Planning Cyclic Queuing and Forwarding for Time-Sensitive Networking via Heuristic and Neurodynamic Optimization

IF 6.7 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2025-03-11 DOI:10.1109/TNSE.2025.3550174

Jiasen Wang;Jun Wang

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引用次数: 0

Abstract

Time-sensitive networking (TSN) is a potential means to provide high-quality communication for cyber-physical systems in industrial automation and manufacturing. IEEE 802.1Qch cyclic queuing and forwarding (CQF) is a mechanism for transmitting time-sensitive packets in TSN systems. This paper addresses the planning of periodic time-sensitive flows for CQF in TSN systems. An integer programming problem is formulated to plan the periodic time-sensitive flows. In view of the NP-hardness of the integer programming problem, a polynomial-time heuristic algorithm is developed for CQF planning based on flow efficiency sorting and packet swapping to compute time offsets of multi-period flows. The heuristic algorithm is proven to converge to a CQF plan with a constant worst-case performance ratio with respect to the global optimal plan under given assumptions and derived sufficient conditions. Based on the heuristic solution, neurodynamic optimization is used to search for global optimal plans. Experimental and simulation results on open-loop network flow planning and closed-loop networked model predictive control are elaborated to substantiate the efficacy of the planning algorithms.

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基于启发式和神经动力学优化的时间敏感网络循环排队和转发规划

时间敏感网络（TSN）是一种为工业自动化和制造业中的信息物理系统提供高质量通信的潜在手段。IEEE 802.1Qch循环排队转发（CQF）是TSN系统中用于传输时间敏感报文的一种机制。本文研究了TSN系统中CQF周期时敏流的规划问题。提出了一个整数规划问题来规划周期时间敏感流。针对整数规划问题的np -硬度，提出了一种基于流效率排序和分组交换的多项式时间启发式算法，用于计算多周期流的时间偏移量。在给定的假设和推导的充分条件下，证明了启发式算法相对于全局最优方案收敛到具有恒定最坏性能比的CQF方案。在启发式解的基础上，采用神经动力学优化方法搜索全局最优方案。通过开环网络流规划和闭环网络模型预测控制的实验和仿真结果，验证了规划算法的有效性。

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.