Journal of Intelligent Information Systems

, Volume 45, Issue 1, pp 29–59 | Cite as

Design of distributed database systems: an iterative genetic algorithm

Article

Abstract

The two important aspects for design of distributed database systems are operation allocation and data allocation. Operation allocation refers to query execution plan indicating which operations (subqueries) should be allocated to which sites in a computer network, so that query processing costs are minimized. Data allocation is to allocate relations to sites so that the performance of distributed database are improved. In this research, we developed a solution technique for operation allocation and data allocation problem, using three objective functions: total time minimization or response time minimization, and the combination of total time and response time minimization. We formulated these allocation problems and provided analytical cost models for each objective function. Since the problem is NP-hard, we proposed a heuristic solution based on genetic algorithm (GA). Comparison of results with the exhaustive enumeration indicated that GA produced optimal solutions in all cases in much less time.

Keywords

Distributed database design Operation (subquery) allocation Data allocation Optimization and performance Genetic algorithms 

References

  1. Arcangeli, J., Hameurlain, A., Migeon, E., Morvan, F. (2004). Mobile agent based self-adaptive join for wide-area distributed query processing. Journal of Database Management, 15(4), 25–44.CrossRefMATHGoogle Scholar
  2. Baiao, F., Mattoso, M., Zaverucha, G. (2004). A distribution design methodology for object DBMS. Journal of Distributed and Parallel Databases, 16(1), 45–90.CrossRefGoogle Scholar
  3. Bellatreche, L., Cuzzocrea, A., Benkrid, S. (2010). F&A: a methodology for effectively and efficiently designing parallel relational data warehouses on hetrogenous database clusters. In Data warehouse and knowledge discovery (DaWak) (pp. 89–104)Google Scholar
  4. Bellatreche, L., Cuzzocrea, A., Benkrid, S. (2012). Effectively and efficiently designing and querying parallel relational data warehouses on hetrogenous database clusters: the F&A approach. Journal of Database Management, 23(4), 17–51.CrossRefGoogle Scholar
  5. Bergsten, B., Couprie, M., Valduriez, P. (1993). Overview of parallel architectures for database. The Computer Journal, 36, 734–740.CrossRefGoogle Scholar
  6. Cheng, C., Lee, W., Wong, K. (2002). A genetic algorithm-based clustering approach for database partitioning. IEEE Transactions on Systems, Man, and Cybernetics, 32(3), 215–230.CrossRefGoogle Scholar
  7. Cuadrado, J. (1995). Optimize database queries. In Byte (pp. 57–63).Google Scholar
  8. Davis, L. (1991). Handbook of genetic algorithms. New York, NY: Van Nostrand Reinhold.Google Scholar
  9. Du, J., Alhajj, R., Barker, K. (2006). Genetic algorithms based approach to database vertical partitioning. Journal of Intelligent Information Systems, 26(2), 167–183.CrossRefMATHGoogle Scholar
  10. Goldberg, D.E. (1989). Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley.MATHGoogle Scholar
  11. Gorla, N. (2001). An object-oriented database design for improved performance. Data and Knowledge Engineering, 37, 117–138.CrossRefMATHGoogle Scholar
  12. Gu, X., Lin, W., Bharadwaj, V. (2006). Practically realizable efficient data allocation and replication strategies for distributed databases with buffer constraints. IEEE Transactions on Parallel & Distributed Systems, 17(9), 1001–1013.CrossRefGoogle Scholar
  13. Hababeh, I., Ramachandran, M., Bowring, N. (2007). A high-performance computing method for data allocation in distributed databse systems. Journal of Supercomputing, 39(1), 3–18.CrossRefGoogle Scholar
  14. Johansson, J., March, S., Naumann, J. (2003). Modeling network latency and parallel processing in distributed database design. Decision Sciences, 34(4), 677–706.CrossRefGoogle Scholar
  15. Keshavamurthy, B., Bettahally, K., Asad, D. (2013). Privacy preserving assocation rule mining over distributed databases using genetic algorithm. Neural Computing & Applications, 22, 351–364.CrossRefGoogle Scholar
  16. Kossmann, D. (2000). The state of the art in distributed query processing. ACM Computing Surveys, 32(4), 422–469.CrossRefGoogle Scholar
  17. Li, B., & Jiang, W. (2000). A novel stochastic optimization algorithm. IEEE Transactions on Systems, Man, and Cybernetics: Part B, 30(1), 191–198.Google Scholar
  18. March, S.T., & Rho, S. (1995). Allocating data and operations to nodes in distributed database design. IEEE Transactions on Knowledge and Data Engineering, 7(2), 305–317.CrossRefGoogle Scholar
  19. Martin, T., Lam K., Russel, J. (1990). An evaluation of site selection algorithms for distributed query processing. The Computer Journal, 33(1), 61–70.CrossRefGoogle Scholar
  20. Menon, S. (2005). Allocating fragments in distributed databases. IEEE Transactions on Parallel & Distributed Systems, 16(7), 577–585.CrossRefGoogle Scholar
  21. Ozsu, M., & Valduriez, P. (1991). Principles of distributed database systems, englewood cliffs. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  22. Seshadri, S., & Cooper, B. (2007). Routing queries through a peer-to-peer infobeacons network using information retrieval techniques. IEEE Transactions on Parallel & Distributed Systems, 18(12), 1754–1765.CrossRefGoogle Scholar
  23. Sevince, E., & Cosar, A. (2011). An evolutionary genetic algorithm for optimization of distributed database queries. The Computer Journal, 54(5), 717–725.CrossRefGoogle Scholar
  24. Song, S., & Gorla, N. (2000). A genetic algorithm for vertical fragmentation and access path selection. The Computer Journal, 43(1), 81–93.CrossRefMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Youngsan UniversityBusanRepublic of Korea

Personalised recommendations