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 sensi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/18/10073 |
| _version_ | 1797581430377676800 |
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| author | Azam Amin Mohsen Jahanshahi Mohammad Reza Meybodi |
| author_facet | Azam Amin Mohsen Jahanshahi Mohammad Reza Meybodi |
| author_sort | Azam Amin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T23:05:21Z |
| format | Article |
| id | doaj.art-4e77eb2a957f4c90ac9cf9ae84168329 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T23:05:21Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4e77eb2a957f4c90ac9cf9ae841683292023-11-19T09:22:39ZengMDPI AGApplied Sciences2076-34172023-09-0113181007310.3390/app131810073Improved Learning-Automata-Based Clustering Method for Controlled Placement Problem in SDNAzam Amin0Mohsen Jahanshahi1Mohammad Reza Meybodi2Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran 1477893855, IranDepartment of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran 1477893855, IranDepartment of Computer Engineering and IT, Amirkabir University of Technology, Tehran 1591634311, IranClustering, 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.https://www.mdpi.com/2076-3417/13/18/10073clustering methodlearning automata (LA)k-Harmonic means (KHM)controller placement problem (CPP)software-defined network (SDN) |
| spellingShingle | Azam Amin Mohsen Jahanshahi Mohammad Reza Meybodi Improved Learning-Automata-Based Clustering Method for Controlled Placement Problem in SDN Applied Sciences clustering method learning automata (LA) k-Harmonic means (KHM) controller placement problem (CPP) software-defined network (SDN) |
| title | Improved Learning-Automata-Based Clustering Method for Controlled Placement Problem in SDN |
| title_full | Improved Learning-Automata-Based Clustering Method for Controlled Placement Problem in SDN |
| title_fullStr | Improved Learning-Automata-Based Clustering Method for Controlled Placement Problem in SDN |
| title_full_unstemmed | Improved Learning-Automata-Based Clustering Method for Controlled Placement Problem in SDN |
| title_short | Improved Learning-Automata-Based Clustering Method for Controlled Placement Problem in SDN |
| title_sort | improved learning automata based clustering method for controlled placement problem in sdn |
| topic | clustering method learning automata (LA) k-Harmonic means (KHM) controller placement problem (CPP) software-defined network (SDN) |
| url | https://www.mdpi.com/2076-3417/13/18/10073 |
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