The Journal of Supercomputing

, Volume 39, Issue 1, pp 3–18 | Cite as

A high-performance computing method for data allocation in distributed database systems

  • Ismail Omar Hababeh
  • Muthu Ramachandran
  • Nicholas Bowring
Article

Abstract

Enhancing the performance of the DDBs (Distributed Database system) can be done by speeding up the computation of the data allocation, leading to higher speed allocation decisions and resulting in smaller data redundancy and shorter processing time. This paper deals with an integrated method for grouping the distributed sites into clusters and customizing the database fragments allocation to the clusters and their sites. We design a high speed clustering and allocating method to determine which fragments would be allocated to which cluster and site so as to maintain data availability and a constant systemic reliability, and evaluate the performance achieved by this method and demonstrate its efficiency by means of tabular and graphical representation. We tested our method over different network sites and found it reduces the data transferred between the sites during the execution time, minimizes the communication cost needed for processing applications, and handles the database queries and meets their future needs.

Keywords

High-performance data allocation Communication cost Clustering Fragment allocation Simulation Performance evaluation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Karlapalem K, Navathe S, Morsi M (1994) Issues in distribution design of object oriented databases. Distributed Object Management, Morgan Kaufmann PublishersGoogle Scholar
  2. 2.
    Ezeife C, Barker K (1998) Distributed object based design: vertical fragmentation of classes. Int J Distr Parallel Databases, 6(4):327–360. Kluwer Academic Publishers.Google Scholar
  3. 3.
    Yee W, Donahoo M, Navathe S (2000) A framework for server data fragment grouping to improve server scalability in intermittently synchronized databases. CIKM.Google Scholar
  4. 4.
    Huang Y, Chen J (2001) Fragment allocation in distributed database design. J Inf Sci Eng 17:491–506MathSciNetGoogle Scholar
  5. 5.
    Cheng C, Lee W, Wong K (2002) A genetic algorithm-based clustering approach for database partitioning. IEEE Trans Syst Man Cybern—Part C: Appl Rev 32(3)Google Scholar
  6. 6.
    Lim S, Kai Y (1997) Vertical fragmentation and allocation in distributed deductive database systems. Inf Syst 22(1):1–24CrossRefGoogle Scholar
  7. 7.
    Hwang S, Yang C (1998) Component and data distribution in a distributed workflow management system. IEEE Soft Eng Conf 244–251Google Scholar
  8. 8.
    Son J, Kim M (2003) An adaptable vertical partitioning method in distributed systems. J Syst Soft. ElsevierGoogle Scholar
  9. 9.
    Daudpota N (1998) Five steps to construct a model of data allocation for distributed database systems. J Intell Inf Syst: Integr Artif Intell Database Technol 11(2):153–68Google Scholar
  10. 10.
    Lee H, Park Y, Jang G, Huh S (2000) Designing a distributed database on a local area network: a methodology and decision support system. Inf Soft Technol 42:171–184CrossRefGoogle Scholar
  11. 11.
    Tamhankar A, Ram S (1998) Database fragmentation and allocation: an integrated methodology and case study. IEEE Trans Syst, Man Cybern—Part A. Syst Hum 28(3):288–305CrossRefGoogle Scholar

Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  • Ismail Omar Hababeh
    • 1
  • Muthu Ramachandran
    • 1
  • Nicholas Bowring
    • 2
  1. 1.Faculty of Information and Technology, School of Computing LeedsLeeds Metropolitan UniversityUK
  2. 2.Department of Engineering & TechnologyManchester Metropolitan UniversityManchesterUK

Personalised recommendations