Managing Traffic Demand Uncertainty in Replica Server Placement with Robust Optimization

  • Kin-Hon Ho
  • Stylianos Georgoulas
  • Mina Amin
  • George Pavlou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3976)


The replica server placement problem determines the optimal location where replicated servers should be placed in content distribution networks, in order to optimize network performance. The estimated traffic demand is fundamental input to this problem and its accuracy is essential for the target performance to be achieved. However, deriving accurate traffic demands is far from trivial and uncertainty makes the target performance hard to predict. We argue that it is often inappropriate to optimize the performance for only a particular set of traffic demands that is assumed accurate. In this paper, we propose a scenario-based robust optimization approach to address the replica server placement problem under traffic demand uncertainty. The objective is to minimize the total distribution cost across a variety of traffic demand scenarios while minimizing the performance deviation from the optimal solution. Empirical results demonstrate that robust optimization for replica server placement can achieve good performance under all the traffic demand scenarios while non-robust approaches perform significantly worse. This approach allows content distribution providers to provision better and predictable quality of service for their customers by reducing the impact of inaccuracy in traffic demand estimation on the replica server placement optimization.


Traffic Demand Error Margin Server Node User Node Content Distribution Network 
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  1. 1.
    Qiu, L., et al.: On the Placement of Web Server Replicas. In: Proc. IEEE INFOCOM (2001)Google Scholar
  2. 2.
    Jamin, S., et al.: Constrained Mirror Placement on the Internet. In: Proc. IEEE INFOCOM (2001)Google Scholar
  3. 3.
    Tang, X., Xu, J.: QoS-Aware Replica Placement for Content Distribution. IEEE Transactions on Parallel and Distribution Systems 16(10), 921–932 (2005)CrossRefGoogle Scholar
  4. 4.
    Rodolakis, G., et al.: Replicaed Server Placement with QoS Constraints. In: Proc. 3rd International Workshop on QoS in Multiservice IP Networks, QoSIP (2005)Google Scholar
  5. 5.
    Feldmann, A., et al.: Deriving Traffic Demands for Operational IP Networks: Methodology and Experience. IEEE/ACM Transactions on Networking 9(3), 265–280 (2001)CrossRefGoogle Scholar
  6. 6.
    Kouvelis, P., Yu, G.: Robust Discrete Optimization and Its Applications. Kluwer Academic Publishers, Dordrecht (1997)CrossRefMATHGoogle Scholar
  7. 7.
    Mulvey, J.M., et al.: Robust optimization of large-scale systems. Operations Research 43, 264–281 (1995)MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Hu, N., et al.: Optimizing Network Performance in Replicated Hosting. In: Proc. IEEE International Workshop on Web Caching and Content Distribution, WCW (2005)Google Scholar
  9. 9.
    Taha, H.A.: Operations Research, 7th edn. Prentice Hall, Englewood Cliffs (2003)Google Scholar
  10. 10.
    Medina, A., et al.: BRITE: An Approach to Universal Topology Generation. In: Proc. MASCOTS 2001 (2001)Google Scholar
  11. 11.
    Breslau, L., et al.: Web Caching and Zipf-like Distributions: Evidence and Implications. In: Proc. IEEE INFOCOM (1999)Google Scholar
  12. 12.
    A Modeling Language for Mathematical Programming, Available at,
  13. 13.
    The MINLP solver. University of Dundee, UKGoogle Scholar
  14. 14.
    Cohon, J.L.: Multiobjective programming and planning. Mathematics in Science and Engineering. Academic Press, New York (1978)MATHGoogle Scholar
  15. 15.
    Kangasharju, J., et al.: Object replication strategies in content distribution networks. Computer Communications 25(4), 376–383 (2002)CrossRefGoogle Scholar
  16. 16.
    Krishnan, P., et al.: The cache location problem. IEEE/ACM Transactions on Networking 8(5), 568–582 (2000)CrossRefGoogle Scholar
  17. 17.
    Vieira, S.L., Liebeherr, J.: Topology design for service overlay networks with bandwidthi guarantees. In: Proc. IEEE IWQOS, pp. 211–220 (2004)Google Scholar
  18. 18.
    Bektas, T., et al.: Designing cost-effective content distribution networks. To appear in Computers & Operations Research (2006)Google Scholar
  19. 19.
    The economic impacts of unacceptable web-site download speeds. Zona Research (1999)Google Scholar
  20. 20.
    Chankong, V., Haimes, Y.V.: Multiobjectve Decision Making–Theory and Methodology. Elsevier, New York (1983)MATHGoogle Scholar
  21. 21.
    Zhang, Y., et al.: An Information-Theoretic Approach to Traffic Matrix Estimation. In: Proc. ACM SIGCOMM (2003)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2006

Authors and Affiliations

  • Kin-Hon Ho
    • 1
  • Stylianos Georgoulas
    • 1
  • Mina Amin
    • 1
  • George Pavlou
    • 1
  1. 1.Centre for Communication Systems ResearchUniversity of SurreyUK

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