, including a CPU, a memory and a network bandwidth; classifying the micro-services into resource redundancy, resource insufficiency and resource self-sufficiency according to the predicted resource demand, the current resource allocation and the resource guarantee threshold; according to the resource state and the business importance, the dynamic priority of the micro-services is calculated, the micro-services with redundant resources are sorted in an ascending order, and the micro-services with insufficient resources are sorted in a descending order; and recovering redundant resources from the low-priority micro-services, and allocating the redundant resources to the high-priority micro-services until available resources are exhausted or resource requirements are met, so that the resource utilization rate is optimized, and a service level protocol is guaranteed. According to the method, when resources are limited, the adaptability of the micro-service in different load environments can be improved.

The invention relates to a computing power scheduling method and device based on resource affinity, equipment and a medium, and the method comprises the following steps: collecting the load state data of each computing node through a computing power node monitoring module; then demand analysis is carried out on a task submitted by a user, a task resource demand vector is generated, affinity matching is carried out on the task resource demand vector and node load data, and a resource affinity matching value is obtained; then analyzing the dependency relationship between the nodes, calculating a resource dependency coefficient, and weighting the resource dependency coefficient with the affinity matching value to obtain a comprehensive affinity value; sorting the comprehensive affinity values in a descending order to form a node priority sequence; and finally, computing power mapping scheduling is carried out based on the sequence, and a final scheduling scheme is generated. According to the method, the resource matching degree and the node cooperation relation are comprehensively considered, the scheduling efficiency and the resource utilization rate are improved, and the technical problems that the resource utilization rate is low, task response delay is high and loads among the nodes are uneven due to a traditional scheduling method are solved.

The invention belongs to the technical field of computers, and discloses a computing power dynamic segmentation method and system, computer equipment and a storage medium, and the method comprises the steps: collecting system historical load data and a task execution mode, and predicting a future resource use condition by adopting a machine learning algorithm model; monitoring the overall load data of the system; searching available computing resources in the system; if the overall load data of the system exceeds an overall load threshold value, the resource demand of a single task exceeds a resource tension threshold value and / or the future resource use condition exceeds the computing power of the system, triggering a computing power segmentation decision; matching the compatibility between the available computing resources and resource parameters of a to-be-executed task, and obtaining a compatibility result; according to the compatibility result, the available computing resources and resource parameters of the to-be-executed task, a segmentation strategy is formulated; and redistributing the available computing resources according to the segmentation strategy. The technical problems of low resource utilization rate and long waiting time existing in computing power segmentation can be solved.

The invention discloses a dynamic batch processing method and device for reasoning requests, electronic equipment and a storage medium, relates to the technical field of computers, and aims to learn and obtain an optimal batch processing strategy by utilizing a dynamic batch processing reward algorithm of reinforcement learning, dynamically adjust the batch processing amount of the reasoning requests and improve the efficiency of batch processing of the reasoning requests. The reasoning request processing efficiency and the resource utilization rate are remarkably improved, the training and reasoning progress is accelerated, the resource utilization rate and the reasoning response speed are improved, the real-time performance index is monitored and obtained to dynamically adjust the optimal batch processing strategy in the process that the reasoning node executes the reasoning request, data changes are adapted, and the reasoning efficiency and the reasoning response speed are improved. The method is suitable for reasoning scenes requiring high throughput, low delay and high resource utilization rate, so that the technical problem of low resource utilization rate or response delay increase caused by static batch processing in the face of request quantity fluctuation or data distribution change can be solved, and resource management and scheduling are more efficient and unified.

