Distributed Task Offloading and Resource Allocation for Latency Minimization in Mobile Edge Computing Networks

IF 7.7 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Transactions on Mobile Computing Pub Date : 2024-09-10 DOI:10.1109/TMC.2024.3458185
Minwoo Kim;Jonggyu Jang;Youngchol Choi;Hyun Jong Yang
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Abstract

The growth in artificial intelligence (AI) technology has attracted substantial interests in latency-aware task offloading of mobile edge computing (MEC)—namely, minimizing service latency. Additionally, the use of MEC systems poses an additional problem arising from limited battery resources of MDs. This paper tackles the pressing challenge of latency-aware distributed task offloading optimization, where user association (UA), resource allocation (RA), full-task offloading, and battery of mobile devices (MDs) are jointly considered. In existing studies, joint optimization of overall task offloading and UA is seldom considered due to the complexity of combinatorial optimization problems, and in cases where it is considered, linear objective functions such as power consumption are adopted. Revolutionizing the realm of MEC, our objective includes all major components contributing to users’ quality of experience, including latency and energy consumption. To achieve this, we first formulate an NP-hard combinatorial problem, where the objective function comprises three elements: communication latency, computation latency, and battery usage. We derive a closed-form RA solution of the problem; next, we provide a distributed pricing-based UA solution. We simulate the proposed algorithm for various resource-intensive tasks. Our numerical results show that the proposed method Pareto-dominates baseline methods. More specifically, the results demonstrate that the proposed method can outperform baseline methods by 1.62 times shorter latency with 41.2% less energy consumption .
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移动边缘计算网络中的分布式任务卸载和资源分配以实现延迟最小化
随着人工智能(AI)技术的发展,人们对移动边缘计算(MEC)的延迟感知任务卸载(即最大限度地减少服务延迟)产生了浓厚的兴趣。此外,由于 MD 的电池资源有限,MEC 系统的使用还带来了一个额外的问题。本文将共同考虑用户关联(UA)、资源分配(RA)、全任务卸载和移动设备(MDs)电池等问题,以应对延迟感知分布式任务卸载优化这一紧迫挑战。在现有研究中,由于组合优化问题的复杂性,很少考虑整体任务卸载和用户关联的联合优化,即使考虑了,也是采用线性目标函数,如功耗。我们的目标包括影响用户体验质量的所有主要因素,包括时延和能耗,这是 MEC 领域的一次革命。为此,我们首先提出了一个 NP 难度的组合问题,其中目标函数包括三个要素:通信延迟、计算延迟和电池使用。我们推导出了该问题的闭式 RA 解决方案;接下来,我们提供了一种基于分布式定价的 UA 解决方案。我们针对各种资源密集型任务模拟了所提出的算法。我们的数值结果表明,所提出的方法在帕累托优势上优于基准方法。更具体地说,结果表明所提出的方法比基准方法的延迟时间短 1.62 倍,能耗低 41.2%。
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来源期刊
IEEE Transactions on Mobile Computing
IEEE Transactions on Mobile Computing 工程技术-电信学
CiteScore
12.90
自引率
2.50%
发文量
403
审稿时长
6.6 months
期刊介绍: IEEE Transactions on Mobile Computing addresses key technical issues related to various aspects of mobile computing. This includes (a) architectures, (b) support services, (c) algorithm/protocol design and analysis, (d) mobile environments, (e) mobile communication systems, (f) applications, and (g) emerging technologies. Topics of interest span a wide range, covering aspects like mobile networks and hosts, mobility management, multimedia, operating system support, power management, online and mobile environments, security, scalability, reliability, and emerging technologies such as wearable computers, body area networks, and wireless sensor networks. The journal serves as a comprehensive platform for advancements in mobile computing research.
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