Vol. 20 No. 5 (2017) Cover Image
Vol. 20 No. 5 (2017)

Published: November 30, 2017

Pages: 1047-1056

Articles

SDN-Based Load Balancing Scheme for Fat-Tree Data Center Networks

Shavan Askar 1
1 Research Center, Duhok Polytechnic University
History
  • Received: August 10, 2017
  • Available Online: November 3, 2017

Abstract

This paper proposes a new load balancing algorithm for data center networks by means of exploiting the characteristics of Software Defined Networks. Mininet was utilized as an emulation tool for the purpose of emulating and evaluating the proposed design, Miniedit was utilized as a GUI tool for the same purpose. In order to obtain a realistic environment to the data center network, Fat-Tree topology was utilized with the following parameters; 4 pods, 16 edge switches, 16 aggregation switches, 4 core switches, and 16 hosts. Different scenarios and traffic distributions were applied in order to cover as much possible cases of the real traffic. POX controller was chosen as an SDN controller.The suggested design showed outperformance when compared to the traditional scheme in term of throughput and loss rate for all the evaluated scenarios. The first scenario assumes joining of new hosts while in the second scenario; there was an increase in the demand of the already established connections. The proposed algorithm showed a loss free performance in the first scenarios, whereas, the traditional scheme presented 15% to 31% loss rate for the same scenario. In the second scenario, the proposed algorithm recorded up to 81% improvement in the loss rate when compared to the traditional scheme.  Moreover, the proposed algorithm showed a superiority over the traditional scheme in term of throughput, where it maintained the throughput intact without any reduction in the first scenario in contrast to the traditional scheme that underwent from a considerable degradation in the throughput value. The traditional scheme underwent from an average throughput reduction of 5Mbps in the case of joining of new hosts (first scenario). In the second scenario, both schemes underwent from a throughput reduction, however, the proposed scheme always showed superiority over the traditional scheme, whereas, it recorded up to 16.6% improvement in the throughput average value.

