ارائه روشی جهت کنترل ازدحام مبتنی بر مهاجرت سوئیچ در شبکه‌های نرم‌افزار محور با استفاده از الگوریتم بهبودیافته کرکس آفریقائی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکترای گروه مهندسی کامپیوتر، واحد میبد، دانشگاه آزاد اسلامی، میبد، ایران

2 استادیار گروه مهندسی کامپیوتر، واحد تفت، دانشگاه آزاد اسلامی، تفت، ایران

3 استادیار گروه مهندسی کامپیوتر، واحد میبد، دانشگاه آزاد اسلامی، میبد، ایران

10.22034/abmir.2025.22659.1093

چکیده

شبکه‌های نرم‌افزارمحور به دلیل قابلیت برنامه‌ریزی و انعطاف‌پذیری بالا، به‌عنوان راهکاری نوین در مدیریت شبکه‌های بزرگ و پیچیده مطرح‌شده‌اند. بااین‌حال، در شبکه‌های چنددامنه که از چندین کنترل‌کننده برای بهبود عملکرد و افزایش مقیاس‌پذیری استفاده می‌کنند، چالش‌های جدی ازجمله مدیریت ازدحام و توازن بار میان کنترل‌کننده‌ها پدید می‌آید. این مقاله، باهدف حل مسئله ازدحام، روشی مبتنی بر مهاجرت پویا و هدفمند سوئیچ‌ها را برای توزیع ترافیک و کاهش فشار بر کنترل‌کننده‌ها ارائه می‌دهد. روش پیشنهادی از یک الگوریتم بهبودیافته کرکس آفریقایی برای مدیریت بار کنترل‌کننده‌ها بهره می‌گیرد. در این روش، ابتدا میزان بار سوئیچ‌ها و کنترل‌کننده‌ها اندازه‌گیری شده و در صورت نیاز، سوئیچ‌های پُربار به کنترل‌کننده‌هایی با ظرفیت بیشتر منتقل می‌شوند. عملکرد روش پیشنهادی با دو روش مشابه مقایسه شده است. نتایج شبیه‌سازی نشان می‌دهد که روش پیشنهادی توان عملیاتی شبکه را به میزان 15 درصد افزایش داده و لرزش را حدود 20 درصد بهبود داده است. این نتایج نشان‌دهنده کارایی بالاتر روش پیشنهادی در بهبود عملکرد شبکه است.

کلیدواژه‌ها


عنوان مقاله [English]

Proposing a method for congestion control based on switch migration in software-defined networks using an improved African vulture metaheuristic algorithm

نویسندگان [English]

  • MohammadReza Jenabzadeh 1
  • Vahid Ayatollahitafti 2
  • MohammadReza Mollakhalili Meybodi 3
  • Mohammadreza Mollahoseini Ardakani 3
1 PhD Student, Department of Computer Engineering, Maybod Branch, Islamic Azad University, Maybod , Iran
2 Assistant Professor, Department of Computer Engineering, Taft Branch, Islamic Azad University, Taft, Iran
3 Assistant Professor, Department of Computer Engineering, Maybod Branch, Islamic Azad University, Maybod , Iran
چکیده [English]

Software-defined networks have emerged as an innovative solution for managing large and complex networks due to their high programmability and flexibility. However, in multi-domain networks that utilize multiple controllers to enhance performance and scalability, serious challenges arise, including congestion management and load balancing among controllers. This paper presents a method based on dynamic and targeted migration of switches to distribute traffic and reduce pressure on controllers, with the aim of addressing the congestion issue. The proposed method employs an improved African Vulture Optimization Algorithm for managing controller load. In this approach, the load levels of switches and controllers are first measured, and if necessary, heavily loaded switches are transferred to controllers with greater capacity. The performance of the proposed method is compared with two similar methods. Simulation results indicate that the proposed method increases network throughput by 15% and improves delay and jitter about 20%. These results indicate the higher efficiency of the proposed method in improving network performance.

کلیدواژه‌ها [English]

