Skip to main content
Log in

An enhanced AHP–TOPSIS-based load balancing algorithm for switch migration in software-defined networks

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Considering a software defined network, distributed controller architecture uses multiple controllers in which each controller manages a part of the network. The load imbalance problem in this architecture causes a large number of switch migrations resulting in a significant increase in switch migration cost and average network response time along with a decrease in throughput. Although recent studies have addressed these issues, access to optimal response time had been achieved with high cost of switch migration and sometimes with reduction of throughput using their methods. Therefore, the load balance in the present study is managed by a variable threshold based on the controllers’ workload. In other words, migration is done by selecting optimal switch and controller so that the switch will be selected with the lowest traffic generation rate which could return the source controller to its steady state. Using the suggested method, a destination controller is selected based on some important parameters such as CPU utilization, rate of incoming packets and the number of hops between switch and controller. The TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) algorithm is used to select the best controller based on the above-mentioned criteria and the AHP (analytic hierarchy process) algorithm is employed for determining the ratio of each criterion. The proposed method considerably outperforms other methods by achieving about 6 and 78% improvement in throughput and the number of switch migration in our implementation, respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30
Fig. 31
Fig. 32
Fig. 33

Similar content being viewed by others

References

  1. Kreutz D, Ramos F, Verissimo P, Rothenberg CE, Azodolmolky S, Uhlig S (2014) Software-defined networking: a comprehensive survey. arXiv preprint arXiv:14060440

  2. Singh S, Jha RK (2017) A survey on software defined networking: architecture for next generation network. J Netw Syst Manag 25(2):321–374

    Article  Google Scholar 

  3. Xia W, Wen Y, Foh CH, Niyato D, Xie H (2014) A survey on software-defined networking. IEEE Commun Surv Tutor 17(1):27–51

    Article  Google Scholar 

  4. Hu T, Guo Z, Yi P, Baker T, Lan J (2018) Multi-controller based software-defined networking: a survey. IEEE Access 6:15980–15996

    Article  Google Scholar 

  5. Li G, Wang X, Zhang Z (2019) SDN-based load balancing scheme for multi-controller deployment. IEEE Access 7:39612–39622

    Article  Google Scholar 

  6. Sahoo KS, Sahoo B (2019) CAMD: a switch migration based load balancing framework for software defined networks. IET Netw 8:264–271

    Article  Google Scholar 

  7. Al-Tam F, Correia N (2019) On load balancing via switch migration in software-defined networking. IEEE Access 7:95998–96010

    Article  Google Scholar 

  8. Li L, Xu Q (2017) Load balancing researches in SDN: a survey. In: 2017 7th IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC). IEEE, pp 403–408

  9. Cimorelli F, Priscoli FD, Pietrabissa A, Celsi LR, Suraci V, Zuccaro L (2016) A distributed load balancing algorithm for the control plane in software defined networking. In: 2016 24th Mediterranean Conference on Control and Automation (MED). IEEE, pp 1033–1040

  10. Wang K-Y, Kao S-J, Kao M-T (2018) An efficient load adjustment for balancing multiple controllers in reliable SDN systems. In: 2018 IEEE International Conference on Applied System Invention (ICASI). IEEE, pp 593–596

  11. Zhang S, Lan J, Sun P, Jiang Y (2018) Online load balancing for distributed control plane in software-defined data center network. IEEE Access 6:18184–18191

    Article  Google Scholar 

  12. Kang S-B, Kwon G-I (2016) Load balancing of software-defined network controller using genetic algorithm. Contemp Eng Sci 9(18):881–888

    Article  Google Scholar 

  13. Filali A, Kobbane A, Elmachkour M, Cherkaoui S (2018) SDN controller assignment and load balancing with minimum quota of processing capacity. In: 2018 IEEE International Conference on Communications (ICC). IEEE, pp 1–6

  14. Lu J, Ruan D (2007) Multi-objective group decision making: methods, software and applications with fuzzy set techniques, vol 6. Imperial College Press, London

