4results about How to "Implement resource scheduling" patented technology

The invention provides a resource scheduling method and device, equipment, a storage medium and a computer program product, and relates to the technical field of communication, the method comprises the following steps: in response to a task unloading request sent by terminal equipment, issuing a to-be-unloaded task carried by the task unloading request to adjacent MEC equipment; receiving an evaluation result of the to-be-unloaded task sent by the adjacent MEC equipment; the evaluation result is used for representing whether the adjacent MEC equipment has the capability of processing the to-be-unloaded task or not; and determining at least one target MEC device according to the energy consumption information and the time delay information of the MEC device under the condition of determining that the to-be-unloaded task needs to be migrated based on the evaluation result, and scheduling the to-be-unloaded task to the target MEC device. According to the invention, the task does not need to be unloaded to the edge device from the cloud center node, the local edge device receives the task request, distributes the calculation strategy and autonomously completes resource allocation, and decentralized resource scheduling is realized.

This application discloses a resource scheduling method, apparatus, electronic device, and medium, belonging to the field of network resource scheduling technology. The method includes: acquiring energy consumption data, latency data, and load data from multiple servers, wherein the first server includes a cloud server and an edge server; inputting the energy consumption data, latency data, and load data into a preset resource scheduling model, and outputting multiple system data of the resource scheduling system composed of the multiple servers; inputting the multiple system data into a preset deep learning model, and outputting response data corresponding to the resource scheduling system to represent the network environment's response to the state; the deep learning model is a model combining a time-recurrent neural network and an attention mechanism; and scheduling the network resources of the multiple servers based on the response data. The embodiments of this application employ a resource scheduling model and a deep learning model to improve resource scheduling efficiency.

The invention provides a satellite cloud-oriented resource virtualization management system, which comprises a mixed granularity virtualization container, a management and monitoring module and a satellite visualization simulation module, the mixed granularity virtualization container comprises a system-level virtualization container and a container-level virtualization container; the management and monitoring module is used for carrying out full-life-cycle management and resource state monitoring on the system-level virtualization container and the container-level virtualization container; and the satellite visual simulation module is arranged in the container and is used for realizing visual simulation of the satellite according to the relevant parameters of the satellite. The system has container-level and system-level virtualization functions, supports full-life-cycle management and resource state monitoring of containers, realizes standardized packaging, deployment and centralized management of services, provides quick construction of mirror image templates, and carries out fine management and control on various information of mirror images; a visual simulation function is provided for an on-orbit satellite, and the position information of the satellite in a required time period is simulated and observed.

The invention relates to the technical field of computers, in particular to a computing power distribution method and device based on a heterogeneous graphics processor. The method comprises the steps of obtaining a system performance state quantity of a target computing cluster and computing power demand parameters of multiple tasks; the target computing cluster comprises a plurality of graphics processors, the graphics processors are input into the trained computing power distribution model to generate a corresponding computing power distribution strategy, and then the computing power distribution strategy is verified according to the actually monitored actual resource utilization rate of the target computing cluster and a preset distribution constraint condition; therefore, the reliability of the scheduling strategy can be effectively ensured while the dynamic resource scheduling of the graphics processor is realized.