A Genetic-based Discrete Metaheuristic Algorithm for Controller Placement in Software-Defined Network

Subject Areas : Information Technology in Engineering Design (ITED) Journal

mahnaz khojand ¹ , Kambiz Majidzadeh ² , mohammad masderi ³ , yousef farhang ⁴

1 - Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran
2 - Urmia Branch, Islamic Azad University, Urmia, Iran
3 - Computer Engineering Department, Islamic Azad University
4 - Department of Computer Engineering, khoy Branch, Islamic Azad University, khoy, Iran

Received: 2024-07-19 Accepted : 2024-08-26 Published : 2024-08-14

Keywords: Software-defined network, controller placement problem, meta-heuristic, horse-held optimization algorithm, elite opposition-based learning, genetic algorithm,

Abstract :

Software-Defined Networking (SDN) concept involves separating the control plane from the data plane. The network control is determined by an entity called the controller located on the control plane. Determining the optimal number and placement of controllers on the control plane is known as the Controller Placement Problem (CPP). This article addresses the resolution of CPP using Unsupervised Kmeans (U-kmeans) and Horse Herd Optimized Algorithm (HOA). The U-kmeans algorithm clusters switches and determines the number of controllers. Since the CPP problem is discrete, the HOA algorithm uses genetic operators, called Modified HOA (MHOA). The next step of this article involves finding the optimal location for each controller within its cluster using MHOA. To improve the convergence rate, MHOA utilizes an Elite Opposition-based Learning (EOBL) strategy. The effectiveness and scalability of the proposed algorithm are evaluated through simulation tests on various networks. The results show that the proposed method outperforms other state-of-the-art algorithms regarding metrics such as end-to-end delay, load imbalance, and energy consumption. In particular, the proposed method reduces load imbalance by 9.66%, end-to-end delay by 19.65%, and average energy consumption by 8.43%.

References:

