移动边缘计算在线计算卸载的深度强化学习方法

Yameng Zhang, Tong Liu, Yanmin Zhu, Yuanyuan Yang
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引用次数: 9

摘要

随着移动智能设备的爆炸式增长,出现了许多计算密集型应用,如交互式游戏和增强现实。移动边缘计算作为云计算的延伸,是为了满足应用的低延迟需求而提出的。在本文中,我们考虑一个建立在具有众多基站的超密集网络中的边缘计算系统,在网络中移动的智能设备上依次生成异构计算任务。设备用户需要一个最优的任务卸载策略,以及最优的CPU频率和传输功率调度,以最大限度地减少任务完成延迟和能耗。然而,由于计算任务的随机性和网络条件的动态性,使得该问题特别难以解决。受强化学习的启发,我们将问题转化为马尔可夫决策过程。然后,我们提出了一种基于双深度Q网络的在线卸载方法,其中还提供了一个特定的神经网络模型来估计每个动作所获得的累积奖励。我们还进行了大量的模拟,以比较我们提出的方法与基线的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Deep Reinforcement Learning Approach for Online Computation Offloading in Mobile Edge Computing
With the explosion of mobile smart devices, many computation intensive applications have emerged, such as interactive gaming and augmented reality. Mobile edge computing is put forward, as an extension of cloud computing, to meet the low-latency requirements of the applications. In this paper, we consider an edge computing system built in an ultra-dense network with numerous base stations, and heterogeneous computation tasks are successively generated on a smart device moving in the network. An optimal task offloading strategy, as well as optimal CPU frequency and transmit power scheduling, is desired by the device user, to minimize both task completion latency and energy consumption in a long-term. However, due to the stochastic computation tasks and dynamic network conditions, the problem is particularly difficult to solve. Inspired by reinforcement learning, we transform the problem into a Markov decision process. Then, we propose an online offloading approach based on a double deep Q network, in which a specific neural network model is also provided to estimate the cumulative reward achieved by each action. We also conduct extensive simulations to compare the performance of our proposed approach with baselines.
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