Journal of Soft Computing and Information Technology

Journal of Soft Computing and Information Technology

A Novel Approach Based on Gray Wolf Evolutionary Algorithm for Controller Load Balancing in Software Defined Networks Using Dynamic Switch Migration

Document Type : Persian Original Article

Authors
Saeid Bakhtiari 1
Vosough Guran Ourimi 2
1 Computer Department, Amin Police University
2 Department of Computer, Faculty of Engineering, Central Tehran Branch, Islamic Azad University, Teheran, Iran.
Abstract
Software Defined Network (SDN) has become a popular model for centralized control and management in many modern network scenarios. However, for large data centers with hundreds of thousands of servers and several thousand switches, a single controller mode causes the system to suffer from lack of scalability and reliability. The use of distributed architecture can improve system performance, but the main limitation of this work is the static mapping between the switch and the controller, which may lead to load imbalances in the controllers. The use of multiple controllers distributed in SDN has been used to improve scalability and reliability, where each controller manages a fixed partition of the network. In the proposed method, a balanced controller (BalCon) is used as a scheme to dynamic migrate the SDN switch to achieve load balance between SDN controllers with low migration cost. The decision to migrate is made by the monitor based on network traffic. To balance load in the software-based network, a staged switch migration strategy is used and the target controller is selected by the gray wolf algorithm. The results show that the response time of the proposed method is 13% better than the BalCon method. Also, by increasing the sending rate to 8.33%, the controller's throughput improves compared to the BalCon method. Therefore, the load balance of the proposed method has a better performance than previous works.
Keywords
Software Defined Network
Switch Migration
Load Balancing
Gray Wolf Algorithm
  1. -Y. Hong et al., “Achieving high utilization with software-driven WAN,” presented at the SIGCOMM’13: ACM SIGCOMM 2013 Conference, Aug. 2013.
  2. Cohen, L. Lewin-Eytan, J. S. Naor, and D. Raz, “On the effect of forwarding table size on SDN network utilization,” presented at the IEEE INFOCOM 2014 - IEEE Conference on Computer Communications, Apr. 2014.
  3. Wang, Y. Guo, F. Hao, T. V. Lakshman, and S. Chen, “Scotch,” presented at the CoNEXT ’14: Conference on emerging Networking Experiments and Technologies, Dec. 2014.
  4. Yu, Y. Wang, K. Pei, S. Zhang, and J. Li, “A load balancing mechanism for multiple SDN controllers based on load informing strategy,” presented at the 2016 18th Asia-Pacific Network Operations and Management Symposium (APNOMS), Oct. 2016.
  5. Liang, R. Kawashima, and H. Matsuo, “Scalable and Crash-Tolerant Load Balancing Based on Switch Migration for Multiple Open Flow Controllers,” presented at the 2014 Second International Symposium on Computing and Networking (CANDAR), Dec. 2014.
  6. A. Dixit, F. Hao, S. Mukherjee, T. V. Lakshman, and R. Kompella, “ElastiCon,” presented at the the tenth ACM/IEEE symposium, 2014.
  7. Zhou, Y. Wang, J. Yu, J. Ba, and S. Zhang, “Load balancing for multiple controllers in SDN based on switches group,” presented at the 2017 19th Asia-Pacific Network Operations and Management Symposium (APNOMS), Sep. 2017.
  8. P.-C. Lin and Z. Tsai, “Hierarchical Edge-Cloud SDN Controller System With Optimal Adaptive Resource Allocation for Load-Balancing,” IEEE Systems Journal, vol. 14, no. 1, pp. 265–276, Mar. 2020.
  9. Al-Tam and N. Correia, “On Load Balancing via Switch Migration in Software-Defined Networking,” IEEE Access, vol. 7, pp. 95998–96010, 2019.
  10. Liu, Y. Wang, J. Zhang, H. Liao, Z. Liang, and X. Liu, “AAMcon: an adaptively distributed SDN controller in data center networks,” Front. Comput. Sci., vol. 14, no. 1, pp. 146–161, Mar. 2019.
  11. Rupani, N. Punjabi, M. Shamdasani, and S. Chaudhari, “Dynamic Load Balancing in Software-Defined Networks Using Machine Learning,” in Proceeding of International Conference on Computational Science and Applications, Springer Singapore, 2020, pp. 283–292.
  12. Montazerolghaem and M. H. Yaghmaee, “Load-Balanced and QoS-Aware Software-Defined Internet of Things,” IEEE Internet Things J., vol. 7, no. 4, pp. 3323–3337, Apr. 2020.
  13. Kulakov, A. Kohan, S. Kopychko, and R. Cherevatenko, “Load Balancing in Software Defined Networks Using Multipath Routing,” in Advances in Computer Science for Engineering and Education III, Springer International Publishing, pp. 384–395, January 2021.
  14. Zhao, X. Wang, Q. He, C. Zhang, and M. Huang, “PLOFR: An Online Flow Route Framework for Power Saving and Load Balance in SDN,” IEEE Systems Journal, vol. 15, no. 1, pp. 526–537, Mar. 2021.
  15. Cello, Y. Xu, A. Walid, G. Wilfong, H. J. Chao, and M. Marchese, “BalCon: A Distributed Elastic SDN Control via Efficient Switch Migration,” presented at the 2017 IEEE International Conference on Cloud Engineering (IC2E), Apr. 2017.
  16. Xu et al., “Dynamic Switch Migration in Distributed Software-Defined Networks to Achieve Controller Load Balance,” IEEE J. Select. Areas Commun., vol. 37, no. 3, pp. 515–529, Mar. 2019.
  17. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey Wolf Optimizer,” Advances in Engineering Software, vol. 69, pp. 46–61, Mar. 2014.
  18. Hu, J. Zhang, L. Wang, and D. Qiao, “An Improved Switch Migration Approach to Controller Load Balancing in SDN,” presented at the 2017 2nd International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2017), 2017.
  19. Dixit, F. Hao, S. Mukherjee, T. V. Lakshman, and R. Kompella, “Towards an elastic distributed SDN controller,” SIGCOMM Comput. Commun. Rev., vol. 43, no. 4, pp. 7–12, Sep. 2013.
  20. Bozorg-Haddad, Ed., “Advanced Optimization by Nature-Inspired Algorithms,” Springer Singapore, 2018.
Journal of Soft Computing and Information Technology
Volume 11, Issue 1 - Serial Number 35
Spring
Spring 2022
Pages 32-48