基于DDPG的边缘计算任务卸载和服务缓存算法

doi:10.19678/j.issn.1000-3428.0061371

摘要/Abstract

摘要： 当计算任务被转移到移动边缘计算（MEC）服务器上时，通过服务缓存能够降低获取和初始化服务应用程序的实时时延和带宽成本。此外，体验质量是驱动卸载决策的关键因素，有效利用有限的计算资源能够提升用户满意度。考虑一个边缘服务器帮助移动用户执行一系列计算任务的场景，建立混合整数非线性规划问题，提出一种基于深度确定性策略梯度（DDPG）的算法来联合优化服务缓存位置、计算卸载决策和资源分配，从而提高用户对服务的体验质量，最大化用户使用计算资源所节约的成本。仿真结果表明，该算法在提高用户体验质量和节约成本方面较使用无缓存策略、随机选择策略和无缓存随机选择策略的算法性能更优。

关键词: 移动边缘计算, 深度强化学习, 任务卸载, 服务缓存, 资源分配

Abstract: When computing tasks are transferred to the Mobile Edge Computing(MEC) servers, the service cache effectively reduces the real-time delay and bandwidth cost of acquiring and initializing the service application.In addition, Quality of Experience(QoE) is a key factor driving offload decisions, and effective utilization of limited computing resources can keep users satisfied.This paper considers a scenario where a single edge server is used to help mobile users perform a series of computing tasks.On this basis, a Mixed Integer Nonlinear Programming(MINLP) is established, and a Deep Deterministic Policy Gradient(DDPG) algorithm is proposed to jointly optimize the service cache location, the offload decision and the resource allocation, so as to improve the user's QoE of services and maximize the cost saved by users using computing resources.Simulation results show that the proposed method achieves higher QoE and lower cost than the algorithms using non-cache strategy, random-choice strategy and non-cache random-choice strategy.

Key words: Mobile Edge Computing (MEC), deep reinforcement learning, task offloading, service caching, resource allocation

中图分类号:

TP391

陈清林, 邝祝芳. 基于DDPG的边缘计算任务卸载和服务缓存算法[J]. 计算机工程, 2021, 47(10): 26-33.

CHEN Qinglin, KUANG Zhufang. Task Offloading and Service Caching Algorithm Based on DDPG in Edge Computing[J]. Computer Engineering, 2021, 47(10): 26-33.

http://www.ecice06.com/CN/Y2021/V47/I10/26

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