Email this article A hybrid-ABC approach to the multi-controller placement problem of software-defined networks
Abstract
The emergence of Software-Defined Network (SDN) paradigm offers a revolutionary approach to network management, aiming to enhance agility and flexibility by centralizing control of network nodes. However, large-scale SDN deployments face scalability challenges, necessitating the use of multi-controller architectures. This gives rise to the Controller Placement Problem (CPP), a complex NP-hard issue that critically influences network performance. To address the CPP, this paper introduces a hybrid-ABC algorithm designed specifically for multi-CPP scenarios. The proposed hybrid-ABC algorithm leverages the efficiency of Artificial Bee Colony (ABC) metaheuristic, a nature-inspired approach known for quickly reaching optimal solutions. The proposed algorithm integrates K-means clustering with the ABC algorithm to enhance solution quality and address the issue of poor exploitation of solutions inherent in ABC. Thus, the proposed method is a fusion of metaheuristic swarm intelligence and machine learning techniques, combining the efficiency of ABC metaheuristic with K-means clustering. The study evaluates the efficacy of the hybrid-ABC algorithm against the traditional ABC algorithm across diverse datasets. Key performance metrics, including between-controller delays, solution quality, standard deviations, and latency times, are thoroughly assessed to measure effectiveness. Experimental results demonstrate that the proposed hybrid-ABC algorithm outperforms the traditional ABC algorithm in determining optimal controller placements within SDN architectures. This highlights the algorithm’s superior performance in solving the multi-CPP problem, highlighting its potential for enhancing SDN network scalability and efficiency.
Received: 22 April 2024
Accepted: 29 July 2024
Published: 26 August 2024
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R. Amin, E. Rojas, A. Aqdus, S. Ramzan, D. Casillas-Perez, and J. M. Arco, “A Survey on Machine Learning Techniques for Routing Optimization in SDN,” 2021. doi: 10.1109/ACCESS.2021.3099092.
D. Espinel Sarmiento, A. Lebre, L. Nussbaum, and A. Chari, “Decentralized SDN Control Plane for a Distributed Cloud-Edge Infrastructure: A Survey,” IEEE Communications Surveys and Tutorials, vol. 23, no. 1, 2021, doi: 10.1109/COMST.2021.3050297.
T. Hu, Z. Guo, P. Yi, T. Baker, and J. Lan, “Multi-controller Based Software-Defined Networking: A Survey,” IEEE Access, vol. 6, 2018, doi: 10.1109/ACCESS.2018.2814738.
J. Lu, Z. Zhang, T. Hu, P. Yi, and J. Lan, “A survey of controller placement problem in software-defined networking,” IEEE Access, vol. 7, 2019, doi: 10.1109/ACCESS.2019.2893283.
G. Wang, Y. Zhao, J. Huang, Q. Duan, and J. Li, “A K-means-based network partition algorithm for controller placement in software defined network,” in 2016 IEEE International Conference on Communications, ICC 2016, 2016. doi: 10.1109/ICC.2016.7511441.
B. BABAYİĞİT and B. ULU, “A High Available Multi-Controller Structure for SDN and Placement of Multi-Controllers of SDN with Optimized K-means Algorithm,” Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol. 11, no. 4, 2021, doi: 10.21597/jist.932575.
B. P. R. Killi, E. A. Reddy, and S. V. Rao, “Cooperative game theory based network partitioning for controller placement in SDN,” in 2018 10th International Conference on Communication Systems and Networks, COMSNETS 2018, 2018. doi: 10.1109/COMSNETS.2018.8328186.
L. Liao and V. C. M. Leung, “Genetic algorithms with particle swarm optimization based mutation for distributed controller placement in SDNs,” in 2017 IEEE Conference on Network Function Virtualization and Software Defined Networks, NFV-SDN 2017, 2017. doi: 10.1109/NFV-SDN.2017.8169836.
K. S. Sahoo, S. Sahoo, A. Sarkar, B. Sahoo, and R. Dash, “On the placement of controllers for designing a wide area software defined networks,” in IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2017. doi: 10.1109/TENCON.2017.8228398.
