An Adaptive AQM Based on the Consecutive Change Detection in the Programmable Queue

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

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-02 DOI:10.1109/TNSE.2025.3557164

Xinyue Jiang;Dezhang Kong;Xiang Chen;Shuangxi Chen;Haifeng Zhou;Chunming Wu;Xuan Liu;Wei Ruan

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引用次数: 0

Abstract

With the rapid expansion of the Internet, the surge in data traffic, propelled by the exponential growth of network services and users, has heightened the risk of network congestion, security breaches, and system instability. Addressing these challenges presents stringent demands and novel complexities in queue management. However, prevailing solutions often rely heavily on average queue size thresholds while ignoring traffic variations. In this paper, CCD-AQM, an Adaptive Queue Management approach based on the Consecutive Change trend Detection in the queue size is proposed. Considering that today's programmable data plane offers promising ways for finer analysis of the queue in the hardware switches, we implement CCD-AQM on an RMT switch and analyze its resource usage. Large-scale simulations are conducted to evaluate CCD-AQM, showcasing its superior performance in queue management. The results demonstrate its ability to maintain low queue occupancy and high throughput while ensuring fairness among competing flows.

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基于可编程队列连续变化检测的自适应AQM

随着互联网的迅速发展，在网络服务和用户呈指数级增长的推动下，数据流量激增，增加了网络拥塞、安全漏洞和系统不稳定的风险。解决这些挑战在队列管理中提出了严格的要求和新的复杂性。然而，主流的解决方案通常严重依赖于平均队列大小阈值，而忽略了流量变化。本文提出了一种基于队列大小连续变化趋势检测的自适应队列管理方法CCD-AQM。考虑到当今的可编程数据平面为硬件交换机中的队列提供了更精细的分析方法，我们在RMT交换机上实现了CCD-AQM并分析了其资源使用情况。通过大规模仿真对CCD-AQM算法进行了评价，证明了其在队列管理方面的优越性。结果表明，它能够在保证竞争流之间的公平性的同时保持低队列占用率和高吞吐量。

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.