پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شماره‌ها | 660 |
| تعداد مقالات | 9,661 |
| تعداد مشاهده مقاله | 68,770,259 |
| تعداد دریافت فایل اصل مقاله | 48,281,498 |
Enhancing quality of service in SDNs through Pareto-optimized controller placement using NS-MF algorithm | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 14، دوره 16، شماره 9، آذر 2025، صفحه 157-167 اصل مقاله (477.5 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2024.34424.5141 | ||
| نویسنده | ||
| Ahmad Jalili* | ||
| Department of Computer Engineering, Faculty of Basic Sciences and Engineering, Gonbad Kavous University, Gonbad Kavous, Iran | ||
| تاریخ دریافت: 23 خرداد 1403، تاریخ پذیرش: 26 مرداد 1403 | ||
| چکیده | ||
| Software-defined networks (SDN) have emerged as a new paradigm to overcome rigidity in traditional networks. SDN controllers manage network switches through a centralized control plane. Strategically placing controllers is vital for meeting performance needs. We model the NP-hard controller placement problem (CPP) as multi-objective optimization reconciling switch-controller latency, resilience to failures, inter-controller coordination overhead and load balancing. A customized Non-dominated Sorting Moth Flame algorithm (NS-MF) with novel recombination and perturbation techniques is proposed to effectively approximate the Pareto-optimal set of placements on large problem instances. NS-MF is benchmarked on a diverse corpus of 41 topologies against the exhaustive POCO solver, assessing computational time and solution quality tradeoffs. Compared to POCO, the proposed algorithm attains over 20X speedup for the largest graphs with an average optimality gap within 0.8%. The proposed NS-MF demonstrates superior performance over state-of-the-art metaheuristics (NSGA-II and PSA) in reconciling proximity and diversity objectives when estimating Pareto-optimal fronts. Experimental results substantiate NS-MF's efficacy in effectively navigating objectives pertinent to resilient SDN design. | ||
| کلیدواژه‌ها | ||
| Facility Location Problem (FLP)؛ Multi-Objective Combinatorial Optimization (MOCO)؛ Software Defined Networking (SDN)؛ Controller Placement Problem (CPP)؛ Non-dominated Sorting Moth Flame algorithm (NS-MF)؛ Heuristic Algorithms | ||
| مراجع | ||
|
[1] B. Almadani, A. Beg, and A. Mahmoud, DSF: A distributed SDN control plane framework for the east/west interface, IEEE Access 9 (2021), 26735–26754. [2] X. Cai, Y. Xiao, M. Li, H. Hu, H. Ishibuchi, and X. Li, A grid-based inverted generational distance for multi/many-objective optimization, IEEE Trans. Evolut. Comput. 25 (2020), no. 1, 21–34. [3] S. Dou, L. Qi, C. Yao, and Z. Guo, Exploring the impact of critical programmability on controller placement for software-defined wide area networks, IEEE/ACM Trans. Network. 31 (2023), no. 6, 2575–2588. [4] S. Favuzza, M.G. Ippolito, and E.R. Sanseverino, Crowded comparison operators for constraints handling in NSGA-II for optimal design of the compensation system in electrical distribution networks, Adv. Engin. Inf. 20 (2006), no. 2, 201–211. [5] D. Hock, S. Gebert, M. Hartmann, T. Zinner, and P. Tran-Gia, POCO-framework for Pareto-optimal resilient controller placement in SDN-based core networks, IEEE Network Oper. Manag. Symp., 2014, pp. 1–2. [6] D. Hock, M. Hartmann, S. Gebert, T. Zinner, and P. Tran-Gia, POCO-PLC: Enabling dynamic Pareto-optimal resilient controller placement in SDN networks, IEEE Conf. Comput. Commun. Workshops (INFOCOM WKSHPS), IEEE, 2014, pp. 115–116. [7] A.A. Ibrahim, F. Hashim, A. Sali, N.K. Noordin, K. Navaie, and