114 results about "Resource reallocation" patented technology

The invention relates to the technical field of network time delay scheduling, and discloses a deterministic network time delay scheduling method and device based on a TSN and a storage medium. The method comprises the following steps: carrying out graph theory analysis on a TSN network topology structure to obtain a network node connection relationship and link bandwidth information; extracting time delay requirements and bandwidth requirement characteristics of the data streams, and generating a data stream priority queue and a resource requirement index matrix; inputting a deep Q network, establishing a state-action mapping relation including a network load state and a time slice distribution action, and generating an initial time delay scheduling strategy model; calculating queuing time delay, transmission time delay and processing time delay of each data stream, and generating a time slice resource redistribution scheme; generating a global resource allocation matrix based on the time slice resource reallocation scheme; and parameter feedback updating is carried out on the initial time delay scheduling strategy model to obtain a target time delay scheduling strategy model, and the utilization efficiency of network resources is remarkably improved through bandwidth borrowing and time slice reallocation.

The invention provides a supercomputing resource intelligent allocation method and system based on AI load monitoring, and the method comprises the steps: firstly obtaining a real-time load data set of a plurality of computing nodes in a supercomputing system, including a resource occupancy rate index and a task queue state parameter, then carrying out the load feature extraction processing of the real-time load data set, generating resource occupancy fluctuation characteristics and task queue evolution characteristics, performing resource demand analysis on the load characteristic set by using a pre-constructed resource demand prediction model, predicting resource demands of each computing node in a subsequent time window, determining resource allocation priorities and migration strategy parameters based on prediction results, and performing resource allocation according to the resource allocation priorities and the migration strategy parameters. The resource dynamic allocation instruction is generated and sent to the target computing node, the resource reallocation operation is triggered, intelligent and efficient allocation of super computing system resources is achieved, the resource utilization rate and the task execution efficiency are improved, and the overall energy efficiency ratio and stability of the system are optimized.

The invention provides a virtualized computing resource scheduling method and system based on a power wireless local area network, and the method comprises the steps: firstly obtaining power equipment operation load data of access equipment in the coverage of the power wireless local area network, including real-time current fluctuation and other features, carrying out the load feature extraction of the power equipment operation load data, and carrying out the load feature extraction of the power equipment operation load data; performing dynamic resource demand prediction on the set based on a preset load prediction model to generate a virtual resource demand prediction result including computing resource allocation magnitude and the like; generating a virtualized resource scheduling strategy containing edge computing node resource allocation topology and the like according to a virtualized resource demand prediction result, and finally dynamically adjusting virtualized computing resources based on the virtualized resource scheduling strategy, triggering resource reallocation and updating a global resource state mapping table. Effective scheduling of virtualized computing resources of the power wireless local area network is realized.

The invention relates to the technical field of network communication, provides a network self-healing device based on node state data, and aims to improve the stability and self-healing capability of network communication. Through real-time monitoring of the network state, state estimation and filtering are carried out in combination with an extended Kalman filtering algorithm, and fault prediction, path selection and resource allocation are carried out through an ant colony algorithm and a simulated annealing algorithm, so that prediction and rapid recovery of potential faults are realized. The device comprises a state monitoring module, a state estimation and filtering module, a fault prediction and prevention module, a fault recovery module and a resource redistribution module, forms a closed-loop optimization mechanism, and is particularly suitable for a complex large-scale network environment, such as a data center network.

The invention provides a computing power resource scheduling method and system based on cloud network integration. Selecting a computing power task to be scheduled as a current scheduling task, and generating a node resource adaptation matrix based on the real-time load data, the cloud network topological relation between the nodes and the dynamic bandwidth data; then calculating the lowest scheduling cost of each node according to the matrix, and determining a dynamic adjustment factor when a resource allocation conflict occurs in the current scheduling task; based on the lowest scheduling cost, the computing power demand scale and the data transmission estimated overhead, calculating the final scheduling overhead for scheduling the current scheduling task to each node; and finally, allocating tasks to a target node according to the final scheduling overhead, updating a running task queue, if a conflict occurs, calling a dynamic adjustment factor to execute resource reallocation, and updating the queue after the reallocation succeeds. And circulating the process until all tasks are scheduled. According to the scheme, optimal scheduling of computing power resources in a cloud network convergence environment can be realized.

