2results about How to "Implement resource scheduling" patented technology

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 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.