An Efficient Controller Placement Algorithm using Clustering in Software Defined Networks
DOI:
https://doi.org/10.26636/jtit.2023.4.1371Keywords:
clustering, controller placement, PAM, K-means++, silhoutte score, SDNAbstract
Software-defined networking (SDN) is an emerging network paradigm that separates the control plane from data plane and provides programmable network management. The control plane within SDN is responsible for decision-making, while packet forwarding is handled by the data plane based on flow entries defined by the control plane. The placement of controllers is an important research issue that significantly impacts performance of SDN. In this work, we utilize clustering algorithms to group networks into multiple clusters and propose an algorithm for optimal controller placement within each cluster. Initially, using the silhouette score, we determine the optimal number of controllers for various topologies. Additionally, to enhance network performance, we employ the meeting point algorithm to calculate the optimal location for placing the controller within each cluster. We implement the proposed work in the Mininet emulator with POX as the SDN controller. Furthermore, we compare our proposed approach to existing work in terms of throughput, delay, and jitter using six topologies from the Internet Zoo dataset.
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J. Lu et al., "A Survey of Controller Placement Problem in Software-defined Networking", IEEE Access, vol. 7, pp. 24290-24307, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2893283
S.-K. Yoon, Z. Khalib, N. Yaakob, and A. Amir, "Controller Placement Algorithms in Software Defined Network - A Review of Trends and Challenges", MATEC Web of Conferences, vol. 140, art. no. 01014, 2017. DOI: https://doi.org/10.1051/matecconf/201714001014
M. Li, X. Bi, L. Wang, and X. Han, "A Method of Two-stage Clustering Learning Based on Improved DBSCAN and Density Peak Algorithm", Computer Communications, vol. 167, pp. 75-84, 2021;. DOI: https://doi.org/10.1016/j.comcom.2020.12.019
G. Ramya and R. Manoharan, "Traffic-aware Dynamic Controller Placement in SDN using NFV", The Journal of Supercomputing, vol. 79, no. 4, pp. 2082-2107, 2023. DOI: https://doi.org/10.1007/s11227-022-04717-8
M. Ider and B. Barekatain, "An Enhanced AHPTOPSIS-based Load Balancing Algorithm for Switch Migration in Software-defined Networks", The Journal of Supercomputing, vol. 77, pp. 563-596, 2021. DOI: https://doi.org/10.1007/s11227-020-03285-z
S. Torkamani-Azar and M. Jahanshahi, "A New GSO Based Method for SDN Controller Placement", Computer Communications, vol. 163, pp. 91-108, 2020. DOI: https://doi.org/10.1016/j.comcom.2020.09.004
V. Huang, G. Chen, Q. Fu, and E. Wen, "Optimizing Controller Placement for Software-Defined Networks," in: IFIP/IEEE Symposium on Integrated Network and Service Management (IM), Arlington, USA, pp. 224-232, 2019 (https://arxiv.org/pdf/1902.09451).
O. Flauzac, E.G. Robledo, C. Gonzalez, F. Mauhourat, and F. Nolot, "SDN Architecture to Prevent Attacks with OpenFlow", in: 8th International Conference on Wireless Networks and Mobile Communications (WINCOM), pp. 1-6, Reims, France, 2020. DOI: https://doi.org/10.1109/WINCOM50532.2020.9272445
L. Mamushiane, J. Mwangama, and A.A. Lysko, "Controller Placement Optimization for Software Defined Wide Area Networks (SDWAN)", ITU Journal on Future and Evolving Technologies, vol. 2, no. 1, pp. 45-66, 2021. DOI: https://doi.org/10.52953/PUIU5171
G. Ramya and R. Manoharan, "Prediction Based Dynamic Controller Placement in SDN", EAI Endorsed Transactions on Scalable Information Systems, vol. 8, no. 32, 2021.
A.K. Singh and S. Srivastava, "A Survey and Classification of Controller Placement Problem in SDN", International Journal of Network Management, vol. 28, no. 3, 2018. DOI: https://doi.org/10.1002/nem.2018
Y. Qi et al., "Towards Multi-controller Placement for SDN Based on Density Peaks Clustering", in: ICC IEEE International Conference on Communications, Shanghai, China, 2019. DOI: https://doi.org/10.1109/ICC.2019.8761814
H. Kuang, Y. Qiu, R. Li, and X. Liu, "A Hierarchical K-means Algorithm for Controller Placement in SDN-based WAN Architecture", in: 2018 10th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), Changsha, China, pp. 263-267, 2018. DOI: https://doi.org/10.1109/ICMTMA.2018.00070
W. Chen, C. Chen, X. Jiang, and L. Liu, "Multi-controller Placement towards SDN Based on Louvain Heuristic Algorithm", IEEE Access, vol. 6, pp. 49486-49497, 2018. DOI: https://doi.org/10.1109/ACCESS.2018.2867931
A.A.Z. Ibrahim et al., "A Modified Genetic Algorithm for Controller Placement Problem in SDN Distributed Network", in: 2021 26th IEEE Asia-Pacific Conference on Communications (APCC), Kuala Lumpur, Malaysia, 2021. DOI: https://doi.org/10.1109/APCC49754.2021.9609838
J. Chen, H. Yin, C. Xiao, and D. He, "A Cross Entropy-Based Approach for Controller Placement Problem in Software Defined Network", in: 2021 International Conference on Information Technology and Biomedical Engineering (ICITBE), Nanchang, China, 2021 . DOI: https://doi.org/10.1109/ICITBE54178.2021.00010
T. Das and M. Gurusamy, "Multi-objective Control Plane Dimensioning in Hybrid SDN/Legacy Networks", IEEE Transactions on Network and Service Management, vol. 18, no. 3, pp. 2929-2942, 2021. DOI: https://doi.org/10.1109/TNSM.2021.3066847
I. Maity, S. Misra, and C. Mandal, "SCOPE: Cost-Efficient QoS-Aware Switch and Controller Placement in Hybrid SDN", IEEE Systems Journal, vol. 16, no. 3, pp. 4873-4880, 2022. DOI: https://doi.org/10.1109/JSYST.2021.3124280
A.B. Sapkota, B.B.R. Dawadi, and C.S.R. Joshi, "Multi-Controller Placement Optimization Using Naked Mole-Rat Algorithm over Software-Defined Networking Environment", Journal of Computer Networks and Communications, vol. 2022, art. no. 3145276, 2022. DOI: https://doi.org/10.1155/2022/3145276
Closest Meeting Point [Online]. Available: https://www.educative.io/m/closest-meeting-point.
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