The invention discloses a security resource allocation method for a low-altitude economic unmanned aerial vehicle semantic communication network, which relates to the technical field of wireless communication network resource allocation and comprises the following steps: acquiring communication data and network state sensing data of each node in an unmanned aerial vehicle network; performing semantic analysis on the communication data, determining semantic importance and setting a transmission priority; monitoring a security state based on the network state sensing data, identifying a potential security threat and determining a threat level; establishing a security constraint condition set according to the threat level and the semantic importance; establishing a multi-objective optimization model, and performing optimization allocation on network resources under the security constraint condition set; performing hierarchical processing and resource scheduling on the communication task based on the transmission priority and the optimal distribution result; and monitoring a resource allocation effect, and triggering resource reallocation when the performance index deviates from a threshold value. According to the method, adaptive resource allocation considering communication efficiency and safety protection is realized, and the overall performance of the unmanned aerial vehicle semantic communication network is improved.

The invention discloses an application resource adaptive allocation management system for a concentrator, which relates to the technical field of concentrator management and comprises a monitoring module, a resource allocation module, a priority management module, a resource recovery module and a data storage module. According to the invention, the resource use conditions of the CPU, the memory, the bandwidth and the terminal equipment are monitored in real time, and the network flow and the terminal equipment demand data are collected. A resource allocation strategy is dynamically adjusted to ensure efficient utilization of resources. And according to the task type, the important value of the terminal equipment and the CRDI value, the priority is dynamically adjusted to ensure that the key task and the important terminal equipment preferentially obtain resources when the resources are insufficient. By monitoring the resource utilization rate and the task state, the recycling operation is triggered in real time, and the idle resources are redistributed to the current terminal equipment with the demand, so that the overall resource use efficiency is optimized.

The invention discloses an LCD display screen production whole-process collaborative management method and system based on the Internet of Things, particularly relates to the technical field of production process collaborative management, and is used for solving the problems of circular waiting and resource stiffness caused by lack of global resource allocation dependency relationship analysis in a complex production scene in an existing collaborative management method. Equipment state data and material position data of each process are acquired in real time, a resource allocation relation graph with production equipment as resource nodes and production tasks as task nodes is constructed, a resource access sequence of each production task is analyzed to identify a circular waiting path, and a process association network and a production flow network are combined to evaluate a blockage propagation risk. And finally, on the basis of the relief urgency degree, the production tasks are selected to carry out resource reallocation so as to realize the relief of the circular waiting path, and the overall efficiency of the production line and the resource utilization rate are improved.

The invention provides an industrial internet scene-oriented dynamic resource allocation method and related equipment, which considers that the industrial internet scene has mobile position production equipment of a time-varying geographic position, so that the total time delay and the total energy consumption of the scene are analyzed, and processing is carried out through a resource consumption algorithm based on the total time delay and the total energy consumption, so that the dynamic resource allocation efficiency is improved. According to the error between the predicted total resource energy consumption and the actual total resource energy consumption, guiding whether the target edge server executes resource reallocation in the communication range or not; and then adaptively solving an optimal resource allocation scheme in the considered scene based on the near-end strategy optimization algorithm as a target resource allocation strategy, and carrying out resource allocation according to the target resource allocation strategy, so that the resource allocation strategy can be adaptively optimized while the production demand change of the industrial internet scene can be met, and the resource allocation efficiency is improved. And the overall efficiency and reliability of the system are improved.

The invention relates to the technical field of ocean information processing, in particular to an ocean supercomputing environment-oriented heterogeneous computing power resource dynamic allocation method, which comprises the following steps of S1, monitoring dynamic demand characteristics of an ocean data processing task in real time, including a task calculation amount and a task real-time level; s2, dynamically calibrating task priorities; s3, screening out target resources which simultaneously meet the task calculation amount requirement and have response speeds matched with the real-time level; s4, allocating the task to a target resource; s5, if the load rate of the target resource exceeds a preset threshold value, triggering resource redistribution; and S6, outputting a final resource allocation scheme, and recording the completion time of the corresponding task and the utilization rate of each computing power resource. According to the invention, through the task priority-driven dynamic scheduling and real-time load-aware redistribution mechanism, the timeliness guarantee of key tasks and the efficient utilization of heterogeneous resources in the ocean supercomputing environment are realized.