The embodiment of the invention discloses a distributed computing power resource scheduling method and system, a storage medium and a program product. The method comprises the following steps: decomposing a composite computing task into a plurality of sub-tasks and constructing a dependency graph; obtaining demand resource information of each subtask and constructing a demand resource matrix according to the demand resource information; obtaining performance data of the global computing power node and constructing a global dynamic resource portrait according to the performance data; dividing global computing power nodes into different areas based on the global dynamic resource portrait; based on the demand resource matrix and the performance data, determining an adaptive area of each sub-task from all the areas, and then determining adaptive computing power nodes of the sub-tasks from the adaptive areas of the sub-tasks; and sequentially executing each subtask based on the dependency graph and the adaptive computing power node of each subtask. According to the method, the problems of low cross-data center resource utilization rate, high burst task response delay and poor heterogeneous environment scheduling efficiency can be solved, the overall computing power resource utilization rate is improved, and the task average delay is reduced.

The invention relates to the technical field of network slice resource allocation, in particular to a 6G intelligent load balancing and fault self-healing method based on AI and network slices, which comprises the step of building an AI decision-making layer model architecture, a self-adaptive optimization mechanism, a resource dynamic scheduling mechanism and a strategy execution guarantee architecture. According to the method, the network load trend is predicted in real time through the AI technology, the resource allocation between the slices is dynamically adjusted, and the problem of low resource utilization rate caused by static allocation and periodic adjustment is solved. Meanwhile, an automatic fault detection and recovery mechanism is designed, when a node fault is detected, standby resource takeover can be quickly triggered, session continuity can be kept, the service interruption time is remarkably shortened, and the requirements of a 6G network for high reliability and low time delay are met.

The invention discloses a method and a device for supporting elastic expansion capability by computing power resources of an intelligent computing center. The method comprises the following steps: monitoring a QPS index of a Kubernetes cluster service in real time, and dynamically dividing service state grades after smoothing data through a sliding window; dynamically adjusting a QPS threshold value based on the historical data and the cluster load; the HF service dynamically expands and shrinks the capacity according to the QPS, the MF service keeps a single instance, and the LF service releases the instance and pre-caches resources to the SSD node; the service life cycle is managed through queue state transition, and cache is optimized in combination with an LRU-LFU mixed elimination strategy and a disk pressure trigger mechanism; the device comprises a monitor, a scheduler, a router, a three-level cache system, a fault-tolerant processing unit, a processor and a memory. And a three-level cache system is adopted to accelerate cold start, so that the fault recovery time is effectively shortened, and the problems of high loading delay, low resource utilization rate and cross-node scheduling of the AI large model are solved.

The invention discloses an automatic machine learning collaborative optimization method based on hardware perception. The method is used for design of an integrated learning field programmable gate array accelerator in edge artificial intelligence. The method aims at solving the balance challenge between algorithm performance and hardware resource consumption. The method comprises the following steps: defining a joint optimization space containing algorithm hyper-parameters and FPGA hardware configuration parameters; a collaborative optimization objective function is established, the function is determined by algorithm performance indexes and predicted FPGA hardware resource occupancy, and resource prediction quantifies hardware influence according to algorithm hyper-parameters; and searching according to the objective function in the joint optimization space by utilizing an AutoML optimization algorithm so as to identify the optimal configuration of the balance performance and resources. According to the method, the problems of low resource efficiency and high development cost caused by separate optimization of an algorithm and hardware in the prior art are effectively solved, balanced design is realized, automation is enhanced, and complexity is reduced.

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 cross-regional computing power resource collaborative allocation method based on computing power centers, and relates to the technical field of computing power resource scheduling and optimizing.The cross-regional computing power resource collaborative allocation method comprises the steps that real-time load data, historical task execution data and network delay data of all computing power centers are collected, and a regional load feature database is constructed; predicting the load demand of each computing power center in a future time window by using a long short-term memory network model to generate a load prediction value; calculating a resource gap coefficient and a resource margin coefficient of each region according to the load prediction value and the current resource capacity, identifying a resource insufficient region and a resource surplus region, and generating a resource collaborative matching matrix between the regions; and optimizing and solving the cross-regional task allocation scheme by adopting an improved genetic algorithm to generate an optimal resource allocation strategy, and allocating the to-be-processed task to a corresponding computing power center according to the optimal resource allocation strategy. According to the method, the cooperative utilization rate and the distribution efficiency of the computing power resources in the cross-regional scene are effectively improved.

