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A Particle Swarm Optimization Algorithm for Controller Placement Problem in Software Defined Network

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

Abstract

Software defined network (SDN) decouples the control plane from packet processing device and introduces the controller placement problem. The previous methods only focus on propagation latency between controllers and switches but ignore either the latency from controllers to controllers or the capacities of controllers, both of which are critical factors in real networks. In this paper, we define a global latency controller placement problem with capacitated controllers, taking into consideration both the latency between controllers and the capacities of controllers. And this paper proposes a particle swarm optimization algorithm to solve the problem for the first time. Simulation results show that the algorithm has better performance in propagation latency, computation time, and convergence.

Keywords

Software defined network Controller placement Propagation latency Particle swarm optimization 

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.
    McKeown, N., et al.: Open flow: enabling innovation in campus networks. ACM SIGCOMM Comput. Commun. Rev. 38(2), 69–74 (2008)CrossRefGoogle Scholar
  2. 2.
    Lange, S., et al.: Heuristic approaches to the controller placement problem in large scale SDN networks. IEEE Trans. Netw. Serv. Manage. 12(1), 4–17 (2015)CrossRefGoogle Scholar
  3. 3.
    Heller, B., Sherwood, R., McKeown, N.: The controller placement problem. In: Proceedings of ACM SIGGCOM HotSDN, pp. 7–12 (2012)Google Scholar
  4. 4.
    Hock, D., et al.: POCO-framework for Pareto-optimal resilient controller placement in SDN-based core network. In: IEEE NOMS (2014)Google Scholar
  5. 5.
    Hu, Y., Wang, W., et al.: Reliability-aware controller placement for software-defined networks. In: IEEE International Symposium on Integrated Network Management (2013)Google Scholar
  6. 6.
    Yao, G., Bi, J.: On the capacitated controller placement problem in software defined networks. IEEE Commun. Lett. 18(8), 1339–1342 (2014)CrossRefGoogle Scholar
  7. 7.
    Sallahi, A., St-Hilaire, M.: Optimal model for the controller placement problem in software defined networks. IEEE Commun. Lett. 19(1), 30–33 (2015)CrossRefGoogle Scholar
  8. 8.
    Koponen, T., Casado, M., Gude, N., Stribling, J., Poutievski, L., Zhu, M., Ramanathan, R., Iwata, Y., Inoue, H., Hama, T., Shenker, S.: Onix: a distributed control platform for large-scale production networks. In: Proceedings of OSDI (2010)Google Scholar
  9. 9.
    Jain, S., Kumar, A., Mandal, S., Ong, J., Poutievski, L., Singh, A., Venkata, S., Wanderer, J., Zhou, J., et al.: B4: experience with a globally-deployed software defined wan. ACM SIGCOMM Comput. Commun. Rev. 43, 3–14 (2013)CrossRefGoogle Scholar
  10. 10.
    Arya, V., Garg, N., Khandekar, R., Meyerson, A., Munagala, K., Pandit, V.: Local search heuristics for k-median and facility location problems. SIAM J. Comput. 33(3), 544–562 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the 6th International Symposium on Micromachine and Human Science, pp. 39–43 (1995)Google Scholar
  12. 12.
    Pehlivanoglu, Y.V.: A new particle swarm optimization method enhanced with a periodic mutation strategy and neural networks. IEEE Trans. Evol. Comput. 17(3), 436–452 (2013)CrossRefGoogle Scholar
  13. 13.
    Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: IEEE International Conference of Evolutionary Computation, Piscataway, vol. 8, no. 3, pp. 240–255 (1998)Google Scholar
  14. 14.
    Gong, M., Cai, Q., Chen, X., Ma, L.: Complex network clustering by multiobjective discrete particle swarm optimization based on decomposition. IEEE Trans. Evol. Comput. 18(1), 82–97 (2014)CrossRefGoogle Scholar
  15. 15.
    Naldi, M.: Connectivity of Waxman topology models. Comput. Commun. 29, 24–31 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Chuangen Gao
    • 1
  • Hua Wang
    • 1
  • Fangjin Zhu
    • 1
  • Linbo Zhai
    • 1
  • Shanwen Yi
    • 1
  1. 1.School of Computer Science and TechnologyShandong UniversityJinanChina

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