Learn to Offload in Mobile Edge Computing

Ting Liu, Zhaowei Zhu, Junrong Gu, Xiliang Luo

Abstract: Computation offloading is a promising technology in mobile edge computing (MEC) systems. In this paper, we take into account the system dynamics and the user mobility and formulate the mobile computation offloading as a stochastic optimal control problem. On the one hand, when the system information is fully known, we derive the optimal offloading policy. On the other hand, in the case of limited system information, we design a Q-learning algorithm which also gives optimal system performance yet with a slower converge rate. To speed up the convergence and deal with a more complex system, we further develop one more algorithm based on the deep-Q-network (DQN). Simulation results show our proposed DQN-based algorithm indeed converges at a much faster rate.