Task Offloading for Multi-Gateway-Assisted Mobile Edge Computing Based on Deep Reinforcement Learning

Abstract

An effective task offloading strategy in the mobile edge computing enables terminals to migrate their tasks to the edge server, accelerating the execution of terminal tasks. However, most researches on task offloading are limited to the single edge server, while in practice, it is difficult for a single edge server to support joint offloading requests from multiple terminals. Edge gateways can be flexibly deployed around terminal devices to further reduce the computing load of edge servers. Therefore, we jointly study the task offloading problem in the multi-gateway-assisted mobile edge computing scenario. Constrained by discrete environmental variables, the offloading process jointly optimizes user scheduling, task offloading rate, and gateway resource allocation, with evaluation indexes defined by the average task delay and energy consumption. Aiming at minimizing the long-term cost of the whole system, we design a deep reinforcement learning algorithm with dynamically adjusted offloading strategies and allocated resources with only the partial state information. The simulation results demonstrate that the algorithm can obtain the optimal computation offloading policy in an uncontrollable dynamic environment. Compared with the other four benchmark algorithms, it has better system cost performance, and can quickly converge to the optimum. Meanwhile, In order to ensure the relative load balance on multiple gateways, we design a low-complexity balanced offloading strategy among multiple gateways and verify its performance.