[1] G. Ramya and R. Manoharan, "Enhanced optimal placements of multi-controllers in SDN," Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 8187-8204, 2021.#
[2] A. Shirmarz and A. Ghaffari, "Performance issues and solutions in SDN-based data center: a survey," The Journal of Supercomputing, vol. 76, no. 10, pp. 7545-7593, 2020. #
[3] W. Xia, Y. Wen, C. H. Foh, D. Niyato, and H. Xie, "A survey on software-defined networking," IEEE Communications Surveys & Tutorials, vol. 17, no. 1, pp. 27-51, 2014. #
[4] D. Kreutz, F. M. Ramos, P. E. Verissimo, C. E. Rothenberg, S. Azodolmolky, and S. Uhlig, "Software-defined networking: A comprehensive survey," Proceedings of the IEEE, vol. 103, no. 1, pp. 14-76, 2014. #
[5] T. Jafarian, M. Masdari, A. Ghaffari, and K. Majidzadeh, "SADM-SDNC: security anomaly detection and mitigation in software-defined networking using C-support vector classification," Computing, vol. 103, pp. 641-673, 2021. #
[6] T. Jafarian, M. Masdari, A. Ghaffari, and K. Majidzadeh, "A survey and classification of the security anomaly detection mechanisms in software defined networks," Cluster Computing, vol. 24, pp. 1235-1253, 2021. #
[7] G. D’Angelo and F. Palmieri, "A co-evolutionary genetic algorithm for robust and balanced controller placement in software-defined networks," Journal of Network and Computer Applications, vol. 212, p. 103583, 2023. #
[8] A. Shirmarz and A. Ghaffari, "An adaptive greedy flow routing algorithm for performance improvement in software‐defined network," International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, vol. 33, no. 1, p. e2676, 2020. #
[9] K. Benzekki, A. El Fergougui, and A. Elbelrhiti Elalaoui, "Software‐defined networking (SDN): a survey," Security and communication networks, vol. 9, no. 18, pp. 5803-5833, 2016. #
[10] A. A. Ateya et al., "Chaotic salp swarm algorithm for SDN multi-controller networks," Engineering Science and Technology, an International Journal, vol. 22, no. 4, pp. 1001-1012, 2019. #
[11] A. Shirmarz and A. Ghaffari, "Automatic software defined network (SDN) performance management using TOPSIS decision-making algorithm," Journal of Grid Computing, vol. 19, pp. 1-21, 2021. #
[12] A. Shirmarz and A. Ghaffari, "An autonomic software defined network (SDN) architecture with performance improvement considering," Journal of Information Systems and Telecommunication (JIST), vol. 8, no. 30, pp. 121-129, 2020. #
[13] K. P. Sinaga and M.-S. Yang, "Unsupervised K-means clustering algorithm," IEEE access, vol. 8, pp. 80716-80727, 2020. #
[14] N. Firouz, M. Masdari, A. B. Sangar, and K. Majidzadeh, "A hybrid multi-objective algorithm for imbalanced controller placement in software-defined networks," Journal of Network and Systems Management, vol. 30, no. 3, p. 51, 2022. #
[15] A. Mohammadi-Balani, M. D. Nayeri, A. Azar, and M. Taghizadeh-Yazdi, "Golden eagle optimizer: A nature-inspired metaheuristic algorithm," Computers & Industrial Engineering, vol. 152, p. 107050, 2021. #
[16] K. S. Sahoo, D. Puthal, M. S. Obaidat, A. Sarkar, S. K. Mishra, and B. Sahoo, "On the placement of controllers in software-defined-WAN using meta-heuristic approach," Journal of Systems and Software, vol. 145, pp. 180-194, 2018. #
[17] A. Ghaffari, "Designing a wireless sensor network for ocean status notification system," Indian Journal of Science and Technology, vol. 7, no. 6, p. 809, 2014. #
[18] N. McKeown et al., "OpenFlow: enabling innovation in campus networks," ACM SIGCOMM computer communication review, vol. 38, no. 2, pp. 69-74, 2008. #
[19] S. Torkamani-Azar and M. Jahanshahi, "A new GSO based method for SDN controller placement," Computer Communications, vol. 163, pp. 91-108, 2020. #
[20] B. R. Killi and S. V. Rao, "Poly-stable matching based scalable controller placement with balancing constraints in SDN," Computer Communications, vol. 154, pp. 82-91, 2020. #
[21] A. Jalili, M. Keshtgari, and R. Akbari, "Optimal controller placement in large scale software defined networks based on modified NSGA-II," Applied Intelligence, vol. 48, pp. 2809-2823, 2018. #
[22] C. Gao, H. Wang, F. Zhu, L. Zhai, and S. Yi, "A particle swarm optimization algorithm for controller placement problem in software defined network," in Algorithms and Architectures for Parallel Processing: 15th International Conference, ICA3PP 2015, Zhangjiajie, China, November 18-20, 2015, Proceedings, Part III 15, 2015: Springer, pp. 44-54. #
[23] K. Kanodia, S. Mohanty, K. Kurroliya, and B. Sahoo, "CCPGWO: A meta-heuristic strategy for link failure aware placement of controller in SDN," in 2020 International Conference on Inventive Computation Technologies (ICICT), 2020: IEEE, pp. 859-863. #
[24] S. Rahman et al., "Virtualized controller placement for multi-domain optical transport networks using machine learning," Photonic Network Communications, vol. 40, pp. 126-136, 2020. #
[25] A. Ksentini, M. Bagaa, T. Taleb, and I. Balasingham, "On using bargaining game for optimal placement of SDN controllers," in 2016 IEEE International Conference on Communications (ICC), 2016: IEEE, pp. 1-6. #
[26] P. Yi, T. Hu, Y. Hu, J. Lan, Z. Zhang, and Z. Li, "SQHCP: secure-aware and QoS-guaranteed heterogeneous controller placement for software-defined networking," Computer Networks, vol. 185, p. 107740, 2021. #
[27] N. Firouz, M. Masdari, A. B. Sangar, and 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, vol. 24, pp. 2511-2544, 2021. #
[28] Y. Hu, T. Luo, N. C. Beaulieu, and C. Deng, "The energy-aware controller placement problem in software defined networks," IEEE Communications Letters, vol. 21, no. 4, pp. 741-744, 2016. #
[29] L. Wei, C. Chang, Y. Liu, and Y. Wang, "Energy-Efficient Controller Placement in Software-Defined Satellite-Terrestrial Integrated Network," Remote Sensing, vol. 14, no. 21, p. 5561, 2022. #
[30] C. Li, K. Jiang, and Y. Luo, "Dynamic placement of multiple controllers based on SDN and allocation of computational resources based on heuristic ant colony algorithm," Knowledge-Based Systems, vol. 241, p. 108330, 2022. #
[31] R. R. Mostafa, M. A. Gaheen, M. Abd ElAziz, M. A. Al-Betar, and A. A. Ewees, "An improved gorilla troops optimizer for global optimization problems and feature selection," Knowledge-Based Systems, vol. 269, p. 110462, 2023. #
[32] F. MiarNaeimi, G. Azizyan, and M. Rashki, "Horse herd optimization algorithm: A nature-inspired algorithm for high-dimensional optimization problems," Knowledge-Based Systems, vol. 213, p. 106711, 2021. #
[33] B. Heller, R. Sherwood, and N. McKeown, "The controller placement problem," ACM SIGCOMM Computer Communication Review, vol. 42, no. 4, pp. 473-478, 2012. #
[34] D. Hock, M. Hartmann, S. Gebert, M. Jarschel, T. Zinner, and P. Tran-Gia, "Pareto-optimal resilient controller placement in SDN-based core networks," in Proceedings of the 2013 25th international teletraffic congress (ITC), 2013: IEEE, pp. 1-9. #
[35] M. Khojand, K. Majidzadeh, M. Masdari, and Y. Farhang, "Controller placement in SDN using game theory and a discrete hybrid metaheuristic algorithm," The Journal of Supercomputing, pp. 1-49, 2023. #
[36] Z. C. Dagdia and M. Mirchev, "When Evolutionary Computing Meets Astro-and Geoinformatics," in Knowledge Discovery in Big Data from Astronomy and Earth Observation: Elsevier, 2020, pp. 283-306. #
[37] S. Knight, H. X. Nguyen, N. Falkner, R. Bowden, and M. Roughan, "The internet topology zoo," IEEE Journal on Selected Areas in Communications, vol. 29, no. 9, pp. 1765-1775, 2011. #
[38] M. A. Bagha, K. Majidzadeh, M. Masdari, and Y. Farhang, "Improving delay in SDNs by metaheuristic controller placement," International Journal of Industrial Electronics Control & Optimization, vol. 5, no. 3, 2022. #