A hybrid evolutionary algorithm of optimized controller placement in sdn environment

• Autorzy: Hemagowri J. , Tamil Selvan P.

Identyfikatory

DOI

10.24423/cames.498

• Języki publikacji: EN

Abstrakty

EN

Controller placement problem (CPP) is a significant technological challenge in software defined network (SDN). Deployment of a properly designed SDN-based network is required to detect optimal number of controllers for enhancing the network’s performance. However, the best possible controller placement for enhancing the network’s performance faces many issues. To solve the CPP, a novel technique called the hybrid evolutionary algorithm of optimized controller placement (HEA-OCP) in SDN environment is introduced to increase network’s performance by different network topologies. In the proposed model, optimized controller placement using improved multi-objective artificial fish optimization is employed to improve data transmission and reduce latency. Controller placement can be determined using an undirected graph based on a variety of factors, including propagation delay, load balancing capabilities and bandwidth, fault tolerance and data transfer rate, and a variety of other factors. For each controller, the fitness value is calculated over multi-criteria functions. The optimizer’s performance can be improved with the use of Gaussian chaotic maps. In large-scale SDN networks using HEC-OCP, the algorithm dynamically analyzes the optimal number of controllers and the best connections between switches and controllers. As a result, the overall network performance is improved and the delay minimization-based controller placement strategy is obtained. The simulation of HEA-OCP with existing methods is conducted by a network topology dataset of various metrics, namely packet delivery ratio, packet drop rate, throughput, average latency, and jitter. The proposed HEA-OCP improves the packet delivery and throughput with reduced average latency, and packet drop ensures more instantaneous communications in real-time applications of SDN for better decision-making.

Słowa kluczowe

EN

controller; software defined network; Gaussian chaotic map; fish swarm; multi-criteria optimization

PL

kontroler; sieć zdefiniowana programowo; mapa chaosu Gaussa; rój ryb; optymalizacja wielokryterialna

• Wydawca: Instytut Podstawowych Problemów Techniki PAN
• Czasopismo: Computer Assisted Methods in Engineering and Science
• Rocznik: 2023
• Tom: Vol. 30, no. 4
• Strony: 539--556
• Opis fizyczny: Bibliogr. 21 poz., rys., tab.

Twórcy

autor

Hemagowri J.

• Department of Computer Science, Karpagam Academy of Higher Education, Coimbatore, India

autor

Tamil Selvan P.

• Department of Computer Science, Karpagam Academy of Higher Education, Coimbatore, India

Bibliografia

• 1. V. Suma, Wearable IoT based distributed framework for ubiquitous computing, Journal of Ubiquitous Computing and Communication Technologies (UCCT), 3(01): 23–32, 2021, doi: 10.36548/jucct.2021.1.003.
• 2. J.V. Anand, Design and development of secure and sustainable software defined networks, Journal of Ubiquitous Computing and Communication Technologies (UCCT), 1(02): 110–120, 2019, doi: 10.36548/jucct.2019.2.005.
• 3. 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, 34(8) Part B: 5721–5733, 2022, doi: 10.1016/j.jksuci.2021.04.012.
• 4. S. Torkamani-Azar, M. Jahanshahi, A new GSO based method for SDN controller placement, Computer Communications, 163: 91–108, 2020, doi: 10.1016/j.comcom.2020.09.004.
• 5. G. Schütz, J.A. Martins, A comprehensive approach for optimizing controller placement in software-defined networks, Computer Communications, 159: 198–205, 2020, doi: 10.1016/j.comcom.2020.05.008.
• 6. Y. Li, S. Guan, C. Zhang, W. Sun, Parameter optimization model of heuristic algorithms for controller placement problem in large-scale SDN, IEEE Access, 8: 151668–151680, 2020, doi: 10.1109/ACCESS.2020.3017673.
• 7. A.K. Singh, S. Maurya, N. Kumar, S. Srivastava, Heuristic approaches for the reliable SDN controller placement problem, Transaction on Emerging Telecommunication Technologies, 31(2): e3761, 2020, doi: 10.1002/ett.3761.
• 8. N. Samarji, M. Salamah, A fault tolerance metaheuristic-based scheme for controller placement problem in wireless software-defined networks, Internationals Journal of Communication System, 34(4): e4624, 2021, doi: 10.1002/dac.4624.
• 9. B.R. Killi, S.V. Rao, Poly-stable matching based scalable controller placement with balancing constraints in SDN, Computer Communications, 154: 82–91, 2020, doi: 10.1016/j.comcom.2020.02.053.
• 10. R. Soleymanifar, A. Srivastava, C. Beck, S. Salapaka, A clustering approach to edge controller placement in software-defined networks with cost balancing, IFAC Papers OnLine, 53(2): 2642–2647, 2020, doi: 10.1016/j.ifacol.2020.12.379.
• 11. Y. Fan, L. Wang, X. Yuan, Controller placements for latency minimization of both primary and backup paths in SDN, Computer Communications, 163: 35–50, 2020, doi: 10.1016/j.comcom.2020.09.001.
• 12. S. Tahmasebi, N. Rasouli, A.H. Kashefi, E. Rezabeyk, H.R. Faragardi, SYNCOP: An evolutionary multi-objective placement of SDN controllers for optimizing cost and network performance in WSNs, Computer Networks, 185: 107727, 2021, doi: 10.1016/j.comnet.2020.107727.
• 13. R. Chai, Q. Yuan, L. Zhu, Q. Chen, Control plane delay minimization-based capacitated controller placement algorithm for SDN, EURASIP Journal on Wireless Communications and Networking, 2019: 282, 17 pages, 2019, doi: 10.1186/s13638-019-1607-x.
• 14. N. Firouz, M. Masdari, A.B. Sangar, K. Majidzadeh, A novel controller placement algorithm based on network portioning concept and a hybrid discrete optimization algorithm for multi-controller software-defined networks, Cluster Computing, 24: 2511–2544, 2021, doi: 10.1007/s10586-021-03264-w.
• 15. T. Hu et al., An efficient approach to robust controller placement for link failures in software-defined networks, Future Generation Computer Systems, 124: 187–205, 2021, doi: 10.1016/j.future.2021.05.022.
• 16. A.A. Ateya et al., Chaotic salp swarm algorithm for SDN multi-controller networks, Engineering Science and Technology, an International Journal, 22(4): 1001–1012, 2019, doi: 10.1016/j.jestch.2018.12.015.
• 17. A. Jalili, M. Keshtgari, A new reliable controller placement model for software-defined WANs, Journal of AI and Data Mining, 8(2): 269–277, 2020, doi: 10.22044/jadm.2019.6319.1745.
• 18. Y.P. Llerena, P.R.L. Gondim, SDN-controller placement for D2D communications, IEEE Access, 7: 169745–169761, 2019, doi: 10.1109/ACCESS.2019.2955434.
• 19. E. Tohidi, S. Parsaeefard, M.A. Maddah-Ali, B.H. Khalaj, A. Leon-Garcia, Nearoptimal robust virtual controller placement in 5G software defined networks, IEEE Transactions on Network Science and Engineering, 8(2): 1687–1697, 2021, doi: 10.1109/TNSE.2021.3068975.
• 20. A.K. Tran, M.J. Piran, C. Pham, SDN controller placement in IoT networks: An optimized submodularity-based approach, Sensors, 19(24): 5474, 12 pages, 2019, doi: 10.3390/s19245474.
• 21. A. Dvir, Y. Haddad, A. Zilberman, The controller placement problem for wireless SDN, Wireless Networks, 25: 4963–4978, 2019, doi: 10.1007/s11276-019-02077-5.