A Clustering Offloading Decision Method for Edge Computing Tasks Based on Deep Reinforcement Learning

In many IoT scenarios, the resources of terminal devices are limited, and it is difficult to provide services with low latency and low energy consumption. Mobile edge computing is an effective solution by offloading computing tasks to edge server processing. There are some problems in the existing offloading decision algorithms: the offloading decision method based on heuristic algorithms cannot dynamically adjust the policy in the changing environment; the offloading algorithm based on deep reinforcement learning will lead to slow convergence and poor exploration effect due to the problem of dimension explosion. To solve the above problems, this paper designs an offloading decision algorithm to make dynamic decisions in a mobile edge computing network with multi-device access. The algorithm comprehensively considers the energy consumption of terminal equipment, offloading overhead, average delay and success rate of task completion, aiming to achieve the highest total revenue of the whole system in a period of time. In this work, the online offloading problem is abstracted as a Markov decision process. Based on the Double Dueling Deep Q-Network (D3QN) algorithm, the offloading decision is designed to adapt to the highly dynamic environment of the edge computing network and solve the problem of high state space complexity. In addition, this paper innovatively introduces a clustering algorithm into deep reinforcement learning (DRL) to preprocess the action space and solve the explosion problem of the action space dimension caused by the increase of terminal devices. The experimental results show that the proposed algorithm is superior to the baseline strategies such as Deep Q-Network (DQN) algorithm in convergence speed and total reward.