Improved Algorithms for Data Migration

  • Samir Khuller
  • Yoo-Ah Kim
  • Azarakhsh Malekian
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4110)


Our work is motivated by the need to manage data on a collection of storage devices to handle dynamically changing demand. As demand for data changes, the system needs to automatically respond to changes in demand for different data items. The problem of computing a migration plan among the storage devices is called the data migration problem. This problem was shown to be NP-hard, and an approximation algorithm achieving an approximation factor of 9.5 was presented for the half-duplex communication model in [Khuller, Kim and Wan: Algorithms for Data Migration with Cloning, SIAM J. on Computing, Vol. 33(2):448–461 (2004)]. In this paper we develop an improved approximation algorithm that gives a bound of 6.5+o(1) using various new ideas. In addition, we develop better algorithms using external disks and get an approximation factor of 4.5. We also consider the full duplex communication model and develop an improved bound of 4 +o(1) for this model, with no external disks.


Bipartite Graph Data Item Approximation Factor Edge Coloring Data Migration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Anderson, E., Hall, J., Hartline, J., Hobbes, M., Karlin, A., Saia, J., Swaminathan, R., Wilkes, J.: An Experimental Study of Data Migration Algorithms. In: Brodal, G.S., Frigioni, D., Marchetti-Spaccamela, A. (eds.) WAE 2001. LNCS, vol. 2141, pp. 145–158. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  2. 2.
    Aggarwal, G., Motwani, R., Zhu, A.: The load rebalancing problem. In: Symp. on Parallel Algorithms and Architectures, pp. 258–265 (2003)Google Scholar
  3. 3.
    Baev, I.D., Rajaraman, R.: Approximation algorithms for data placement in arbitrary networks. In: Proc. of ACM-SIAM SODA, pp. 661–670 (2001)Google Scholar
  4. 4.
    Khuller, S., Kim, Y.A., Wan, Y.C.: Algorithms for Data Migration with Cloning. Siam J. on Comput. 33(2), 448–461 (2004)MATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Bondy, J.A., Murty, U.S.R.: Graph Theory with Applications. American Elsevier, New York (1977)Google Scholar
  6. 6.
    Gandhi, R., Mestre, J.: Combinatorial algorithms for Data Migration to minimize the average completion time. In: Díaz, J., Jansen, K., Rolim, J.D.P., Zwick, U. (eds.) APPROX 2006 and RANDOM 2006. LNCS, vol. 4110. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Golubchik, L., Khanna, S., Khuller, S., Thurimella, R., Zhu, A.: Approximation Algorithms for Data Placement on Parallel Disks. In: Proc. of ACM-SIAM SODA, Washington, D.C., USA, pp. 661–670. Society of Industrial and Applied Mathematics (2000)Google Scholar
  8. 8.
    Golubchik, L., Khuller, S., Kim, Y., Shargorodskaya, S., Wan, Y.C.: Data migration on parallel disks. In: Albers, S., Radzik, T. (eds.) ESA 2004. LNCS, vol. 3221, pp. 689–701. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Guha, S., Munagala, K.: Improved algorithms for the data placement problem. In: Proc. of ACM-SIAM SODA, San Fransisco, CA, USA, pp. 106–107. Society of Industrial and Applied Mathematics (2002)Google Scholar
  10. 10.
    Hall, J., Hartline, J., Karlin, A., Saia, J., Wilkes, J.: On Algorithms for Efficient Data Migration. In: Proc. of ACM-SIAM SODA, pp. 620–629 (2001)Google Scholar
  11. 11.
    Kashyap, S., Khuller, S.: Algorithms for Non-Uniform Size Data Placement on Parallel Disks. In: Pandya, P.K., Radhakrishnan, J. (eds.) FSTTCS 2003. LNCS, vol. 2914, pp. 265–276. Springer, Heidelberg (2003); Full version to appear in: Journal of Algorithms (2006)CrossRefGoogle Scholar
  12. 12.
    Kashyap, S., Khuller, S., Wan, Y.C., Golubchik, L.: Fast reconfiguration of data placement in parallel disks. In: 2006 ALENEX Conference (January 2006)Google Scholar
  13. 13.
    Khuller, S., Kim, Y., Wan, Y.C.: On Generalized Gossiping and Broadcasting. In: Di Battista, G., Zwick, U. (eds.) ESA 2003. LNCS, vol. 2832, pp. 373–384. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  14. 14.
    Kim, Y.: Data Migration to minimize the average completion time. In: Proc. of ACM-SIAM SODA 2003, pp. 97–98 (2003)Google Scholar
  15. 15.
    Meyerson, A., Munagala, K., Plotkin, S.A.: Web caching using access statistics. In: Symposium on Discrete Algorithms, pp. 354–363 (2001)Google Scholar
  16. 16.
    Shachnai, H., Tamir, T.: On Two Class-constrained Versions of the Multiple Knapsack Problem. Algorithmica 29, 442–467 (2001)MATHCrossRefMathSciNetGoogle Scholar
  17. 17.
    Shachnai, H., Tamir, T.: Polynomial Time Approximation Schemes for Class-constrained Packing Problems. In: Jansen, K., Khuller, S. (eds.) APPROX 2000. LNCS, vol. 1913, pp. 238–249. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  18. 18.
    Shannon, C.E.: A Theorem on Colouring Lines of a Network. J. Math. Phys. 28, 148–151 (1949)MATHMathSciNetGoogle Scholar
  19. 19.
    Shmoys, D.B., Tardos, E.: An Aproximation Algorithm for the Generalized Assignment Problem. Mathematical Programming A 62, 461–474 (1993)CrossRefMathSciNetGoogle Scholar
  20. 20.
    Vizing, V.G.: On an Estimate of the Chromatic Class of a p-graph (Russian). Diskret. Analiz. 3, 25–30 (1964)MathSciNetGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Samir Khuller
    • 1
  • Yoo-Ah Kim
    • 2
  • Azarakhsh Malekian
    • 1
  1. 1.Department of Computer ScienceUniversity of MarylandCollege ParkUSA
  2. 2.Department of Computer Science and EngineeringUniversity of ConnecticutStorrsUSA

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