Computation Offloading Method Using Stochastic Games for Software-Defined-Network-Based Multiagent Mobile Edge Computing

Abstract

In the scenario of Industry 4.0, mobile smart devices (SDs) on production lines have to process massive amounts of data. These computing tasks sometimes far exceed the computing capability of SDs and require lots of energy and time to process. How to effectively reduce energy consumption and latency is necessary to be solved. To this end, we first propose an SDN (Software Defined Network)-based MEC (Mobile Edge Computing) system. In the MEC system, SDs can offload computation tasks to edge servers to decrease the processing latency and avoid the waste of energy. At the same time, taking advantage of SDNs programmability, scalability, and isolation of the control plane and the data plane, an SDN controller can manage edge devices within the MEC system. Secondly, based on a stochastic game, we study the computation offloading and resource allocation problems in the MEC system and establish a stochastic game-based computation offloading model. Furthermore, we prove that the multi-user stochastic game in this system can achieve Nash Equilibrium. We further consider each SD as an independent agent and design a stochastic game-based resource allocation algorithm with prioritized experience replays (SGRA-PER) to minimize energy consumption and processing latency with Multi-Agent Reinforcement Learning. Experiment results demonstrate that the proposed SGRA-PER is superior to MADDPG, Q-Mix, and MAPPO algorithms, which can significantly reduce the processing delay and energy consumption with dynamic resource allocation. Moreover, SGRA-PER can still keep a higher performance under the increase of SDs, which can be applied in a large-scale MEC system.