The invention discloses a network resource allocation method and device, a computer program product and electronic equipment. The method relates to the field of big data or other related fields, and comprises the following steps: acquiring resource load data of a network platform in a preset time period, and acquiring network behavior data of a user using the network platform in the preset time period; when the network platform is in the first network state, generating a first resource scheduling strategy according to the resource load data and the network behavior data, and sending the first resource scheduling strategy to the client; when the network platform is in the second network state, the first resource scheduling strategy is adjusted to obtain a second resource scheduling strategy, the second resource scheduling strategy is sent to the client side, and resource scheduling operation is executed at the client side according to the second resource scheduling strategy. Through the method and the device, the technical problems of low resource utilization rate and poor response capability during network resource scheduling in related technologies are solved.

The invention provides a server GPU computing power allocation method and system and a server, belongs to the field of server resource management, and solves the problems of low utilization rate and inflexible decision of traditional allocation resources. The method comprises the steps that a central agent constructs a directed weighted game diagram of tasks and a GPU cluster, and a global allocation strategy is solved based on Nash equilibrium; a local agent distributes tasks to a specific GPU through reinforcement learning, intelligent migration is achieved in combination with a dynamic threshold value and anomaly detection, and a strategy is iteratively optimized through federal learning. The system comprises a central agent, a local agent, a GPU cluster and an experience pool module, and a server carries the system execution method. According to the scheme, through double-layer agent cooperation and multi-algorithm fusion, the task cluster adaptation relation is quantified, the allocation strategy is dynamically adjusted, the resource utilization rate and the task processing efficiency are remarkably improved, and the adaptivity and reliability in a complex scene are enhanced.

The invention provides an internet of things equipment collaborative task scheduling optimization method and system oriented to edge computing, and relates to the technical field of the internet of things, and the method comprises the steps: collecting a task request from an internet of things terminal, and extracting task features; constructing a dynamic resource state table; generating a candidate node set; selecting a target computing node from the candidate node set, and deploying a task execution unit on the target computing node; generating a resource utilization rate index based on the real-time operation index of the node; and generating a task performance index based on task execution feedback, dynamically adjusting a task scheduling rule, or isolating an abnormal computing node from an abnormal task. Through the task scheduling method and device, the technical problems of low resource utilization rate and high response delay caused by lack of global collaboration of task scheduling in the prior art are solved, and efficient task allocation between edge devices is realized by extracting task features and establishing an optimization model, so that the overall scheduling efficiency and the system performance are improved.

The invention discloses an open architecture system for an underwater acoustic signal processor, and belongs to the technical field of underwater acoustic signal processors. The system comprises a hardware resource pool, a standardized algorithm container group, a visual processing link construction module, a containerized deployment and scheduling platform and a data processing and communication plane. According to the system, special hardware resources are pooled, an underwater sound processing algorithm is packaged into a standardized container, a user is allowed to arrange a processing flow in a graphical mode, an intelligent scheduling platform dynamically deploys the algorithm container to matched hardware for execution according to real-time resource conditions, and meanwhile, an efficient and reliable data communication channel is provided. According to the method, the problems that a traditional system is closed, poor in expansibility and low in resource utilization rate are solved, elastic sharing of hardware resources, agile deployment of an algorithm and flexible construction of a flow are achieved, and openness, flexibility and overall efficiency of an underwater acoustic signal processing system are remarkably improved.