An energy-conscious workflow planning system based on LLM with dynamic resource allocation, consisting of: a workflow input interface configured to receive workflow-directed acyclic graphs (DAGs), energy budget constraints, system performance constraints, and natural language requests from human operators; a large language model (LLM) logic module connected to the workflow input interface and configured to analyze the workflow specifications and system constraints in natural language, generate energy-conscious planning recommendations based on the analyzed workflow specifications, and provide explainable planning rationales in natural language; a reinforcement learning-based scheduling unit connected to the LLM reasoning module and configured to: receive scheduling recommendations from the LLM reasoning agent, fine-tune task-resource assignments by dynamically adapting to runtime variations, and perform online resource redistribution under runtime variability; an energy monitoring unit configured to: continuously monitor CPU and GPU utilization in heterogeneous clusters, track power consumption and thermal limits per node, and generate energy profiles for system components; a multi-objective optimization engine configured to: perform a Pareto-optimal scheduling analysis that balances energy consumption, lead time and reliability, apply statistical and AI-supported trade-off analyses and ensure optimal resource allocation based on Pareto frontier analysis; a dynamic resource allocation unit configured to: use predictive models that incorporate LLM inferences and feedback from reinforcement learning, reassign tasks between nodes and clusters while minimizing energy consumption and improving system throughput based on the predictive models; a performance optimization module configured to optimize scheduling decisions using multi-criteria optimization analysis; and a user interface that allows human operators to override and refine planning strategies in real time based on verifiable planning reasons.

The invention discloses a human resource sharing service platform based on big data, and relates to the technical field of human resource sharing service. Comprising a demand analysis and initial weight setting module, an information push and resource allocation module, a channel performance monitoring and data analysis module, a dynamic weight adjustment and optimization module and a resource reallocation and weight updating module. And the demand analysis and initial weight setting module comprehensively knows recruitment demands of the enterprise before the recruitment activity starts, and sets the initial push weight of each recruitment channel based on the recruitment demands. According to the method, the efficient channel is preferentially put by setting the initial push weight, the channel performance is monitored and analyzed in real time, the conversion effect is accurately predicted in combination with the machine learning model, the push weight is dynamically adjusted, the resources are inclined to the efficient channel, and the conversion rate and the recruitment quality are improved. Meanwhile, resource allocation is optimized, waste is avoided, cost is reduced, the period is shortened, competitive advantages of talents of enterprises are enhanced, and business growth and market competitiveness improvement are facilitated.

The invention discloses a distributed smart home equipment management system based on the Internet of Things, and relates to the technical field of smart home. The central controller is in communication connection with an equipment access module, a data acquisition module, an equipment anomaly classification module, an equipment anomaly judgment module, a resource allocation evaluation module, a strategy optimization module and a log recording module, and all the modules are in electric signal connection. According to the invention, by monitoring the operation state of the smart home equipment, the equipment abnormity can be found and responded, and by using the cooperative work of the equipment abnormity judgment module and the resource allocation evaluation module, when the performance reduction or security vulnerability of the smart home equipment is detected, the resource reallocation strategy is triggered, and the key equipment is ensured to obtain priority processing. The response speed of the system is improved, the equipment fault can be repaired in the shortest time, and the service interruption time caused by the equipment fault is shortened.

The invention relates to the technical field of project management, and particularly discloses a project management method and system based on an AI large model technology, and the method comprises the steps: collecting multi-dimensional project execution data, carrying out the data cleaning and feature coupling analysis of the multi-dimensional project execution data, and generating a normalized project feature tensor; inputting the task delay risk into a pre-trained time sequence prediction large model, and outputting a task delay risk probability distribution matrix; carrying out risk level label mapping based on the task delay risk probability distribution matrix, and generating a multi-level risk early warning signal; and in response to the risk event exceeding the preset threshold in the multi-level risk early warning signal, a resource redistribution scheme is generated, and a project manager not only can monitor the project progress in real time, but also can quickly adjust resource configuration when the risk occurs, thereby realizing dynamic management. The intelligent project management mode based on data driving not only improves transparency and controllability of the project, but also provides powerful technical support for successful implementation of the project.

