Intelligent Task Offloading Method using Deep Q-Network for Collaborative Edge Computing System

Recently, various applications using artificial intelligence (AI) are deployed in edge network. In particular, An intelligence applications demanded with high computation and low end-to-end latency are executed on edge computing environments. Thus, in this paper, for the optimization of the resource of edge servers in multi-edge network environments, we propose the intelligent task offloading method based on Deep Q-network that can optimize computation capability of the multi-edge computing environments. For this, first at all, we formulate the problem of multi-edge computing allocation with a Markov decision process and propose the policy for allocating edge resource adopting a deep reinforcement learning algorithm. In the simulation, the results show the proposed method gets a better performance in terms of the end-to-end latency of the offloaded task than the existing methods.