Advertisement

Parallel Processing with Autonomous Databases in a Cluster System

  • Stéphane Gançarski
  • Hubert Naacke
  • Esther Pacitti
  • Patrick Valduriez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2519)

Abstract

We consider the use of a cluster system for Application Service Provider (ASP). In the ASP context, hosted applications and databases can be update- intensive and must remain autonomous. In this paper, we propose a new solution for parallel processing with autonomous databases, using a replicated database organization. The main idea is to allow the system administrator to control the tradeoff between database consistency and application performance. Application requirements are captured through execution rules stored in a shared directory. They are used (at run time) to allocate cluster nodes to user requests in a way that optimizes load balancing while satisfying application consistency requirements. We also propose a new preventive replication method and a transaction load balancing architecture which can trade-off consistency for performance using execution rules. Finally, we discuss the on-going implementation at LIP6 using a Linux cluster running Oracle 8i.

Keywords

Integrity Constraint Cluster System Conflict Manager Master Node Slave Node 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    R. Alonso, D. Barbará, H. Garcia-Molina. Data Caching Issues in an Information Retrieval System. ACM Transactions on Database Systems (TODS), 15(3), 1990.Google Scholar
  2. [2]
    H. Berenson, P. Bernstein, J. Gray, J. Melton, E. O’Neil, P. O’Neil. A Critique of ANSI SQL Isolation Levels. In ACM SIGMOD Int. Conf. on Management of Data, 1995.Google Scholar
  3. [3]
    A. Doucet, S. Gançarski, C. León, M. Rukoz. Checking Integrity Constraints in Multidatabase Systems with Nested Transactions. In Int. Conf. On Cooperative Information Systems (CoopIS), 2001.Google Scholar
  4. [4]
    S. Gançarski, H. Naacke, P. Valduriez. Load Balancing of Autonomous Applications and Databases in a Cluster System. In 4th Workshop on Distributed Data and Structure (WDAS), 2002.Google Scholar
  5. [5]
    T. Grabs, K. Böhm, H.-J. Schek. Scalable Distributed Query and Update Service Implementations for XML Document Elements. In IEEE RIDE Int. Workshop on Document Management for Data Intensive Business and Scientific Applications, 2001.Google Scholar
  6. [6]
    M. Hayden. The Ensemble System. Technical Report, Departement of Computer Science, Cornell University, TR-98-1662, 1998.Google Scholar
  7. [7]
    B. Kemme, G. Alonso. Don’t be lazy be consistent: Postgres-R, A new way to implement Database Replication. In Int. Conf on Very Large Databases (VLDB), 2000.Google Scholar
  8. [8]
    C. Olston, J. Widom. Offering a Precision-Performance Tradeoff for Aggregation Queries over Replicated Data. In Int. Conf. on Very Large Databases (VLDB), 2000.Google Scholar
  9. [9]
    T. Özsu, P. Valduriez. Principles of Distributed Database Systems. Prentice Hall, 2nd edition, 1999.Google Scholar
  10. [10]
    T. Özsu, P. Valduriez. Distributed and Parallel Database Systems-Technology and current state-of-the-art. ACM Computing Surveys, 28(1), 1996.Google Scholar
  11. [11]
    E. Pacitti. Improving Data Freshness in Replicated Databases. PhD Thesis, INRIA-RR 3617, 1999.Google Scholar
  12. [12]
    E. Pacitti, O. Dedieu. Algorithms for Optimistic Replication on the Web. Journal of the Brazilian Computing Society, 2002, to appear.Google Scholar
  13. [13]
    E. Pacitti, P. Minet, E. Simon. Fast Algorithms for Maintaining Replica Consistency in Lazy Master Replicated Databases. In Int. Conf. on Very Large Databases (VLDB), 1999.Google Scholar
  14. [14]
    E. Pacitti, P. Minet, E. Simon. Replica Consistency in Lazy Master Replicated Databases. Distributed and Parallel Databases, 9(3), 2001.Google Scholar
  15. [15]
    E. Pacitti. Preventive Lazy Replication in Cluster Systems. Technical Report RR-2002-01, CRIP5, University Paris 5, 2002.Google Scholar
  16. [16]
    M. Patiño-Martínez, R. Jiménez-Peris, B. Kemme, G. Alonso. Scalable Replication in Database Clusters. In Int. Conf. on Distributed Computing (DISC), 2000.Google Scholar
  17. [17]
    D. Powel et al. Group communication (special issue). Communication of the ACM, 39(4), 1996.Google Scholar
  18. [18]
    U. Röhm, K. Böhm, H.-J. Schek. Cache-Aware Query Routing in a Cluster of Databases. Int. Conf. on Data Engineering (ICDE), 2001.Google Scholar
  19. [19]
    A. Sheth, M. Rusinkiewicz. Management of Interdependent Data: Specifying Dependency and Consistency Requirements. Workshop on the Management of Replicated Data, 1990.Google Scholar
  20. [20]
    D. Stacey. Replication: DB2, Oracle, or Sybase. Database Programming & Design. 7(12), 1994.Google Scholar
  21. [21]
    P. Valduriez. Parallel Database Systems: open problems and new issues. Int. Journal on Distributed and Parallel Databases, 1(2), 1993.Google Scholar
  22. [22]
    G. Voelker et al. Implementing Cooperative Prefetching and Caching in a Global Memory System.In ACM Sigmetrics Conf. on Performance Measurement, Modeling, and Evaluation, 1998.Google Scholar
  23. [23]
    G. Weikum. Principles and Realization Strategies of Multilevel Transaction Management. ACM Transactions on Database Systems (TODS), 16(1), 1991.Google Scholar
  24. [24]
    K. L. Wu, P. S Yu, C. Pu. Divergence Control for Epsilon-Serializability. In 8th Int. Conf. on Data Engineering (ICDE), 1992.Google Scholar
  25. [25]
    H. Yu, A. Vahdat. Efficient Numerical Error Bounding for Replicated Network Services. In Int. Conf. On Very Large Databases (VLDB), 2000.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Stéphane Gançarski
    • 1
  • Hubert Naacke
    • 1
  • Esther Pacitti
    • 2
  • Patrick Valduriez
    • 1
  1. 1.LIP6, University Paris 6PARIS
  2. 2.Institut de Recherche en Informatique de NantesFrance

Personalised recommendations