  • Software-defined networks
  • Congestion control
  • Switch migration
  • African vulture algorithm
  • Metaheuristic optimization
[1]     D. Kreutz, F. M. V. Ramos, P. Verissimo, C. E. Rothenberg, S. Azodolmolky, and S. Uhlig, “Software-Defined Networking: A Comprehensive Survey,” Jun. 2014, [Online]. Available: http://arxiv.org/abs/1406.0440
[2]     Hodaei and S. Babaie, “A Survey on Traffic Management in Software-Defined Networks: Challenges, Effective Approaches, and Potential Measures”, Springer, May 01, 2021,. doi: 10.1007/s11277-021-08100-3.
[3]     T. Semong et al., “Intelligent load balancing techniques in software defined networks: A survey,” Electronics (Switzerland), vol. 9, no. 7, pp. 1–24, Jul. 2020, doi: 10.3390/electronics9071091
[4]     Y. Zhao, Y. Li, X. Zhang, G. Geng, W. Zhang, and Y. Sun, “A Survey of Networking Applications Applying the Software Defined Networking Concept Based on Machine Learning,” IEEE Access, vol. 7, pp. 95397–95417, 2019, doi: 10.1109/ACCESS.2019.2928564
[5]     O. Adekoya, A. Aneiba, and M. Patwary, “An Improved Switch Migration Decision Algorithm for SDN Load Balancing,” IEEE Open Journal of the Communications Society, vol. 1, pp. 1602–1613, 2020, doi: 10.1109/OJCOMS.2020.3028971.
[6]     V. G. Ourimi and S. Bakhtiari, “A novel approach based on gray wolf evolutionary algorithm for controller load balancing in software defined networks using dynamic switch migration,” Journal of Soft Computing and Information Technology, Vol 11, pp. 32-48, 2022.
[7]     U. Prajapati, C. Chatterjee, and A. Banerjee, “OptiGSM: Greedy-Based Load Balancing with Minimum Switch Migrations in Software-Defined Networks,” IEEE Transactions on Network and Service Management,vol 21, pp. 2200-2210, 2023, doi: 10.1109/TNSM.2023.
[8]     U. Prajapati, B. Chand Chatterjee, and A. Banerjee, “FractionalLB: Controller Load Balancing Using Fractional Switch Migration in Software-Defined Networks,” IEEE Networking Letters, vol. 6, no. 2, pp. 129–133, Jan. 2024, doi: 10.1109/lnet.2024.3357089.
[9]     W. K. Lai, Y. C. Wang, Y. C. Chen, and Z. T. Tsai, “TSSM: Time-Sharing Switch Migration to Balance Loads of Distributed SDN Controllers,” IEEE Transactions on Network and Service Management, vol. 19, no. 2, pp. 1585–1597, Jun. 2022, doi: 10.1109/TNSM.2022.3146834.
[10] S. Yeo, Y. Naing, T. Kim, and S. Oh, “Achieving balanced load distribution with reinforcement learning-based switch migration in distributed SDN controllers,” Electronics (Switzerland), vol. 10, no. 2, pp. 1–16, Jan. 2021, doi: 10.3390/electronics10020162.
[11] H. Babbar, S. Rani, A. K. Bashir, and R. Nawaz, “LBSMT: Load Balancing Switch Migration Algorithm for Cooperative Communication Intelligent Transportation Systems,” IEEE Transactions on Green Communications and Networking, vol. 6, no. 3, pp. 1386–1395, Sep. 2022, doi: 10.1109/TGCN.2022.3162237.
[12] Y. Xu et al., “Dynamic Switch Migration in Distributed Software-Defined Networks to Achieve Controller Load Balance,” in IEEE Journal on Selected Areas in Communications, Institute of Electrical and Electronics Engineers Inc., pp. 515–529, Mar. 2019. doi: 10.1109/JSAC.2019.2894237.
[13] M. A. Jiru, K. Adere, T. G. Krishna, and J. R. Perumalla, “An Improved Switch Migration Method-Based Efficient Load Balancing for Multiple Controllers in Software-Defined Networks,” Journal of Cases on Information Technology, vol. 25, no. 1, 2023, doi: 10.4018/JCIT.326136.
[14] L. P. A. Sanchez, Y. Shen and M. Guo, “MDQ: A QoS-Congestion Aware Deep Reinforcement Learning Approach for Multi-Path Routing in SDN. Journal of Network and Computer Applications, Mar. 2025, 235, 104082
[15] C. Y. Chu, K. Xi, M. Luo, and H. J. Chao, “Congestion-aware single link failure recovery in hybrid SDN networks” in IEEE Conference on Computer Communications (INFOCOM). April. 2015, pp. 1086-1094,
[16] F. Al-Tam and N. Correia, “On load balancing via switch migration in software-defined networking,” IEEE Access, vol. 7, pp. 95998–96010, 2019, doi: 10.1109/ACCESS.2019.2929651.
[17]            OpenFlow Switch Specification, ON Foundation, 2011. [Online]. Available:https://www.opennetworking.org/
[18] C. Wang, B. Hu, S. Chen, D. Li, and B. Liu, “A Switch Migration-Based Decision-Making Scheme for Balancing Load in SDN,” IEEE Access, vol. 5, pp. 4537–4544, 2017, doi: 10.1109/ACCESS.2017.2684188.
[19] B. Abdollahzadeh, F. S. Gharehchopogh, and S. Mirjalili, “African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems,” Comput Ind Eng, vol. 158, Aug. 2021, doi: 10.1016/j.cie.2021.107408.
[20] K. S. Sahoo and B. Sahoo, “CAMD: A switch migration based load balancing framework for software defined networks,” IET Networks, vol. 8, no. 4, pp. 264–271, Jul. 2019, doi: 10.1049/iet-net.2018.5166.
[21] M. T. Islam, N. Islam, and M. Al Refat, “Node to Node Performance Evaluation through RYU SDN Controller,” Wirel Pers Commun, vol. 112, no. 1, pp. 555–570, May 2020, doi: 10.1007/s11277-020-07060-4.
[22] S. Bhardwaj and S. N. Panda, “Performance Evaluation Using RYU SDN Controller in Software-Defined Networking Environment,” Wirel Pers Commun, vol. 122, no. 1, pp. 701–723, Jan. 2022, doi: 10.1007/s11277-021-08920-3.