Keywords

References

  1. Yang Peng, et. al., “Towards Comprehensive Traffic Forecasting in Cloud Computing: Design and Application”, IEEE/ACM Transactions on Networking, Vol. 24, No. 4, pp. 2210-2222, August 2016.
  2. Shavan Askar, Georgios Zervas, David K. Hunter, Dimitra Simeonidou, “Evaluation of Classified Cloning Scheme with Self-similar Traffic”, 3rd International Conference on Computer Science and Electronic Engineering (CEEC 2011), pp. 23-28, 2011.
  3. Heller, B., S. Seetharaman, P. Mahadevan, Y. Yiakoumis, P. Sharma, S. Banerjee, and N. Mckeown. 2010.
  4. Mohammad Al-Fares, et. al., “Hedera: Dynamic Flow Scheduling for Data Center Networks”, Networked Systems Design and Implementation (NSDI 2010) Symposium., 2010.
  5. Shubhi Prashant Shukla, “Comparative Analysis of Distance Vector Routing & Link State Protocols”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 3, No. 10, pp. 9533-9539, October 2015.
  6. James F. Kurose, Keith W. Ross, “Computer Networking: A Top-Down Approach”, 6th Edition, Pearson, 2012.
  7. Dan Li, Yunfei Shang, Wu He, and Congjie Chen, “Greening Data Center Network with Software Defined Exclusive Routing”, IEEE Transaction on Computers, Vol. 64, No. 9, pp. 2534-2544, 2015.
  8. Liming Wang, and Gang Lu, "The dynamic sub-topology load balancing algorithm for data center networks", International Conference on Information Networking (ICOIN 2016), Kota Kinabalu, pp. 268-273, 2016.
  9. Feilong Tang, Laurence T. Yang, Cang Tang, Jie Li, Minyi Guo, "A Dynamical and Load-Balanced Flow Scheduling Approach for Big Data Centers in Clouds”, IEEE Transactions on Cloud Computing , Vol. 99, pp.1-14, 2016.
  10. Zhaogang Shu; et. al, “Traffic Engineering in Software-Defined-Networking: Measurement and Management”, IEEE Access, Vol. 4, pp. 3246-3256, 2016.
  11. Sixto Ortiz, “Software-defined networking: On the verge of a breakthrough?”, IEEE Computer Society, Vol. 46, No. 7, pp. 10-12, July 2013.
  12. ONF TS-025, “OpenFlow Switch Specification”, Open Networking Foundation, Version 1.5.1, March 2015.
  13. Xuan-Nam Nguyen, Damien Saucez, Chadi Barakat, and Thierry Turletti, "Rules Placement Problem in OpenFlow Networks: A Survey," IEEE Communications Surveys & Tutorials, Vol. 18, No. 2, pp. 1273-1286, Secondquarter 2016.
  14. Andreas Blenk, Arsany Basta, Martin Reisslein, Wolfgang Kellerer, “Survey on Network Virtualization Hypervisors for Software Defined Networking", IEEE Communications Surveys & Tutorials, vol. 18, no. 1, pp. 655-685, Firstquarter 2016.
  15. Ian F. Akyildiz, Ahyoung Lee, Pu Wang, Min Luo, Wu Chou, "Research challenges for traffic engineering in software defined networks", IEEE Network, vol. 30, no. 3, pp. 52-58, May-June 2016.
  16. Jayaram Mudigonda, Praveen Yalagandula, Mohammad Al-Fres, Jeffrey Mogul, “SPAIN: COTS data-center ethernet for multipathing over arbitrary topologies”, 7th USENIX Symposium on Networked Systems Design and Implementation, 2010.
  17. Wei Wang, Yi Sun, Kave Salamatian, and Zhongcheng Li, “Adaptive Path Isolation for Elephant and Mice Flows by Exploiting Path Diversity in Datacenters”, IEEE Transaction on Network and Service Management, Vol. 13, No. 1, pp. 5-18, March 2016.
  18. Zhiyang Guo, and Yuanyuan Yang, “On Nonblocking Multicast Fat-Tree Data Center Networks with Server Redundancy”, IEEE Transactions on Computers, Vol. 64, No. 4, pp. 1058-1073, April 2015.
  19. WANG Yong, T. Xiaoling, H. Qian and K. Yuwen, “A Dynamic Load Balancing Method of Cloud-Center Based on SDN”. in China Communications, vol. 13, no. 2, pp. 130-137, Feb. 2016.
  20. G. Kornaros, T. Orphanoudakis and N. Zervos, “An efficient implementation of fair load balancing over multi-CPU SOC architectures”, Symposium on Digital System Design, pp. 197-203, Belek-Antalya, Turkey, 2003.
  21. Senthil Ganesh N, and Ranjani S., “Dynamic Load Balancing using Software Defined Networks”, International Journal of Computer Applications (0975-8887), 2015.
  22. M. Qilin and S. Weikang, “A Load Balancing Method Based on SDN, ” IEEE International Conference on Measuring Technology and Mechatronics Automation, China, 2015.
  23. Mininet. http://mininet.org. Accessed in August 2016
  24. Faris Keti and Shavan Askar “Emulation of Software Defined Networks Using Mininet in Different Simulation Environments. ” IEEE International Conference on Intelligent Systems, Modelling and Simulation, Malizia, 2015.
  25. Faris Keti and Shavan Askar “An Investigation of Mininet Emulator for Evaluating Software Defined Networks Performance”, Journal of Duhok University, Vol. 18, No. 1, 2016.
  26. Jun Duan, Yuanyuan Yang, “Placement and performance Analysis of Virtual Multicast Networks in Fat-Tree Data Center Networks”, IEEE Transactions on Parallel and Distributed Systems, Vol. 99 , No. 1, pp. 1-14, Janurary 2016.
  27. Y. Lei and et. al , “Multipath Routing in SDN-based Data Center Networks, ” IEEE European Conference on Networks and Communications , Paris, 2015.
  28. A. Craig and et. al “Load Balancing for Multicast Traffic in SDN using Real-Time Link Cost Modification, ” IEEE ICC-Next Generation Network Symposium, 2015.
  29. Iperf, https://iperf.fr/. Accessed in August 2016