The invention discloses a computing power resource dynamic scheduling method and system based on artificial intelligence, relates to the technical field of artificial intelligence, and solves the technical problems that the resource utilization rate of resource data is relatively low and the accuracy and efficiency of a resource dynamic scheduling method are relatively low due to the fact that the change condition of resource use is not considered and predicted in the prior art. Predicted resource data is generated based on task data; according to the predicted resource data and the task queue, generating a capacity expansion and shrinkage resource quantity; generating task priorities based on the task parameters; dynamically adjusting a task queue based on the task waiting duration; and generating a task scheduling scheme according to the capacity expansion and contraction resource quantity and the adjusted task queue, predicting resource data required by tasks in a future period of time in advance, and dynamically adjusting the capacity expansion and contraction opportunity and the expansion and contraction capacity of each piece of resource data in combination with real-time data in the current task queue, so that the utilization rate of the resource data can be maximized, and the task scheduling efficiency is improved. And the accuracy and efficiency of the resource dynamic scheduling method are improved.

The invention discloses a heterogeneous computing power resource dynamic scheduling system and method based on multi-objective optimization, and the system is deployed in a digital ecological platform complex based on multivariate consensus and embedded intelligent management. Comprising a platform access module, a data acquisition and perception module, a multi-target modeling and optimization module, a dynamic scheduling and execution module and a fault-tolerant mechanism module. The system registers as a computing power scheduling service node through an intelligent contract interface, obtains and verifies the compliance of a computing task, collects heterogeneous computing power resource node state data, and generates a scheduling decision by dynamically adjusting the weight of a target function; evaluating task suitability by using an AMCU suitability scoring model, and executing a scheduling AMCU strategy; according to the method, the technical problems of low resource scheduling efficiency and poor fault-tolerant capability in a heterogeneous computing power environment are solved, and the system throughput and the resource utilization rate are improved.

The invention provides a computing power resource partitioning method and device based on hypergraph clustering, equipment and a medium. The method comprises the following steps: constructing a computing power resource hypergraph in a computing power network resource side scene; determining an equipment degree matrix and a hyperedge degree matrix of the computing power resource hypergraph, and converting the computing power resource hypergraph into an equipment Laplacian matrix required by hypergraph clustering by using the equipment degree matrix and the hyperedge degree matrix; carrying out eigendecomposition on the equipment Laplacian matrix to obtain an equipment eigenvector; and in combination with the graph cutting target function, performing hypergraph clustering processing on the computing power resource hypergraph based on the feature vector of the device Laplacian matrix to obtain a target resource partitioning result corresponding to the computing power resource hypergraph. According to the method, the problems of low resource utilization rate, high scheduling complexity, poor resource dynamics, lack of effective partitioning strategies and the like existing in the existing computing power resource partitioning can be obviously improved.

The invention discloses a job migration method and device, a storage medium and a program product, and relates to the technical field of computers, and the method comprises the steps: obtaining multi-dimensional feature data of each arithmetic unit, preprocessing the multi-dimensional feature data into normalized resource state data, determining a to-be-migrated job in combination with a resource demand parameter of a running job, and carrying out the migration of the to-be-migrated job; the limitation that a migration decision is triggered based on a single resource index in the prior art is solved, the resource condition of each operation unit can be comprehensively considered, and new problems caused by insufficient other resources of the migrated target migration calculation unit are avoided. Meanwhile, neighborhood operation unit sets corresponding to different neighborhood radiuses are generated with the source operation unit as the center, the target migration operation units are screened through the preset target function, the traditional mode of indiscriminate traversal or random selection of the target migration operation units is changed, the search range is accurately narrowed, the search time consumption is reduced, the migration delay is reduced, and the search efficiency is improved. The problem of unbalanced resource allocation is effectively relieved, and the overall cluster utilization rate is improved.

