期刊
IEEE TRANSACTIONS ON MOBILE COMPUTING
卷 21, 期 6, 页码 1985-1997出版社
IEEE COMPUTER SOC
DOI: 10.1109/TMC.2020.3036871
关键词
Task analysis; Mobile handsets; Delays; Heuristic algorithms; Mobile computing; Edge computing; Distributed algorithms; Mobile edge computing; computation offloading; resource allocation; deep reinforcement learning; deep Q-learning
资金
- Natural Sciences and Engineering Research Council of Canada (NSERC)
In this paper, a model-free deep reinforcement learning-based distributed algorithm is proposed to address the load problem in mobile edge computing systems. The algorithm can effectively reduce the ratio of dropped tasks and average delay.
In mobile edge computing systems, an edge node may have a high load when a large number of mobile devices offload their tasks to it. Those offloaded tasks may experience large processing delay or even be dropped when their deadlines expire. Due to the uncertain load dynamics at the edge nodes, it is challenging for each device to determine its offloading decision (i.e., whether to offload or not, and which edge node it should offload its task to) in a decentralized manner. In this work, we consider non-divisible and delay-sensitive tasks as well as edge load dynamics, and formulate a task offloading problem to minimize the expected long-term cost. We propose a model-free deep reinforcement learning-based distributed algorithm, where each device can determine its offloading decision without knowing the task models and offloading decision of other devices. To improve the estimation of the long-term cost in the algorithm, we incorporate the long short-term memory (LSTM), dueling deep Q-network (DQN), and double-DQN techniques. Simulation results show that our proposed algorithm can better exploit the processing capacities of the edge nodes and significantly reduce the ratio of dropped tasks and average delay when compared with several existing algorithms.
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