    Book  Google Scholar 

  15. Sangaiah AK, Gopal J, Basu A, Subramaniam PR (2017) An integrated fuzzy DEMATEL, TOPSIS, and ELECTRE approach for evaluating knowledge transfer effectiveness with reference to GSD project outcome. Neural Comput Appl 28(1):111–123

    Article  Google Scholar 

  16. Sangaiah AK, Subramaniam PR, Zheng X (2015) A combined fuzzy DEMATEL and fuzzy TOPSIS approach for evaluating GSD project outcome factors. Neural Comput Appl 26(5):1025–1040

    Article  Google Scholar 

  17. Cello M, Xu Y, Walid A, Wilfong G, Chao HJ, Marchese M (2017) Balcon: a distributed elastic SDN control via efficient switch migration. In: 2017 IEEE International Conference on Cloud Engineering (IC2E). IEEE, pp 40–50

  18. Dixit A, Hao F, Mukherjee S, Lakshman T, Kompella RR (2014) ElastiCon; an elastic distributed SDN controller. In: 2014 ACM/IEEE Symposium on Architectures for Networking and Communications Systems (ANCS). IEEE, pp 17–27

  19. Hai NT, Kim D-S (2016) Efficient load balancing for multi-controller in SDN-based mission-critical networks. In: 2016 IEEE 14th International Conference on Industrial Informatics (INDIN). IEEE, pp 420–425

  20. Li J-Q, Sun E-C, Zhang Y-H (2018) Multi-threshold SDN controllers load balancing algorithm based on controller load. In: International Conference on Computer, Communication and Network Technology (CCNT 2018), Wuzhen, pp 1–10

  21. Hu T, Yi P, Zhang J, Lan J (2018) A distributed decision mechanism for controller load balancing based on switch migration in SDN. China Commun 15(10):129–142

    Article  Google Scholar 

  22. Cui J, Lu Q, Zhong H, Tian M, Liu L (2018) A load-balancing mechanism for distributed SDN control plane using response time. IEEE Trans Netw Serv Manag 15(4):1197–1206

    Article  Google Scholar 

  23. Xu Y, Cello M, Wang I-C, Walid A, Wilfong G, Wen CH-P, Marchese M, Chao HJ (2019) Dynamic switch migration in distributed software-defined networks to achieve controller load balance. IEEE J Sel Areas Commun 37(3):515–529

    Article  Google Scholar 

  24. Kasberg DW, Udapudi D, Yasser MA (2018) Automatic load balancing of switches in a cluster of controllers in a software-defined switch network. Google Patents

  25. Katta N, Hira M, Kim C, Sivaraman A, Rexford J (2016) Hula: scalable load balancing using programmable data planes. In: Proceedings of the Symposium on SDN Research. ACM, p 10

  26. Chen W, Shang Z, Tian X, Li H (2015) Dynamic server cluster load balancing in virtualization environment with openflow. Int J Distrib Sens Netw 11(7):531538

    Article  Google Scholar 

  27. Li Y, Pan D (2013) OpenFlow based load balancing for fat-tree networks with multipath support. In: Proceedings of the 12th IEEE International Conference on Communications (ICC’13), Budapest, Hungary, pp 1–5

  28. Ma Y-W, Chen J-L, Tsai Y-H, Cheng K-H, Hung W-C (2017) Load-balancing multiple controllers mechanism for software-defined networking. Wirel Pers Commun 94(4):3549–3574

    Article  Google Scholar 

  29. Chirammal HD, Mukhedkar P, Vettathu A (2016) Mastering KVM virtualization. Packt Publishing Ltd, Birmingham

    Google Scholar 

  30. Cbench benchmarking. https://githubcom/mininet/oflops/tree/master/cbench

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Behrang Barekatain.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ider, M., Barekatain, B. An enhanced AHP–TOPSIS-based load balancing algorithm for switch migration in software-defined networks. J Supercomput 77, 563–596 (2021). https://doi.org/10.1007/s11227-020-03285-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-020-03285-z

Keywords

Navigation