[1]王宇轩,鲍海洲*,喻国荣,等.基于PER-MATD3的任务卸载和资源优化方法[J].计算机技术与发展,2024,34(12):57-65.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0254]
　WANG Yu-xuan,BAO Hai-zhou*,YU Guo-rong,et al.Task Offloading and Resource Optimization Method Based on PER-MATD3[J].,2024,34(12):57-65.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0254]
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基于PER-MATD3的任务卸载和资源优化方法()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
34
期数:
2024年12期
页码:
57-65
栏目:
移动与物联网络
出版日期:
2024-12-10

文章信息/Info

Title:
Task Offloading and Resource Optimization Method Based on PER-MATD3
文章编号:
1673-629X(2024)12-0057-09
作者:
王宇轩1; 2; 3; 鲍海洲1; 2; 3*; 喻国荣1; 2; 3; 聂雷1; 2; 3; 陈民浩1; 2; 3
1. 武汉科技大学 计算机科学与技术学院,湖北 武汉 430065;2. 智能信息处理与实时工业系统湖北省重点实验室,湖北 武汉 430065;3. 大数据科学与工程研究院,湖北 武汉 430065
Author(s):
WANG Yu-xuan1; 2; 3; BAO Hai-zhou1; 2; 3*; YU Guo-rong1; 2; 3; NIE Lei1; 2; 3; CHEN Min-hao1; 2; 3
1. School of Computer Science and Technology,Wuhan University of Science and Technology,Wuhan 430065,China;2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System,Wuhan 430065,China;3. Institute of Big Data Science and Engineering,Wuhan 430065,China
关键词:
移动边缘计算; 任务卸载; 资源优化; 深度强化学习; 优先经验回放
Keywords:
mobile edge computing; task offloading; resource allocation; deep reinforcement learning; prioritized experience replay
分类号:
TP393
DOI:
10.20165/j.cnki.ISSN1673-629X.2024.0254
摘要:
移动边缘计算(MEC)通过在网络边缘设立边缘服务器,为终端用户设备就近提供更加充足的计算与存储资源,满足了新兴应用的时延与能耗需求。 该文研究了移动边缘计算中多用户的任务卸载策略、计算能力与功率分配的联合优化,该问题为混合整数非线性规划问题。 该文将其表示为一个多智能体马尔可夫决策过程(MAMDP)以最小化系统成本。为了解决该问题,提出一个结合优先经验回放的多智能体双延迟深度确定性策略梯度(PER-MATD3)的算法,该算法缓解了值函数的高估问题,增加了学习效率。 此外,设计了一个卸载动作生成算法将输出的连续动作重构转化为离散卸载动作,并保证了卸载动作的有效性。 仿真实验结果表明,PER-MATD3 算法在训练过程中收敛速度与收敛的奖励值都优于其他基准算法。 该方法在移动边缘计算的任务卸载与资源分配问题中能有效地降低总成本与时延,同时还能保持非常低的任务失败率。
Abstract:
By setting up edge servers at the edge of the network,Mobile Edge Computing ( MEC) provides sufficient computing and storage resources for end-user devices nearby and meets the delay and energy consumption requirements of emerging applications. We study the joint optimization of task offloading strategy, computing capacity, and power allocation for multi - users in mobile edge computing,a mixed integer nonlinear programming problem. We formulate it as a Multi-agent Markov Decision Process (MAMDP) to minimize the system cost. To solve this problem,we propose a multi - agent twin delay deep deterministic policy gradient algorithm combined with prioritized experience replay ( PER- MATD3 ),which alleviates the overestimation problem of the value function and increases the learning efficiency. In addition,an offloading action generation algorithm was designed to transform the output continuous action reconstruction into discrete offloading action, and the effectiveness of the offloading action was guaranteed. Simulation experimental results show that the PER-MATD3 algorithm is superior to other benchmark algorithms in the convergence speed and the reward value of convergence in the training process. The proposed method can effectively reduce the total cost and delay in the task off-loading and resource allocation problem of mobile edge computing while maintaining a very low task failure rate.

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更新日期/Last Update: 2024-12-10