Computation Off-Loading in Resource-Constrained Edge Computing Systems Based on Deep Reinforcement Learning

Abstract

Edge computing is a computational paradigm that brings resources closer to the network edge, such as base stations or gateways, in order to provide quick and efficient computing services for mobile devices while relieving pressure on the core network. However, the current computing power of edge servers are insufficient to handle the high number of tasks generated by access devices. Additionally, some mobile devices may not fully utilize their computing resources. To maximize the use of resources, we propose a novel edge computing system architecture consisting of a resource-constrained edge server and three computing groups. Tasks from each group can be offloaded to either the edge server or the corresponding computing group for execution. We focus on optimizing the computation offloading of devices to minimize the maximum overall task processing latency in the system. This problem is proved to be NP-hard. To solve it, we propose a DQN-based resource utilization task scheduling (DQNRTS) algorithm that has two desirable characteristics: 1) it effectively utilizes the computing resources in the system and 2) it uses deep reinforcement learning to make intelligent scheduling decisions based on system state information. Experimental results demonstrate that the DQNRTS algorithm is capable of reducing the processing latency of the system by converging to optimal solutions.