{"title":"Improved Learning-Automata-Based Clustering Method for Controlled Placement Problem in SDN","authors":"Azam Amin, Mohsen Jahanshahi, M. Meybodi","doi":"10.3390/app131810073","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":48760,"journal":{"name":"Applied Sciences-Basel","volume":" ","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Sciences-Basel","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.3390/app131810073","RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
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.