Dynamic Offloading for Multiuser Muti-CAP MEC Networks: A Deep Reinforcement Learning Approach

In this paper, we study a multiuser mobile edge computing (MEC) network, where tasks from users can be partially offloaded to multiple computational access points (CAPs). We consider practical cases where task characteristics and computational capability at the CAPs may be time-varying, thus, creating a dynamic offloading problem. To deal with this problem, we first formulate it as a Markov decision process (MDP), and then introduce the state and action spaces. We further design a novel offloading strategy based on the deep Q network (DQN), where the users can dynamically fine-tune the offloading proportion in order to ensure the system performance measured by the latency and energy consumption. Simulation results are finally presented to verify the advantages of the proposed DQN-based offloading strategy over conventional ones.

Automated summary

In this paper, a multiuser mobile edge computing network is studied where tasks can be partially offloaded to multiple computational access points. A novel offloading strategy based on DQN is proposed, allowing users to dynamically fine-tune the offloading proportion to optimize system performance. Simulation results demonstrate the advantages of the proposed DQN-based offloading strategy over conventional methods.