The invention discloses a block chain edge computing resource collaborative scheduling system of Internet of Things equipment, and relates to the technical field of Internet of Things, comprising an equipment edge networking module used for deploying edge nodes in an Internet of Things equipment dense area and accessing the edge nodes to a block chain network; the resource state initialization module is used for sending task information to the edge node layer after the Internet of Things equipment generates a task; the intelligent task distribution module is used for calculating an optimal task distribution scheme after the edge node receives the task, and distributing the task to the edge node for processing according to the optimal task distribution scheme; and the dynamic resource adjustment module is used for monitoring the resource load condition of the edge node in real time, and reallocating resources according to a dynamic resource self-adaptive adjustment strategy when the resource load is unbalanced, so that the stable operation of the system is realized. And efficient collaborative scheduling and safe and credible management of edge computing resources among the Internet of Things equipment are realized.

A computer-implemented method for receiving, by a processor set, a workload for processing. The processor set may further collect historical data and real-time data related to processing the received workload and generate a feature map by investigating the received workload, the historical data, and the real-time data. The processor set may further divide the workload into workload steps and assign each workload step to one of a plurality of groups. System resources may also be calculated for each of the workload steps. The processor set trains a machine learning model to determine influence factors for each of the plurality of groups, and then, the processor set reallocates the workload steps based on the calculated system resources and the influence factors.

The invention relates to a production scheduling and worker allocation joint optimization method for an aircraft pulsation assembly line, and the method comprises the following steps: obtaining assembly task data and resource data of the aircraft pulsation assembly line, resources being workers; constructing a dictionary sequence dual-objective optimization integer programming model, and taking the minimum pulsation period and the total resource consumption as objective functions; solving the dictionary sequence dual-objective optimization integer programming model to realize joint optimization of worker allocation and multi-mode assembly tasks in an aircraft pulsation final assembly line under a resource exclusive policy: estimating resource-construction period mapping of each workstation, and determining initial resource allocation according to the estimated mapping; searching a scheduling scheme by adopting genetic manipulation; perfecting mapping by adopting a mapping updating strategy; after a predetermined evolution cycle, resource reallocation is performed to adjust and optimize resource allocation. Compared with the prior art, the method has the advantages of high scheduling result production efficiency, calculation overhead saving and the like.

The invention relates to the technical field of port scheduling, in particular to a port loading and unloading equipment dynamic regulation and control method and system based on a working road, and the method comprises the following steps: obtaining the real-time state data of port loading and unloading equipment in the working road, and obtaining the cooperative state index of the working road; deducing a task operation chain of the operation path in combination with an operation environment factor to obtain a dynamic time flow; obtaining an efficiency prediction index of the job path, and generating a resource allocation regulation and control strategy by constructing a global resource reallocation model; converting the resource allocation regulation and control strategy into an equipment regulation and control instruction set for optimization to obtain a final regulation and control instruction sequence; and executing the final regulation and control instruction sequence, obtaining a regulation and control effect evaluation result, correcting the global resource redistribution model, and realizing dynamic regulation and control of the port handling equipment. The active regulation and control of the port loading and unloading equipment are realized, and the operation efficiency, the adaptability and the intelligent level of the equipment are effectively improved.

The invention discloses a resource allocation method, a computer program product and electronic equipment. The method comprises the following steps: receiving an application data operation request, wherein the application data operation request carries a namespace identifier matched with a first application for triggering operation; determining a node list of a node database occupied by the application data of the first application in the first storage database cluster based on the namespace identifier; obtaining a node demand resource load of a node database in the node list in a second processing period; and determining a first node database from the second storage database cluster and migrating the application data of the first application to the first node database under the condition that the first application is determined to meet the resource redistribution condition based on the node demand resource load. The technical problem of low resource utilization rate caused by neglecting the resource use demand change condition of the application is solved.

The invention discloses a digital twin-driven intelligent construction site resource dynamic scheduling system and method, and belongs to the technical field of building construction management, and the system comprises a four-dimensional resource topology construction module which constructs a four-dimensional topological graph of construction resources based on UWB and RFID technologies; the mixed engine scheduling module adopts a mixed integer programming engine to generate a reference scheduling scheme in an initial stage, and adopts a reinforcement learning optimization engine to perform dynamic adjustment in an execution stage; the conflict prediction network module is used for establishing a conflict prediction model based on historical data and triggering a resource redistribution mechanism; the virtual interaction visualization module displays a scheduling scheme in a virtual reality environment and supports manual intervention, depth relation modeling, double-engine collaborative optimization and active conflict prevention of construction resources are achieved, the resource utilization rate is increased by 35% or above, the construction period is shortened by 15% or above, and conflict events are reduced by 50% or above.