The invention discloses a computing power resource allocation method and device and a nonvolatile storage medium. The method comprises the following steps: acquiring a service demand of a user; the service demand is divided into a preset number of sub-tasks, environment resource information of the computing power network is acquired, each sub-task at least comprises a sub-task data size and a sub-task computing power resource size, and the environment resource information at least comprises computing power resource information of computing power nodes in the computing power network; determining the preset number of subtasks and the environment resource information as environment state data; analyzing the environment state data by adopting a computing power resource allocation model to obtain a computing power resource allocation result; and allocating computing power resources to the computing power nodes corresponding to the preset number of sub-tasks based on the computing power resource allocation result. According to the method and the device, the technical problem of low resource utilization rate caused by unbalanced computing power resource allocation of different computing power nodes in a computing power network when computing power resource allocation is carried out on user services is solved.

The invention belongs to the field of computer resource management, particularly relates to a computer resource dynamic allocation management method based on cloud computing, and aims to solve the problems of locality of a decision view angle and hysteresis of a time dimension in related technologies. The method comprises the following steps: acquiring the resource utilization rate of each virtual machine in a cloud environment, wherein the resource utilization rate comprises a historical resource utilization rate and a real-time resource utilization rate; according to the resource utilization rate, predicting a resource demand of each virtual machine in a future preset time period; determining a resource competition conflict according to the predicted resource demand, and constructing a resource competition graph model; mapping the resource competition graph model into a non-cooperative game model; and determining a scheduling scheme in the cloud environment based on the non-cooperative game model, and executing a resource allocation operation according to the scheduling scheme. According to the method, potential conflicts can be identified and scheduled before resource competition actually occurs, and performance reduction caused by insufficient resources is reduced.

The invention discloses a signature generation method for a physical unclonable function (PUF) of a multiplexing LBIST structure. The signature generation method comprises an excitation decoding stage, a capture clock period calibration stage, a response generation stage and a signature correction stage. According to the invention, the PUF circuit can be realized on the basis of not changing the original LBIST main body structure of the circuit, so that enough reliability of the PUF can be ensured while low resource overhead is realized.

The invention discloses a cloud-side-end collaborative meat intelligent detection system and a cloud-side-end collaborative meat intelligent detection method. The system realizes high precision, low delay and low resource consumption of meat detection through three-level cooperation of the mobile terminal, the edge device and the cloud server and a dynamic task scheduling strategy based on confidence, and is suitable for meat safety supervision in a large-scale and complex environment.

Disclosed in the present application are resource configuration methods, a resource configuration system, an electronic device and a storage medium. A resource configuration method comprises: acquiring configuration requirement information corresponding to a computing power service; on the basis of the configuration requirement information, performing resource configuration on a general-purpose server and a graphics card resource pool to obtain a configuration result, wherein the general-purpose server is obtained on the basis of processor resource configuration, and the graphics card resource pool comprises a plurality of graphics processing units that support a hot-swapping function; and on the basis of the configuration result, providing a first instance for the computing power service, wherein the first instance comprises a processor resource allocated by the general-purpose server and a processor resource allocated by the graphics card resource pool. The present application solves the technical problems in the related art of low resource utilization rate and poor flexibility of the entire unit caused by heterogeneous models being constrained by the fixed configuration of processor resources of the entire unit.

The invention belongs to the technical field of micro-service scheduling, and discloses an APP micro-service intelligent arrangement and scheduling method and device based on a fast application, and a medium. The method comprises the following steps: firstly, performing feature extraction on equipment data to obtain feature data with high representativeness, secondly, creating an atomization service component and a service dependency relationship graph by splitting service instances, and then, on the basis of considering the dependency relationship among services, resource requirements, network bandwidth and service performance by adopting a multi-objective optimization algorithm, establishing an atomization service component and a service dependency relationship graph. And optimizing a scheduling decision, designing an alternative path and a service degradation scheme, establishing a service scheduling strategy, and finally realizing flexible scheduling of service instances based on the set service scheduling strategy. According to the method, the technical problems of insufficient resources, delay, service interruption and the like in a high-concurrency environment are effectively solved, and the performance and the reliability of the industrial APP in a complex environment are improved.