Heuristic Algorithms for Fragment Allocation in a Distributed Database System

Conference paper

Abstract

Communication costs caused by remote access and retrieval of table fragments accessed by queries is the main part execution cost of the distributed database queries. Data Allocation algorithms try to minimize this cost by assigning fragments at or near the sites they may be needed. Data Allocation Problem (DAP) is known to be NP-Hard and this makes heuristic algorithms desirable for solving this problem. In this study, we design a model based on Quadratic Assignment Problem (QAP) for the DAP. The QAP is a well-known problem that has been applied to different problems successfully. We develop a set of heuristic algorithms and compare them with each other through experiments and determine the most efficient one for solving the DAP in distributed databases.

Keywords

Distributed database design Fragmentation Heuristics. 

References

  1. 1.
    Ozsu, M.T., Valduriez, P.: Principles of Distributed Database Systems, 3rd edn, pp. 245–293. Springer (2011)Google Scholar
  2. 2.
    Dokeroglu, T., Cosar, A.: Dynamic programming with ant colony optimization metaheuristic for the optimization of distributed database queries. In: Proceedings of the 26th International Symposium on Computer and Information Sciences (ISCIS), London, Sept 2011Google Scholar
  3. 3.
    Lee, Z., Su, S., Lee, C.: A heuristic genetic algorithm for solving resource allocation problems. Knowl. Inf. Syst. 5(4), 503–511 (2003)Google Scholar
  4. 4.
    Koopmans, T.C., Beckmann, M.J.: Assignment problems and the location of economics activities. Econometrica 25, 53–76 (1957)MathSciNetMATHCrossRefGoogle Scholar
  5. 5.
    Laning, L.J., Leonard, M.S.: File allocation in a distributed computer communication network. IEEE Trans. Comput. 32(3), 232–244 (1983)Google Scholar
  6. 6.
    Gu, X., Lin, W.: Practically realizable efficient data allocation and replication strategies for distributed databases with buffer constraints. IEEE Trans. Parallel Distrib. Syst. 17(9), 1001–1013 (2006)Google Scholar
  7. 7.
    Ceri, S., Pelagatti, G.: Distributed Databases Principles and Systems. McGraw-Hill, New York (1984)Google Scholar
  8. 8.
    Bell, D.A.: Difficult data placement problems. Comput. J. 27(4), 315–320 (1984)Google Scholar
  9. 9.
    Corcoran, A.L., Hale, J.: A genetic algorithm for fragment allocation in a distributed database system. In: Proceedings of the 1994 ACM Symposium on Applied Computing (SAC 94), pp. 247–250. Phoenix (1994)Google Scholar
  10. 10.
    Frieder, O., Siegelmann, H.T.: Multiprocessor document allocation: a genetic algorithm approach. IEEE Trans. Knowl. Data Eng. 9(4), 640–642 (1997)Google Scholar
  11. 11.
    Ahmad, I., Karlapalem, K.: Evolutionary algorithms for allocating data in distributed database systems. Distrib. Parallel Databases 11, 5–32 (2002)Google Scholar
  12. 12.
    Adl, R.K., Rankoohi, S.M.T.R.: A new ant colony optimization based algorithm for data allocation problem in distributed databases. knowl. inf. syst. 25(1), 349–372 (2009)Google Scholar
  13. 13.
    Dokeroglu, T., Tosun, U., Cosar, A.: Parallel mutation operator for the quadratic assignment problem. In: Proceedings of WIVACE, Italian Workshop on Artificial Life and Evolutionary Computation, Parma, Feb 2012Google Scholar
  14. 14.
    Mamaghani, A.S., Mahi, M., Meybodi, M.R., Moghaddam, M.M.: A novel evolutionary algorithm for solving static data allocation problem in distributed database systems, In: Second International Conference on Network Applications, Protocols and Services, Reviews Booklet, Brussels (2010)Google Scholar
  15. 15.
    Lim, M.H., Yuan, Y., Omatu, S.: Efficient genetic algorithms using simple genes exchange local search policy for the quadratic assignment problem. Comput. Optim. Appl. 15(3), 249–268 (2000)Google Scholar
  16. 16.
    Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)Google Scholar
  17. 17.
    Sevinc, E., Cosar, A.: An evolutionary genetic algorithm for optimization of distributed database queries. Comput. J. 54(5), 717–725 (2011)Google Scholar
  18. 18.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)Google Scholar
  19. 19.
    Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B 26(1), 29 (1996)Google Scholar
  20. 20.
    Taillard, E.D., Gambardella, L.M., Gendreau, M., Potvin, J.Y.: Adaptive memory programming: a unifed view of meta-heuristics. EURO XVI Conference tutorial and research (1998)Google Scholar
  21. 21.
    Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E.: Equation of state calculations by fast computing machines. J. Chem. Phys. 21(6), 1087 (1953)Google Scholar
  22. 22.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)Google Scholar
  23. 23.
    He, X., Gu, Z., Zhu, Y.: Task allocation and optimization of distributed embedded systems with simulated annealing and geometric programming. Comput. J. 53(7), 1071–1091 (2010)Google Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.METU Computer Engineering DepartmentAnkaraTurkey

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