Joint Request Offloading and Resource Allocation for Long-Term Utility Optimization in Collaborative Edge Inference With Time-Coupled Resources

IF 7.9 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2025-03-14 DOI:10.1109/TNSE.2025.3551148

Jiale Huang;Jigang Wu;Yalan Wu;Jiaxin Wu

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引用次数: 0

Abstract

Extensive research on edge inference has devoted in optimizing service performance for users. However, recent studies have overlooked the desired utility of application service provider (ASP), which is crucial for achieving long-term service provisioning. Besides, efficient request offloading and resource allocation are essential for optimizing long-term utility of ASP in dynamic networks with time-coupled resources. To address these issues, this paper formulates a long-term utility optimization problem in collaborative edge inference system. The objective is to maximize the long-term average utility of ASP, by jointly optimizing request offloading and resource allocation, under the time-coupled resource constraints. To solve the problem, a Lyapunov based online algorithm is proposed to decompose it into a series of one-slot deterministic problems by decoupling the time-coupled resource constraints. Only the current network states are required for one-slot problem. Then, the one-slot problem is converted into a master request offloading problem with an inner resource allocation problem. A distributed algorithm is proposed to derive the optimal decision to inner problem, while a coalition based algorithm is proposed to seek the stable solution to master problem. Experimental results show that, the proposed algorithm outperforms baseline algorithms for most cases, in terms of long-term average utility of ASP.

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时间耦合资源协同边缘推理中长期效用优化的联合请求卸载和资源分配

边缘推理在优化用户服务性能方面得到了广泛的研究。然而，最近的研究忽略了应用程序服务提供者（ASP）的预期效用，这对于实现长期服务供应至关重要。此外，在资源时间耦合的动态网络中，高效的请求卸载和资源分配是优化ASP长期效用的关键。针对这些问题，本文提出了协同边缘推理系统的长期效用优化问题。目标是在时间耦合的资源约束下，通过联合优化请求卸载和资源分配，使ASP的长期平均效用最大化。为了解决该问题，提出了一种基于Lyapunov的在线算法，通过解耦时间耦合资源约束，将其分解为一系列单槽确定性问题。单槽问题只需要当前的网络状态。然后，将单槽问题转化为带有内部资源分配问题的主请求卸载问题。提出了一种分布式算法来推导内部问题的最优决策，提出了一种基于联盟的算法来寻求主问题的稳定解。实验结果表明，就ASP的长期平均效用而言，该算法在大多数情况下优于基线算法。

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

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.