The embodiment of the invention provides a real-time management and state monitoring method and system for optical division resources. The method comprises the following steps: acquiring an optical division resource state index of each channel in an optical fiber system; determining the optical division resource use state of each channel in the optical fiber system according to the optical division resource state index; performing optical division resource redistribution on the optical fiber system according to the optical division resource use state of each channel; acquiring a redistributed optical division resource state index and an optical division resource use prediction value of a next time period, and judging whether the optical fiber system subjected to optical division resource redistribution meets the optical division resource use requirement or not according to the redistributed optical division resource state index and the optical division resource use prediction value; and if not, carrying out optical distribution resource redistribution on the optical fiber system again. According to the invention, the technical problems of poor management flexibility and poor dynamic adjustment capability of the optical division resources in the optical fiber system are solved, and the technical effect of improving the use efficiency of the optical division resources while meeting the user requirements is achieved.

The present application provides a method and system for intelligent scheduling of heterogeneous resources of a large-model training and pushing all-in-one machine, which initializes the maintenance task list of each heterogeneous resource node in the training and pushing all-in-one machine, and generates a cost matrix for resource scheduling of maintenance tasks based on all maintenance task lists; the minimum cost characteristics of each heterogeneous resource node are screened out from the scheduling cost of the resource scheduling cost matrix, and then the slack variables of each heterogeneous resource node are determined when the target task has an allocation conflict; the scheduling load cost of the target task for each heterogeneous resource node is determined based on each minimum cost characteristic and the load characteristic of the target task; the target task is heterogeneously allocated resources using each scheduling load cost, and each slack variable is used to reallocate resources to the target task each time a task allocation conflict occurs in the heterogeneous resource allocation. Based on the above scheme, resource reallocation when a task allocation conflict occurs in resource scheduling can be achieved.

The invention belongs to the technical field of data processing, and particularly discloses a human resource allocation method and device for elevator maintenance, electronic equipment, a medium and a program product. Constructing and updating a task-personnel dependency graph comprising task nodes, personnel nodes, time sequence edges, cooperation edges and the like; spreading delay from an event source node to determine an affected task set, evaluating task impact weights based on parameters such as urgency and default cost, and extracting high-weight tasks; and taking an event source as a center, extracting a local optimization domain and freezing out-of-domain distribution in combination with a multi-dimensional threshold value, inputting intra-domain information into a large model to generate a candidate strategy, and finally screening an optimal scheme based on a constraint condition and a target function. According to the invention, efficient human resource reallocation under local minimum disturbance is realized, delay cost and default risk are reduced, and the utilization rate of human resources is improved.

The invention discloses a dynamic optimization method for research and development project resource allocation, which comprises the following steps of: acquiring task state data and resource state data in a project execution process, constructing multi-source data and transmitting the multi-source data to a data processing center in real time; performing real-time monitoring on the multi-source data to obtain key indexes and resource pool states of research and development projects; based on a preset triggering rule, judging whether a triggering condition of resource redistribution optimization is met or not, and when the triggering condition is met, constructing a multi-target dynamic resource optimization model; solving the optimization model, generating a resource allocation scheme, and performing multi-dimensional evaluation to determine a final scheme; and executing the final optimization scheme, updating the project resource allocation state, and continuously collecting execution effect data for optimization adjustment, the research and development project resource real-time monitoring and dynamic optimization are realized, and the resource utilization efficiency and the execution quality of the research and development project are improved.

The embodiment of the invention provides a memory resource allocation method, a network cloud platform, computing equipment, a computer readable storage medium and a computer program product. The memory resource allocation method comprises the following steps: acquiring a kernel loading address of a client operating system; determining a kernel version corresponding to the memory resource of the virtual machine based on the kernel loading address; querying target scanning ranges and target scanning parameters of a plurality of physical pages from a target database based on the kernel version; based on the target scanning range and the target scanning parameter, scanning the memory resource of the virtual machine, and determining a target physical page in an idle state in the plurality of physical pages; and based on the target physical page, memory resource reallocation is executed. According to the method and the device, intrusion to the interior of the user virtual machine is avoided, memory resource redistribution is completed under the condition that the user virtual machine is non-inductive, isolation and safety of the user virtual machine are guaranteed, and influence on the performance of the user virtual machine is avoided.

