A Server Placement Algorithm Conscious of Communication Delays and Relocation Costs

  • Junho Shim
  • Taehee Lee
  • Sang-goo Lee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2376)


The server placement algorithm is to locate the given number of cache servers at “proper” coordinates in the network. A typical objective to determine good locations may be simply to find the set of client clusters in which the Euclidean center of each cluster is the location of the cache server. We claim, however, that the objective should also consider 1) the network communication delays and 2) the cost of relocating cache servers, if any. We exploit both hierarchical and partitioning approaches, and present our server placement algorithm. We evaluated the performance of the algorithm, and its result is promising.


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  1. 1.
    D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, 1991.Google Scholar
  2. 2.
    S. Guha, R. Rastogi, and K. Shim. “CURE: An Efficient Clustering Algorithm for Large databases,” Information Systems, Vol. 26, No. 1, Pergamon, 2001.Google Scholar
  3. 3.
    J. Han, and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2001.Google Scholar
  4. 4.
    S. Jamin, C. Jin, A.R. Kurc, D. Raz, and Y. Shavitl, “Constrained Mirror Placement on the Internet,” IEEE INFOCOMM, 2001Google Scholar
  5. 5.
    K. Leung, J. Shim, D. Tcherevik, and A. Vinberg, “A Scalable Yet Transparent Infrastructure for Distributed Applications,” Proc. of the 8th International Conference on Parallel and Distributed Systems, IEEE Computer Society, 2001.Google Scholar
  6. 6.
    P. Krishnan, D. Raz, and Y. Shavitt, “The Cache Location Problem,” IEEE/ACM Transactions on Networking, Vol. 8, No. 5, ACM, 2000.Google Scholar
  7. 7.
  8. 8.
    R.T. Ng, and J. Han, “Efficient and Effective Clustering Methods for Spatial Data Mining,” Proc. of the 20th Very Large Data Bases Conference, Morgan Kaufmann, 1994.Google Scholar
  9. 9.
    L. Qiu, V.N. Padmanabhan, and G.M. Voelker, “On the Placement of Web Server Replicas,” IEEE INFOCOMM, 2001Google Scholar
  10. 10.
    J. Shim, and T. Lee, “Survey on Clustering Algorithms for Optimal Server Placement,” Technical Report, Sookmyung Women’s University, 2001.Google Scholar
  11. 11.
    J. Shim, P. Scheuermann, and R. Vingralek, “Proxy Cache Algorithms: Design, Implementation, and Performance,” IEEE Transactions on Knowledge and Data Engineering, Vol. 11, No. 4, IEEE Computer Society, 1999.Google Scholar
  12. 12.
    U.S. Census Bureau, United States County Statistics,
  13. 13.
    R. Vingralek, Y. Breitbart, M. Sayal, and P. Scheuermann, “A Transparent Replication of HTTP Service,” Proc. of the 15th International Conference on Data Engineering, IEEE Computer Society, 1999.Google Scholar
  14. 14.
    W. E. Wright, “Gravitational Clustering,” Pattern Recognition, Vol. 9, Pergamon Press, 1977.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Junho Shim
    • 1
  • Taehee Lee
    • 2
  • Sang-goo Lee
    • 2
  1. 1.Department of Computer ScienceSookmyung Women’s UniversitySeoulKorea
  2. 2.School of Computer Science & EngineeringSeoul National UniversitySeoulKorea

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