Graph Reinforcement Learning Based Multi-edge Cooperative Load Balancing Method

Abstract

In mobile edge computing,devices can effectively relieve latency and energy consumption by offloading computation-intensive tasks to nearby edge servers.In order to improve the quality of service,edge servers need to collaborate with each other rather than working alone.For the load balancing problem of multi-edge collaboration,the existing solutions often depend on accurate mathematical models or make fair use of edge topological relationships.To solve this problem,an offloading decision-ma-king method based on graph reinforcement learning is proposed in this paper.Firstly,the load balancing scenario with multi-edge collaboration is abstracted as graph data,then a graph embedding process based on graph convolutional neural network is used to extract the information features of the graph,for assisting the deep Q-network to make offloading decisions,and finally the objective load balancing plan is found through a centralized feedback-control mechanism.Simulation experiments are conducted in multiple scenarios,the results verify the effectiveness of the proposed method in shortening the average response latency of the tasks,and the load balancing effect which is better than the comparison algorithms and close to the ideal plan can be obtained in a short period of time.