论文标题
在移动边缘计算系统中的任务卸载的深度强化学习
Deep Reinforcement Learning for Task Offloading in Mobile Edge Computing Systems
论文作者
论文摘要
在移动边缘计算系统中，当大量移动设备卸载其任务时，边缘节点可能会具有高负载。那些卸载的任务可能会遇到大量处理延迟，甚至在截止日期到期时被删除。由于边缘节点处的负载动态不确定，因此每个设备都以分散的方式确定其卸载决策（即是否卸载以及哪个边缘节点应将其任务卸载）都具有挑战性。在这项工作中，我们考虑不可分割和延迟敏感的任务以及边缘负载动态，并制定任务卸载问题，以最大程度地减少预期的长期成本。我们提出了一个基于无模型的深入学习的分布式算法，每个设备都可以在不知道任务模型和其他设备的决策的情况下确定其卸载决策。为了提高算法中长期成本的估计，我们结合了长期的短期记忆（LSTM），对决深Q-网络（DQN）和双DQN技术。使用50个移动设备和5个边缘节点的仿真结果表明，与几种现有算法相比，所提出的算法分别可以将丢弃任务和平均任务延迟的比率降低86.4％-95.4％和18.0％-30.1％。
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 with 50 mobile devices and five edge nodes show that the proposed algorithm can reduce the ratio of dropped tasks and average task delay by 86.4%-95.4% and 18.0%-30.1%, respectively, when compared with several existing algorithms.