The embodiments of the present application relate to the technical field of computers. Provided are a distributed resource management method, apparatus and system, and a device and a nonvolatile readable storage medium. The method comprises: when a power-on instruction has been received, controlling a switch, a target device and a target calculation unit to synchronously power on; when a reset instruction has been received, executing a reset operation on a device to be reset that is indicated by the reset instruction, wherein the device to be reset comprises at least one of the target device, the target calculation unit and the switch; and performing resource scheduling on target resources in a plurality of resource pools on the basis of a resource scheduling request, wherein the resource scheduling comprises resource resetting and resource allocation. In this way, the distributed resource management system can reset the whole system or device, and supports resource resetting and resource reallocation during resource scheduling, thereby providing a more efficient and more flexible resource management architecture, realizing life cycle management of pooled resources, and improving the practicability and flexibility of resource management.

The application relates to a production scheduling and worker allocation joint optimization method for an aircraft pulsating assembly line, which comprises the following steps: acquiring assembly task data and resource data of the aircraft pulsating assembly line, wherein the resource is a worker; constructing a dictionary order double-target optimization integer programming model, taking minimization of a pulsating period and total resource consumption as a target function; solving the dictionary order double-target optimization integer programming model, realizing joint optimization of worker allocation and multi-mode assembly task in the aircraft pulsating assembly line under a resource exclusive policy; estimating a resource-work period mapping of each workstation, and determining an initial resource allocation according to the estimated mapping; searching a scheduling scheme by using genetic operation; improving the mapping by using a mapping updating strategy; after a predetermined evolution period, performing resource reallocation to adjust and optimize the resource allocation. Compared with the prior art, the application has the advantages of high production efficiency of scheduling results, saved calculation overhead and the like.

The application relates to the technical field of network communication, and discloses a network self-healing device based on node state data, which aims to improve the stability and self-healing ability of network communication. By monitoring the network state in real time, combining the extended Kalman filtering algorithm to perform state estimation and filtering, and combining the ant colony algorithm and the simulated annealing algorithm to perform fault prediction, path selection and resource allocation, the potential fault can be predicted and quickly recovered. The device comprises five modules of state monitoring, state estimation and filtering, fault prediction and prevention, fault recovery and resource reallocation, forms a closed-loop optimization mechanism, and is especially suitable for complex large network environments, such as data center networks.

The invention discloses a multi-disciplinary simulation task intelligent planning method based on a knowledge graph, and relates to the technical field of intelligent scheduling, and the method comprises the steps: constructing a preliminary knowledge graph comprising multi-disciplinary task description, resource limitation and execution dependence, analyzing historical task data through a pre-trained deep learning model, and obtaining a multi-disciplinary simulation task planning model; dynamically adjusting the preliminary knowledge graph through adaptive learning, and outputting an adaptive knowledge graph; carrying out resource redistribution optimization according to the updated task scheduling strategy, continuing to execute the task, starting monitoring, and collecting task progress and resource data as a new feedback data stream; the resource reallocation and the new feedback data stream are fed back to the adaptive knowledge graph, and multidisciplinary task description, resource limitation and execution dependency are updated through reasoning to generate a final optimization task execution sequence; the synchronous suppression of waiting time and resource contention is realized, the average utilization rate of resources and the time sequence stability are improved, and the adaptive adjustment capability of operation disturbance is formed.

The application provides a virtualized computing resource scheduling method and system based on a power wireless local area network. Firstly, power equipment operation load data of an access device in the power wireless local area network coverage is acquired, including real-time current fluctuation and other characteristics. Then, load characteristic extraction is performed on the power equipment operation load data to obtain a load characteristic set containing device power consumption fluctuation and other characteristics. Then, dynamic resource demand prediction is performed on the set based on a preset load prediction model to generate a virtualized resource demand prediction result containing a computing resource allocation level. A virtualized resource scheduling strategy containing an edge computing node resource allocation topology is generated according to the virtualized resource demand prediction result. Finally, virtualized computing resources are dynamically adjusted based on the virtualized resource scheduling strategy, resource reallocation is triggered, and a global resource state mapping table is updated to achieve effective scheduling of virtualized computing resources of the power wireless local area network.