Multi-user Computing Offloading Based on Deep Reinforcement Learning

Abstract

With the rise of mobile edge computing, how to deal with the problem of edge computing task offloading has become one of the research hotspots. In order to solve the problem of serious congestion on wireless communication link caused by multi-users unloading to MEC server at the same time and competition for server computing resources among multi-user tasks after unloading, a joint optimization method for offloading decision and resource allocation was proposed. In this paper, a system task offloading model based on OFDMA technology is proposed, which takes into account the intensive and indivisible task resources generated by each user device. On this basis, a dynamic task offloading and resource allocation algorithm based on Nature DQN is proposed to solve the multi-client optimal offloading decision and multi-client computing resource allocation scheme. Finally, the simulation results show that the proposed task offloading model and the computational offloading algorithm based on Nature DQN are effective in optimizing the total delay of the long-term system.