SDN中基于改进学习自动机的受控布局问题聚类方法

IF 2.5 4区 综合性期刊 Q2 CHEMISTRY, MULTIDISCIPLINARY
Azam Amin, Mohsen Jahanshahi, M. Meybodi
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引用次数: 0

摘要

聚类是一种无监督的机器学习技术,在将未标记的数据划分为有意义的组方面起着至关重要的作用。K-means以其简单性而闻名,作为一种聚类方法已经获得了广泛的应用。然而,使用学习自动机的K-means和LAC算法对初始点的选择都很敏感。为了克服这一限制,我们提出了一种基于k -谐波均值方法的增强型LAC算法。我们在七个数据集上评估了它的性能,并证明了它比其他代表性算法的优越性。此外,我们定制此算法以解决软件定义网络中的控制器放置问题,这是此背景下的关键领域。为了优化开关控制器延迟、控制器间延迟和负载平衡等相关参数,我们利用了学习自动机。在我们用Python进行的比较分析中,我们在四种不同的网络拓扑上对我们的算法进行了光谱、K-means和LAC算法的基准测试。结果明确表明,我们提出的算法优于其他算法,实现了3%到11%的显著改进。本研究对聚类技术的发展及其在软件定义网络中的实际应用做出了贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improved Learning-Automata-Based Clustering Method for Controlled Placement Problem in SDN
Clustering, an unsupervised machine learning technique, plays a crucial role in partitioning unlabeled data into meaningful groups. K-means, known for its simplicity, has gained popularity as a clustering method. However, both K-means and the LAC algorithm, which utilize learning automata, are sensitive to the selection of initial points. To overcome this limitation, we propose an enhanced LAC algorithm based on the K-Harmonic means approach. We evaluate its performance on seven datasets and demonstrate its superiority over other representative algorithms. Moreover, we tailor this algorithm to address the controller placement problem in software-defined networks, a critical field in this context. To optimize relevant parameters such as switch–controller delay, intercontroller delay, and load balancing, we leverage learning automata. In our comparative analysis conducted in Python, we benchmark our algorithm against spectral, K-means, and LAC algorithms on four different network topologies. The results unequivocally show that our proposed algorithm outperforms the others, achieving a significant improvement ranging from 3 to 11 percent. This research contributes to the advancement of clustering techniques and their practical application in software-defined networks.
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来源期刊
Applied Sciences-Basel
Applied Sciences-Basel CHEMISTRY, MULTIDISCIPLINARYMATERIALS SCIE-MATERIALS SCIENCE, MULTIDISCIPLINARY
CiteScore
5.30
自引率
11.10%
发文量
10882
期刊介绍: Applied Sciences (ISSN 2076-3417) provides an advanced forum on all aspects of applied natural sciences. It publishes reviews, research papers and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files and software regarding the full details of the calculation or experimental procedure, if unable to be published in a normal way, can be deposited as supplementary electronic material.
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