This paper aims to propose a grouping framework for benchmarking the active queue management (AQM) methods of network congestion control based on multicriteria decision-making (MCDM) techniques to assist developers of AQM methods in selecting the best AQM method. Given the current rapid development of the AQM techniques, determining which of these algorithms is better than the other is difficult because each algorithm performs better in a specific metric(s). Current benchmarking studies benchmark the AQM methods from a single incomplete prospective. In each proposed AQM method, the benchmarking was achieved with reference to some evaluation measures that are relatively close to the desired goal being followed during the development of the AQM methods. Furthermore, the benchmarking frameworks of AQM methods are complicated and challenging because of the following reasons: (1) the technical details of the AQM methods are adapted and the input parameters are selected according to the sensitivity of the AQM methods; and (2) a framework is developed and designed for simulating AQM methods, the simulated network and the collected results. For this purpose, a set of criteria for AQM comparison are determined. These criteria are performance, processing overhead and configuration. The benchmarking framework is developed based on the crossover of three groups of multi-evaluation criteria and several AQM methods as a proof of concept. The AQM families that are implemented and utilized in experiments to generate the data that are used as a proof of concept of our proposed framework are the parameter-based (pars) and fuzzy-based AQM methods. Accordingly, constructing the decision matrix (DM) that will be used to generate the final results is necessary. Subsequently, the underlying AQM methods are benchmarked and ranked using MCDM techniques, namely, integrated analytical hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS). The validation was performed objectively. The mean±standard deviation was computed to ensure that the AQM methods ranking undergo systematic ranking. Results illustrate that (1) the integration of AHP and TOPSIS solves the AQM method benchmarking problems; (2) results of the individual TOPSIS context clearly show variances among the ranking results of the six experts; (3) the ranks of the AQM methods obtained from internal and external TOPSIS group decision-making are nearly similar, with random early detection method being ranked as the best one; and (4) in the objective validation, significant differences were found between the groups’ scores, thereby indicating that the ranking results of internal and external TOPSIS group decision-making were valid.

中文翻译：

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基于AHP和Group-TOPSIS的网络拥塞控制AQM多维基准测试框架

本文旨在提出一种分组框架，用于对基于多准则决策 (MCDM) 技术的网络拥塞控制的主动队列管理 (AQM) 方法进行基准测试，以帮助 AQM 方法的开发人员选择最佳 AQM 方法。鉴于当前 AQM 技术的快速发展，确定这些算法中的哪一个比另一个更好是困难的，因为每个算法在特定指标上的表现都更好。当前的基准研究从一个不完整的前瞻性对 AQM 方法进行了基准测试。在每种提出的 AQM 方法中，基准测试都是参考一些与 AQM 方法开发过程中所遵循的预期目标相对接近的评估措施来实现的。此外，由于以下原因，AQM 方法的基准测试框架复杂且具有挑战性：（1）适应 AQM 方法的技术细节，并根据 AQM 方法的敏感性选择输入参数；(2) 开发和设计用于模拟 AQM 方法、模拟网络和收集结果的框架。为此，确定了一组用于 AQM 比较的标准。这些标准是性能、处理开销和配置。基准测试框架是基于三组多评估标准和几种 AQM 方法的交叉开发的，作为概念证明。在实验中实现和使用的 AQM 系列是基于参数（pars）和基于模糊的 AQM 方法，以生成用作我们提出框架的概念证明的数据。因此，构建将用于生成最终结果的决策矩阵 (DM) 是必要的。随后，使用 MCDM 技术对基础 AQM 方法进行基准测试和排序，即集成层次分析法 (AHP) 和通过与理想解决方案相似度排序的技术 (TOPSIS)。验证是客观进行的。这 集成层次分析法 (AHP) 和通过与理想解相似度排序的技术 (TOPSIS)。验证是客观进行的。这 集成层次分析法 (AHP) 和通过与理想解相似度排序的技术 (TOPSIS)。验证是客观进行的。这意思是±标准计算偏差以确保 AQM 方法排名经过系统排名。结果表明：（1）层次分析法与TOPSIS的结合解决了AQM方法的标杆问题；（2）个人TOPSIS上下文的结果清楚地显示了六位专家的排名结果之间的差异；(3)内部和外部TOPSIS群体决策得到的AQM方法排名接近，随机早期检测方法排名最好；(4)在客观验证中，各组得分存在显着差异，说明内部和外部TOPSIS组决策排序结果有效。