Q. Zhang et al., “A new quantum particle swarm optimization algorithm for controller placement problem in software-defined networking,” Computers and Electrical Engineering, vol. 95, 2021, doi: 10.1016/j.compeleceng.2021.107456.
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, 2017, doi: 10.1109/LCOMM.2016.2645558.
V. Ahmadi and M. Khorramizadeh, “An adaptive heuristic for multi-objective controller placement in software-defined networks,” Computers and Electrical Engineering, vol. 66, 2018, doi: 10.1016/j.compeleceng.2017.12.043.
B. Babayigit, B. Ulu, and E. N. Hascokadar, “Solving Multi-Controller Placement Problem in Software Defined Networks with A Genetic Algorithm,” in UBMK 2019 - Proceedings, 4th International Conference on Computer Science and Engineering, 2019. doi: 10.1109/UBMK.2019.8907199.
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, 2023, doi: 10.1016/j.jnca.2023.103583.
D. He, J. Chen, and X. Qiu, “A density algorithm for controller placement problem in software defined wide area networks,” Journal of Supercomputing, vol. 79, no. 5, 2023, doi: 10.1007/s11227-022-04873-x.
M. Dhar, A. Debnath, B. K. Bhattacharyya, M. K. Debbarma, and S. Debbarma, “A comprehensive study of different objectives and solutions of controller placement problem in software-defined networks,” Transactions on Emerging Telecommunications Technologies, vol. 33, no. 5, 2022, doi: 10.1002/ett.4440.
S. Guan, J. Li, Y. Li, and Z. Wang, “A multi-controller placement method for software defined network based on improved firefly algorithm,” Transactions on Emerging Telecommunications Technologies, vol. 33, no. 7, 2022, doi: 10.1002/ett.4482.
N. Lin, Q. Zhao, L. Zhao, A. Hawbani, L. Liu, and G. Min, “A Novel Cost-Effective Controller Placement Scheme for Software-Defined Vehicular Networks,” IEEE Internet Things J, vol. 8, no. 18, 2021, doi: 10.1109/JIOT.2021.3069878.
N. S. Radam, S. T. F. Al-Janabi, and K. S. Jasim, “Using Metaheuristics (SA-MCSDN) Optimized for Multi-Controller Placement in Software-Defined Networking,” Future Internet, vol. 15, no. 1, 2023, doi: 10.3390/fi15010039.
S. Malakar, M. Ghosh, S. Bhowmik, R. Sarkar, and M. Nasipuri, “A GA based hierarchical feature selection approach for handwritten word recognition,” Neural Comput Appl, vol. 32, no. 7, 2020, doi: 10.1007/s00521-018-3937-8.
N. Bacanin, R. Stoean, M. Zivkovic, A. Petrovic, T. A. Rashid, and T. Bezdan, “Performance of a novel chaotic firefly algorithm with enhanced exploration for tackling global optimization problems: Application for dropout regularization,” Mathematics, vol. 9, no. 21, 2021, doi: 10.3390/math9212705.
D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm,” Journal of Global Optimization, vol. 39, no. 3, 2007, doi: 10.1007/s10898-007-9149-x.
M. Abubaker and W. Ashour, “Efficient Data Clustering Algorithms: Improvements over Kmeans,” International Journal of Intelligent Systems and Applications, vol. 5, no. 3, 2013, doi: 10.5815/ijisa.2013.03.04.
M. B. A. - and H. M. H. -, “Kmeans-Based Convex Hull Triangulation Clustering Algorithm,” Research Notes in Information Science, vol. 9, no. 1, 2012, doi: 10.4156/rnis.vol9.issue1.3.
DOI: https://dx.doi.org/10.21622/ACE.2024.04.2.852
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Copyright (c) 2024 Bilal Babayigit, Mohammed Abubaker, Banu Ulu
Advances in Computing and Engineering
E-ISSN: 2735-5985
P-ISSN: 2735-5977
Published by:
Academy Publishing Center (APC)
Arab Academy for Science, Technology and Maritime Transport (AASTMT)
Alexandria, Egypt
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