Revenue-Oriented Optimal Service Offloading Based on Fog-Cloud Collaboration in SD-WAN Enabled Manufacturing Networks

IF 6.7 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY IEEE Transactions on Network Science and Engineering Pub Date : 2025-01-15 DOI:10.1109/TNSE.2025.3526750

Xu Chen;Yi Zhang;Chunxiao Jiang;Changqiao Xu;Zhenhui Yuan;Gabriel-Miro Muntean

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Abstract

The software-defined wide area network (SD-WAN) is considered one of the most promising paradigms for next generation manufacturing networks. However, SD-WAN users usually suffer from significant delays due to remotely deployed cloud centers. The requirements of delay-sensitive business services make optimal resource allocation methods very important. In this paper, we propose a revenue-oriented service offloading method to improve the efficiency of SD-WAN enabled manufacturing networks through fog-cloud collaboration. To maximize the service revenue, we formulate a coupled combinatorial optimization model to allocate computation and communication resources jointly between the fog node and the cloud. To solve this problem, we propose a service offloading method based on the counterfactual regret minimization (CFR) principle according to the dynamic workload state of the fog nodes. This method reduces the time complexity of problem-solving from exponential to polynomial, and achieves good performance that is very close to the optimal solution in terms of service efficiency. The outstanding contribution of this paper is to unify the multi-objective problem to the revenue scale for optimization to improve the overall service revenue of the SD-WAN. The simulation results show that our method outperforms benchmark methods in terms of both effectiveness and efficiency.

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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.