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SD-WLB: An SDN-aided mechanism for web load balancing based on server statistics

  • Received : 2018.04.15
  • Accepted : 2018.09.05
  • Published : 2019.04.07

Abstract

Software-defined networking (SDN) is a modern approach for current computer and data networks. The increase in the number of business websites has resulted in an exponential growth in web traffic. To cope with the increased demands, multiple web servers with a front-end load balancer are widely used by organizations and businesses as a viable solution to improve the performance. In this paper, we propose a load-balancing mechanism for SDN. Our approach allocates web requests to each server according to its response time and the traffic volume of the corresponding switch port. The centralized SDN controller periodically collects this information to maintain an up-to-date view of the load distribution among the servers, and incoming user requests are redirected to the most appropriate server. The simulation results confirm the superiority of our approach compared to several other techniques. Compared to LBBSRT, round robin, and random selection methods, our mechanism improves the average response time by 19.58%, 33.94%, and 57.41%, respectively. Furthermore, the average improvement of throughput in comparison with these algorithms is 16.52%, 29.72%, and 58.27%, respectively.

Keywords

References

  1. H. Zhong, Y. Fang, and J. Cui, LBBSRT: An efficient SDN load balancing scheme based on server response time, Future Generation Comput. Syst. 68 (2017), 183-190. https://doi.org/10.1016/j.future.2016.10.001
  2. N. Mckeown, How SDN will shape networking, Oct. 2011, available at http://www.youtube.com/watch?v=c9-K5O_qYgA.
  3. S. Schenker, The future of networking, and the past of protocols, Oct. 2011, available at http://www.youtube.com/watch?v=YHeyuD89n1Y.
  4. H. Kim and N. Feamster, Improving network management with software‐defined networking, IEEE Commun. Mag. 51 (2013), no. 2, 114-119. https://doi.org/10.1109/MCOM.2013.6461195
  5. K. Gilly, C. Juiz, and R. Puigjaner, An up‐to‐date survey in web load balancing, World Wide Web 14 (2011), no. 2, 105-131. https://doi.org/10.1007/s11280-010-0101-5
  6. P. Patel et al., Ananta: Cloud scale load balancing, SIGCOMM Comput. Commun. Rev. 43 (2013), no. 4, 207-218. https://doi.org/10.1145/2534169.2486026
  7. Y. Li and D. Pan, OpenFlow based load balancing for Fat-Tree networks with multipath support, Proc. IEEE Int. Conf. Commun. (ICC'13), Budapest, Hungary, June 9-13, 2013, pp. 1-5.
  8. R. Wang, D. Butnariu, and J. Rexford, OpenFlow-based server load balancing gone wild, Proc. USENIX Conf. Hot Topics Manag. Internet, cloud, enterprise netw. Services, Boston, MA, USA, 2011, pp. 12-22.
  9. R. Gandhi et al., Duet: Cloud scale load balancing with hardware and software, Proc. ACM Conf. SIGCOMM (SIGCOMM '14), Chicago, IL, USA, Aug. 17-22, 2014, pp. 27-38.
  10. Project Floodlingt, available at http://www.projectfloodlight.org.
  11. Mininet, available at http://www.mininet.org.
  12. Open vSwitch, available at http://www.openvswitch.org.
  13. Microsoft, Tutorial guide, available at https://www.microsoft.com/net/core.
  14. Wikipedia, Microsift Azure, available at https://en.wikipedia.org/wiki/Microsoft_Azure.
  15. Wikipedia, Zipf's law, available at https://en.wikipedia.org/wiki/Zipf%27s_law.
  16. G. Velusamy and R. Lent, Smart load-balancer for web applications, Proc. Int. Conf. Smart Digital Environ. (ICSDE '17), Rabat Morocco, July 21-23, 2017, pp. 19-26.
  17. T. L. Lin et al., A parameterized wildcard method based on SDN for server load balancing, Int. Conf. Netw. Network Applicat. (NaNA), Hakodate, Japan, July 23-25, 2016, pp. 383-386.
  18. R. H. Hwang and H. P. Tseng, Load balancing and routing mechanism based on software defined network in data centers, Int. Comput. Symp. (ICS), Chiayi, Taiwan, Dec. 15-17, 2016, pp. 165-170.
  19. Sh. Wang et al., Flow distribution‐aware load balancing for the datacenter, Comput. Commun. 106 (2017), 136-146. https://doi.org/10.1016/j.comcom.2017.03.005
  20. G. Li et al., Fuzzy logic load-balancing strategy based on software- defined networking, in Wireless Internet. WiCON 2018, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 230, Springer, Cham, Switzerland, 2018, pp. 471-482.
  21. Z. Guo et al., Improving the performance of load balancing in software‐defined networks through load variance‐based synchronization, Comput. Netw. 68 (2014), 95-109. https://doi.org/10.1016/j.comnet.2013.12.004
  22. Y. Ma et al., Load‐balancing multiple controllers mechanism for software‐defined networking, Wireless Personal Commun. 94 (2017), no. 4, 3549-3574. https://doi.org/10.1007/s11277-016-3790-y