S.M. Fadul, Reliability-aware swarm based multi-objective optimization for controller placement in distributed SDN architecture, Digital Commun. Networks 10 (2024), no. 5, 1245–1257. [8] H. Ishibuchi, H. Masuda, Y. Tanigaki, and Y. Nojima, Modified distance calculation in generational distance and inverted generational distance, Evolut. Multi-Criterion Optim.: 8th Int. Conf., EMO 2015, Guimaraes, Portugal, March 29–April 1, 2015, Springer International Publishing, Proc. Part II 8, 2015, pp. 110–125. [9] B. Isong, R.R.S. Molose, A.M. Abu-Mahfouz, and N. Dladlu, Comprehensive review of SDN controller placement strategies. IEEE Access 8 (2020), 170070–170092. [10] A. Jalili and M. Keshtgari, A new reliable controller placement model for software-defined WANs, J. AI Data Min. 8 (2020), no. 2, 269–277. [11] A. Jalili, M. Keshtgari, and R. Akbari, A new framework for reliable control placement in software-defined networks based on multi-criteria clustering approach, Soft Comput. 24 (2020), no. 4, 2897–2916. [12] R. Jeya, G.R. Venkatakrishnan, and V. Nagarajan, Placing controllers using latency metrics in a smart grid implementing software-defined networking architecture, Adv. Sci. Technol. 124 (2023), 828–835. [13] K. Kaur, U. Singh, and R. Salgotra, An enhanced moth flame optimization, Neural Comput. Appl. 32 (2020), 2315–2349. [14] S. Knight, H.X. Nguyen, N. Falkner, R. Bowden, and M. Roughan, The internet topology zoo, IEEE J. Selected Areas Commun. 29 (2011), no. 9, 1765–1775. [15] Y. Li, S. Guan, C. Zhang, and W. Sun, Parameter optimization model of heuristic algorithms for controller placement problem in large-scale SDN, IEEE Access 8 (2020), 151668–151680. [16] J. Ma, J. Chen, L. Dong, and X. Jiang, (2023). Research on placement of distributed SDN multiple controllers based on IAVOA, Cluster Comput. 27 (2024), no. 1, 913–930. [17] Y. Maleh, Y. Qasmaoui, K. El Gholami, Y. Sadqi, and S. Mounir, A comprehensive survey on SDN security: Threats, mitigations, and future directions, J. Rel. Intel. Envir. 9 (2023), no. 2, 201–239. [18] A. Naseri, M. Ahmadi, and L. PourKarimi, Placement of SDN controllers based on network setup cost and latency of control packets, Comput. Commun. 208 (2023), 15–28. [19] Y.S.D. Phaneendra, U. Prabu, and S. Yasmine, A study on multi-controller placement problem (MCPP) in software-defined networks, Int. Conf. Sustain. Comput. Data Commun. Syst., IEEE, 2023, pp. 1454–1458. [20] M.G. Resendel and C.C. Ribeiro, GRASP with path-relinking: Recent advances and applications, Metaheuristics: Progress as Real Problem Solvers, Springer, 2005, pp. 29–63. [21] M. Shehab, L. Abualigah, H. Al Hamad, H. Alabool, M. Alshinwan, and A.M. Khasawneh, Moth-flame optimization algorithm: Variants and applications, Neural Comput. Appl. 32 (2020), 9859–9884. [22] M. Shehab, H. Alshawabkah, L. Abualigah, and N. AL-Madi, Enhanced a hybrid moth-flame optimization algo[1]rithm using new selection schemes, Engin. Comput. 37 (2021), 2931–2956. [23] T. Singh, N. Saxena, M. Khurana, D. Singh, M. Abdalla, and H. Alshazly, Data clustering using moth-flame optimization algorithm, Sensors 21 (2021), no. 12, 4086. [24] X. Su, C. Zhang, C. Chen, L. Fang, and W. Ji, Dynamic configuration method of flexible workshop resources based on IICA-NS algorithm, Processes 10 (2022), no. 11, 2394. [25] C. Xu, C. Xu, B. Li, S. Li, and T. Li, Load-aware dynamic controller placement based on deep reinforcement learning in SDN-enabled mobile cloud-edge computing networks, Comput. Networks 234 (2023), 109900. | ||
|
آمار تعداد مشاهده مقاله: 765 تعداد دریافت فایل اصل مقاله: 205 |
||