A PBIL for Load Balancing in Network Coding Based Multicasting

  • Huanlai XingEmail author
  • Ying Xu
  • Rong Qu
  • Lexi Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9787)


One of the most important issues in multicast is how to achieve a balanced traffic load within a communications network. This paper formulates a load balancing optimization problem in the context of multicast with network coding and proposes a modified population based incremental learning (PBIL) algorithm for tackling it. A novel probability vector update scheme is developed to enhance the global exploration of the stochastic search by introducing extra flexibility when guiding the search towards promising areas in the search space. Experimental results demonstrate that the proposed PBIL outperforms a number of the state-of-the-art evolutionary algorithms in terms of the quality of the best solution obtained.


Load balancing Multicast Network coding Population based incremental learning 



This research was supported in part by NSFC (No.61401374), the Fundamental Research Funds for the Central Universities (No. 2682014RC23), the Project-sponsored by SRF for ROCS, SEM, P. R. China and University of Nottingham, UK.


  1. 1.
    Benslimane, A.: Multimedia Multicast on the Internet. ISTE, Norwood (2007)CrossRefGoogle Scholar
  2. 2.
    Li, S.Y.R., Yeung, R.W., Cai, N.: Linear network coding. IEEE Trans. Inform. Theory 49(2), 371–381 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Wang, N., Pavlou, G.: Traffic engineered multicast content delivery without MPLS overlay. IEEE Trans. Multimedia 9(3), 619–628 (2007)CrossRefGoogle Scholar
  4. 4.
    Chi, K., Yang, C., Wang, X.: Performance of network coding based multicast. IEE Proc. Commun. 153(3), 399–404 (2006)CrossRefGoogle Scholar
  5. 5.
    Hou, I.H., Tsai, Y.E., Abdelzaher, T.F., Gupta, I.: AdapCode: adaptive network coding for code updates in wireless sensor networks. In: Proceedings of the INFOCOM (2008)Google Scholar
  6. 6.
    Vieira, F., Lucani, D.E., Alagha, N.: Codes and balances: multibeam satellite load balancing with coded packets. In: Proceedings of the ICC (2012)Google Scholar
  7. 7.
    Jiang, D., Xu, Z., Li, W., Chen, Z.: Network coding-based energy-efficient multicast routing algorithm for multi-hop wireless networks. J. Syst. Softw. 104, 152–165 (2015)CrossRefGoogle Scholar
  8. 8.
    Kim, M., Ahn, C.W., Médard, M., Effros, M.: On minimizing network coding resources: an evolutionary approach. In: Proceedings of the NetCod (2006)Google Scholar
  9. 9.
    Kim, M., Médard, M., Aggarwal, V., O’Reilly, V., Kim, W., Ahn, C.W., Effros, M.: Evolutionary approaches to minimizing network coding resources. In: Proceedings of the INFOCOM (2007)Google Scholar
  10. 10.
    Kim, M., Aggarwal, V., O’Reilly, V., Médard, M., Kim, W.: Genetic representations for evolutionary minimization of network coding resources. In: Proceedings of the EvoCOMNET (2007)Google Scholar
  11. 11.
    Folly, K.A.: Multimachine power system stabilizer design based on a simplified version of genetic algorithms combined with learning. In: Proceedings of the ISAP2005 (2005)Google Scholar
  12. 12.
    Yang, S., Yao, X.: Population-based incremental learning with associative memory for dynamic environments. IEEE Trans. Evolut. Comput. 12(5), 542–561 (2008)CrossRefGoogle Scholar
  13. 13.
    Kim, J.H., Kim, Y.H., Choi, S.H., Park, I.W.: Evolutionary multi-objective optimization in robot soccer system for education. IEEE Comput. Intell. Mag. 4(1), 31–41 (2009)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Ho, S.L., Yang, S., Bai, Y., Huang, J.: A robust metaheuristic combing clonal colony optimization and population-based incremental learning methods. IEEE Trans. Magn. 50(2) (2014). DOI: 10.1109/TMAG.2013.2283886 Google Scholar
  15. 15.
    Xing, H., Qu, R.: A population based incremental learning for network coding resources minimization. IEEE Commun. Lett. 15(7), 698–700 (2011)CrossRefGoogle Scholar
  16. 16.
    Xing, H., Qu, R.: A population based incremental learning for delay constrained network coding resource minimization. In: Di Chio, C., et al. (eds.) EvoApplications 2011, Part II. LNCS, vol. 6625, pp. 51–60. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  17. 17.
    Gonzalez, C., Lozano, J.A., Larranaga, P.: Analyzing the population based incremental learning algorithm by means of discrete dynamical systems. Complex Syst. 12, 465–479 (2000)MathSciNetzbMATHGoogle Scholar
  18. 18.
    Ahn, C.W., Yoo, J.C.: Multi-objective evolutionary approach to coding-link cost trade-offs in network coding. Electron. Lett. 48(25), 1595–1596 (2012)CrossRefGoogle Scholar
  19. 19.
    Xing, H., Qu, R.: A nondominated sorting genetic algorithm for bi-objective network coding based multicast routing problems. Inform. Sci. 233, 36–53 (2013)CrossRefGoogle Scholar
  20. 20.
    Xing, H., Qu, R., Bai, L., Ji, Y.: On minimizing coding operations in network coding based multicast: an evolutionary algorithm. Appl. Intell. 41(3), 820–836 (2014)CrossRefGoogle Scholar
  21. 21.
    Lozada-Chang, L.V., Santana, R.: Univariate marginal distribution algorithm dynamics for a class of parametric functions with unitation constraints. Inform. Sci. 181(11), 2340–2355 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Xing, H., Ji, Y., Bai, L., Sun, Y.: An improved quantum-inspired evolutionary algorithm for coding resource optimization based network coding multicast scheme. AEUE 64(12), 1105–1113 (2010)Google Scholar
  23. 23.
    Ji, Y., Xing, H.: A memory-storable quantum-inspired evolutionary algorithm for network coding resource minimization. In: Kita, E. (Ed.) Evolutionary Algorithm, InTech, pp. 363–380 (2011)Google Scholar
  24. 24.
    Xu, L., Chen, Y., Chai, K.K., Schormans, J., Cuthbert, L.: Self-organising cluster-based cooperative load balancing in OFDMA cellular networks. Wiley Wirel. Commun. Mobile Comput. 15(7), 1171–1187 (2015)CrossRefGoogle Scholar
  25. 25.
    Xu, L., Cheng, X., Chen, Y., Chao, K., Liu, D., Xing, H.: Self-optimised coordinated traffic shifting scheme for LTE cellular systems. In: Proceedings of the ICSON2015 (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Information Science and TechnologySouthwest Jiaotong UniversityChengduPeople’s Republic of China
  2. 2.College of Computer Science and Electronic EngineeringHunan UniversityChangshaPeople’s Republic of China
  3. 3.School of Computer ScienceUniversity of NottinghamNottinghamUK
  4. 4.Department of Network Optimisation and ManagementChina Unicom Network Technology Research InstituteBeijingPeople’s Republic of China

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