Efficient Resource Allocation Strategies for Video on Demand Services

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 258)

Abstract

The Web Services have gained considerable attention over the last few years. Video-on-Demand (VoD) systems have resulted in speedy growth of the web traffic. Therefore the concept of load balancer aimed to distribute the tasks to different Web Servers to reduce response times was introduced. This paper attempts to analyze the performance of FCFS, Randomized, Genetic algorithms and Heuristics algorithms for selecting server to meet the VoD requirement. Performances of these algorithms have been simulated with parameters like makespan and average resource utilization for different server models. This paper presents an efficient heuristic called Ga-max-min for distributing the load among servers. Heuristics like min-min and max-min are also applied to heterogeneous server farms and the result is compared with the proposed heuristic for VOD Servers. Ga-max-min was found to provide lower makespan and higher resource utilization than the genetic algorithm.

Keywords

Makespan Resource utilization FCFS Random Genetic Max-min Min-min 

References

  1. 1.
    Gupta, V., Balter, M.H., Sigman, K., Whitt, W.: Analysis of join-the-shortest-queue routing for web server farms. Perform. Eval. 64(9–12), 1062–1081 (2007)CrossRefGoogle Scholar
  2. 2.
    Narasimhan, A.: Distributed multimedia applications-opportunities, issues, risk and challenges: a closer look. In: IASTED International Conference on Intelligent Information Systems, pp. 455–460 (1997)Google Scholar
  3. 3.
    Panigrahi, N., Sahoo, B.: Qos based retrieval strategy for video on demand. Available Online at http://dspace.nitrkl.ac.in:8080/dspace/bitstream/2080/789/1/bdsahoo2009.pdf. Accessed 08 May 2011
  4. 4.
    Ligang, D., Bharadwaj, V., Ko, C.C.: Efficient movie retrieval strategies for movie-on-demand multimedia services on distributed networks. Multimedia Tools Appl. 20(2), 99133 (2003)Google Scholar
  5. 5.
    Niyato, D., Srinilta, C.: Load balancing algorithms for internet video and audio server. In: Proceedings of 9th IEEE International Conference on Networks, p. 76 (2001)Google Scholar
  6. 6.
    Ciardo, G., Riska, A., Smirni, E.: Equiload: a load balancing policy for clustered web servers. Perform. Eval. 46(2–3), 101–124 (2001)CrossRefMATHGoogle Scholar
  7. 7.
    Zhang, Z., Fan, W.: Web server load balancing: a queuing analysis. Eur. J. Oper. Res. 186(2), 681–693 (2008)CrossRefMATHMathSciNetGoogle Scholar
  8. 8.
    Ko, M., Koo, I.: An overview of interactive video on demand system. Technical Report, The University of British Columbia, 13 Dec 1996Google Scholar
  9. 9.
    N. Jian et al.: Hierarchical content routing in large-scale multimedia content delivery network. In: Proceedings of IEEE International Conference on Communications (ICC), Anchorage, May 2003Google Scholar
  10. 10.
    Wang, B., et al.: Optimal proxy cache allocation for efficient streaming media distribution. In: Proceedings of IEEE Infocom, New York, June 2002Google Scholar
  11. 11.
    Thouin, F., Coates, M.: VOD networks: design approaches and future challenges. In: Proceedings of Network IEEE, Montreal, pp. 42–48 (2007)Google Scholar
  12. 12.
    Shari_an, S., Motamedi, S.A., Akbari, M.K.: A predictive and probabilistic load balancing algorithm for cluster based web servers. In: Proceedings of Applied Soft Computing, p. 970, Jan 2011Google Scholar
  13. 13.
    Carlsson, N., Eager, D.L.: Server selection in large scale video on demand. In: Proceedings of ACM Transactions on Multimedia, Computing and Communications (2010)Google Scholar
  14. 14.
    Thouin, F.: VOD equipment allocation. Technical Report, Mcgill University, MontrealGoogle Scholar
  15. 15.
    Chauhan, S.S., Joshi, R.C.: A weighted time min-min max-min selective scheduling strategy for independent tasks on grid. In: Proceedings of Advance Computing Conference (IACC), Patiala, Feb 2010Google Scholar
  16. 16.
    Zomaya, A.Y., Teh Y.H.: On using genetic algorithms for dynamic load balancing. IEEE Trans. Parallel Distrib. Syst. 12(9), 899–911 (2001)Google Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNational Institute of Technology RourkelaRourkelaIndia

Personalised recommendations