Simulated annealing based optimal controller placement in software defined networks with capacity constraint and failure awareness
Software Defined Networking is an evolving network model wherein the control plane is decoupled from data plane. It has become a fascinating problem to decide the number of controllers and their positions, and to allocate switches to them. Each switch must be assigned to a backup controller so that...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-09-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157821000987 |
| _version_ | 1817999769234046976 |
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| author | P. Aravind G.P. Saradhi Varma P.V.G.D. Prasad Reddy |
| author_facet | P. Aravind G.P. Saradhi Varma P.V.G.D. Prasad Reddy |
| author_sort | P. Aravind |
| collection | DOAJ |
| description | Software Defined Networking is an evolving network model wherein the control plane is decoupled from data plane. It has become a fascinating problem to decide the number of controllers and their positions, and to allocate switches to them. Each switch must be assigned to a backup controller so that if a controller encounters failure then the switches which are assigned to it can be immediately connected to their backup controllers. An existing method attempts to solve this problem by employing mixed integer linear programming; but it suffers from huge increase in execution time for larger networks. In order to reduce the execution time, this paper proposes a simulated annealing-based heuristic which aims to minimize the maximum of latencies from all switches to the respective backup controllers. The proposed algorithm is evaluated on seven real networks of varying sizes from Internet Topology Zoo and its performance is compared with the existing model. The results show that the proposed model achieves an average speed-up of 2.5 over the existing model (for the smallest network) and an average speed-up of 280 over the existing model (for the largest network). And at the same time, the proposed model produces near optimal solution. |
| first_indexed | 2024-04-14T03:13:27Z |
| format | Article |
| id | doaj.art-2b0dad44e936418d9ccabb57a3c2de7c |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-04-14T03:13:27Z |
| publishDate | 2022-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-2b0dad44e936418d9ccabb57a3c2de7c2022-12-22T02:15:33ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782022-09-0134857215733Simulated annealing based optimal controller placement in software defined networks with capacity constraint and failure awarenessP. Aravind0G.P. Saradhi Varma1P.V.G.D. Prasad Reddy2Department of Computer Science and Engineering, Gayatri Vidya Parishad College of Engineering (Autonomous), Visakhapatnam, Andhra Pradesh 530048, India; Corresponding author.Department of Computer Science and Engineering, Chaitanya Bharathi Institute of Technology (Autonomous), Hyderabad, Telangana 500075, IndiaDepartment of Computer Science and Systems Engineering, Andhra University, Visakhapatnam, Andhra Pradesh 530003, IndiaSoftware Defined Networking is an evolving network model wherein the control plane is decoupled from data plane. It has become a fascinating problem to decide the number of controllers and their positions, and to allocate switches to them. Each switch must be assigned to a backup controller so that if a controller encounters failure then the switches which are assigned to it can be immediately connected to their backup controllers. An existing method attempts to solve this problem by employing mixed integer linear programming; but it suffers from huge increase in execution time for larger networks. In order to reduce the execution time, this paper proposes a simulated annealing-based heuristic which aims to minimize the maximum of latencies from all switches to the respective backup controllers. The proposed algorithm is evaluated on seven real networks of varying sizes from Internet Topology Zoo and its performance is compared with the existing model. The results show that the proposed model achieves an average speed-up of 2.5 over the existing model (for the smallest network) and an average speed-up of 280 over the existing model (for the largest network). And at the same time, the proposed model produces near optimal solution.http://www.sciencedirect.com/science/article/pii/S1319157821000987Software defined networkLatencyController placementSimulated annealingOptimization |
| spellingShingle | P. Aravind G.P. Saradhi Varma P.V.G.D. Prasad Reddy Simulated annealing based optimal controller placement in software defined networks with capacity constraint and failure awareness Journal of King Saud University: Computer and Information Sciences Software defined network Latency Controller placement Simulated annealing Optimization |
| title | Simulated annealing based optimal controller placement in software defined networks with capacity constraint and failure awareness |
| title_full | Simulated annealing based optimal controller placement in software defined networks with capacity constraint and failure awareness |
| title_fullStr | Simulated annealing based optimal controller placement in software defined networks with capacity constraint and failure awareness |
| title_full_unstemmed | Simulated annealing based optimal controller placement in software defined networks with capacity constraint and failure awareness |
| title_short | Simulated annealing based optimal controller placement in software defined networks with capacity constraint and failure awareness |
| title_sort | simulated annealing based optimal controller placement in software defined networks with capacity constraint and failure awareness |
| topic | Software defined network Latency Controller placement Simulated annealing Optimization |
| url | http://www.sciencedirect.com/science/article/pii/S1319157821000987 |
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