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Energy Saving and Load Balancing for SDN Based on Multi-objective Particle Swarm Optimization

  • Runshui Zhu
  • Hua Wang
  • Yanqing Gao
  • Shanwen Yi
  • Fangjin Zhu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9530)

Abstract

With the rapid development of cloud computing and large-scale data centers, the problem of network energy consumption is increasingly prominent. Most of the energy saving strategies on current IP network only aggregate traffic into a part of links. It leads to imbalance link utilization and seriously impacts the quality of service. With the emergence of the software defined network, the intelligent energy management becomes possible. In this paper, we take advantage of the centralized control and global vision of SDN to achieve the network energy saving and load balancing by dynamically aggregating and balancing of the traffic while ensuring QoS. We add actual QoS constrains to the basic maximum concurrent flow problem to formulate a multi-objective mixed integer programming model and we propose a multi-objective particle swarm optimization algorithm called MOPSO to solve this NP-hard problem. MOPSO distribute optimal paths for dynamic traffic demands and make idol switches and links into sleeping mode. Simulation results on real topologies and traffic demands show the effectiveness of our algorithm both on the objective of energy saving and load balancing compared with other algorithms.

Keywords

Software defined network Multi-objective particle swarm optimization Energy saving Load balancing Mixed integer programming 

Notes

Acknowledgments

The study is supported by the Natural Science Foundation of Shandong Province (Grant No. ZR2015FM008; ZR2013FM029), the Science and Technology Development Program of Jinan (Grant No. 201303010), the National Natural Science Foundation of China (NSFC No. 60773101), and the Fundamental Research Funds of Shandong University (Grant No. 2014JC037).

References

  1. 1.
    Bolla, R., Bruschi, R., Carrega, A., Davoli, F.: Green networking with packet processing engines: modeling and optimization. IEEE/ACM Trans. Networking (TON) 22(1), 110–123 (2014)CrossRefGoogle Scholar
  2. 2.
    Amaldi, E., Capone, A., Coniglio, S., Gianoli, L.G.: Energy-aware traffic engineering with elastic demands and MMF bandwidth allocation. In: IEEE 18th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), pp. 169–174. IEEE Press, USA (2013)Google Scholar
  3. 3.
    Yun, D., Lee, J.: Research in green network for future internet. J. KIISE 28(1), 41–51 (2010)MathSciNetGoogle Scholar
  4. 4.
    Bianzino, A.P., Chaudet, C., Rossi, D., Rougier, J.: A survey of green networking research. Commun. Surv. Tutorials 14(1), 3–20 (2012)CrossRefGoogle Scholar
  5. 5.
    Hong, C.Y., Kandula, S., Mahajan, R., Zhang, M., Gill, V., Nanduri, M., Wattenhofer, R.: Achieving high utilization with software-driven WAN. SIGCOMM 43(4), 15–26 (2013)CrossRefGoogle Scholar
  6. 6.
    McKeown, N., Anderson, T., Balakrishnan, H., Parulkar, G., Peterson, L., Rexford, J., Shenker, S., Turner, J.: OpenFlow: enabling innovation in campus networks. SIGCOMM 38(2), 69–74 (2008)CrossRefGoogle Scholar
  7. 7.
    Nunes, B.A.A., Mendonca, M., Xuan-Nam, N., Obraczka, K., Turletti, T.: A survey of software-defined networking: past, present, and future of programmable networks. Commu. Surv. Tutorials 16(3), 1617–1634 (2014)CrossRefGoogle Scholar
  8. 8.
    Shahrokhi, F., Matula, D.W.: The maximum concurrent flow problem. J. Assoc. Comput. Mach. 37(2), 318–334 (1990)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Gupta, M., Singh, S.: Greening of the Internet. In: Proceedings of the 2003 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, pp. 19–26. ACM, USA (2003)Google Scholar
  10. 10.
    Gupta, M., Singh, S.: Using low-power modes for energy conservation in Ethernet LANs. In: INFOCOM, pp. 2451–2455. IEEE Press, USA (2007)Google Scholar
  11. 11.
    Gunaratne, C., Christensen, K., Suen, S.W.: Ethernet adaptive link rate (alr): analysis of a buffer threshold policy. In: Global Telecommunications Conference, 2006, GLOBECOM 2006, pp. 1–6. IEEE Press, USA (2006)Google Scholar
  12. 12.
    Gunaratne, C., Christensen, K., Nordman, B., Suen, S.: Reducing the energy consumption of Ethernet with adaptive link rate (ALR). IEEE Trans. Comput. 57(4), 448–461 (2008)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Chiaraviglio, L., Mellia, M., Neri, F.: Reducing power consumption in backbone networks. In: IEEE International Conference on Communications ICC 2009, pp. 1–6. IEEE Press, USA (2009)Google Scholar
  14. 14.
    Heller, B., Seetharaman, S., Mahadevan, P., Yiakoumis, Y., Sharma, P., Banerjee, S., McKeown, N.: ElasticTree: saving energy in data center networks. In: NSDI, pp. 249–264. USENIX, USA (2010)Google Scholar
  15. 15.
    Chabarek, J., Sommers, J., Barford, P., Estan, C., Tsiang, D., Wright, S.: Power awareness in network design and routing. In: The 27th Conference on Computer Communications INFOCOM 2008, pp. 116–130. IEEE Press, USA (2008)Google Scholar
  16. 16.
    Rui, W., Zhipeng, J., Suixiang, G., Wenguo, Y., Yinben, X., Mingming, Z.: Energy-aware routing algorithms in software-defined networks. In: 2014 IEEE 15th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 6–20. IEEE Press, USA (2014)Google Scholar
  17. 17.
    Tu, R.L., Wang, X., Yang, Y.: Energy-saving model for SDN data centers. J. Supercomput 70(3), 1477–1495 (2014)CrossRefGoogle Scholar
  18. 18.
    Wang, J., Chen, X., Phillips, C., Yan, Y.: Energy efficiency with QoS control in dynamic optical networks with SDN enabled integrated control plane. Comput. Netw. 78(2), 57–67 (2015)Google Scholar
  19. 19.
    Orlowski, S., Wessäly, R., Pióro, M., Tomaszewski, A.: SNDlib 1.0—survivable network design library. Networks 55(3), 276–286 (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Runshui Zhu
    • 1
  • Hua Wang
    • 1
  • Yanqing Gao
    • 1
  • Shanwen Yi
    • 1
  • Fangjin Zhu
    • 1
  1. 1.School of Computer Science and TechnologyShandong UniversityJinanChina

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