Workload-based adaptive decision-making for edge server layout with deep reinforcement learning

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-22 DOI:10.1016/j.engappai.2024.109662

Shihua Li , Yanjie Zhou , Bing Zhou , Zongmin Wang

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引用次数: 0

Abstract

Mobile edge computing (MEC) is crucial in applications such as intelligent transportation, innovative healthcare, and smart cities. By deploying servers with computing and storage capabilities at the network edge, MEC enables low-latency services close to end users. However, the configuration of edge servers needs to meet the low-latency requirements and effectively balance the servers’ workloads. This paper proposes an adaptive layout and dynamic optimization method, modeling the edge server layout problem as a Markov decision process. It introduces a workload-based server placement rule that adjusts the locations of edge servers according to the load of base stations, enabling the learning of low-latency and load-balanced server layout strategies. Experimental validation on a real dataset from Shanghai Telecom shows that the proposed algorithm improves average latency performance by about 40% compared to existing technologies, and enhances workload balancing performance by about 17%.

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利用深度强化学习为边缘服务器布局制定基于工作量的自适应决策

移动边缘计算（MEC）在智能交通、创新医疗和智能城市等应用中至关重要。通过在网络边缘部署具有计算和存储功能的服务器，MEC 可在终端用户附近提供低延迟服务。然而，边缘服务器的配置需要满足低延迟要求，并有效平衡服务器的工作负载。本文提出了一种自适应布局和动态优化方法，将边缘服务器布局问题建模为马尔可夫决策过程。它引入了基于工作负载的服务器布局规则，可根据基站负载调整边缘服务器的位置，从而学习低延迟和负载平衡的服务器布局策略。在上海电信的真实数据集上进行的实验验证表明，与现有技术相比，该算法的平均延迟性能提高了约 40%，工作负载平衡性能提高了约 17%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Engineering Applications of Artificial Intelligence 工程技术-工程：电子与电气

CiteScore

9.60

自引率

10.00%

发文量

505

审稿时长

68 days

期刊介绍： Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.