Research Article

Dynamic query scheduling in parallel data warehouses

  1. Holger Märtens1,*,
  2. Erhard Rahm2,
  3. Thomas Stöhr2

Article first published online: 12 AUG 2003

DOI: 10.1002/cpe.786

Issue

Concurrency and Computation: Practice and Experience

Concurrency and Computation: Practice and Experience

Special Issue: Euro-Par 2002

Volume 15, Issue 11-12, pages 1169–1190, September 2003

Additional Information

How to Cite

Märtens, H., Rahm, E. and Stöhr, T. (2003), Dynamic query scheduling in parallel data warehouses. Concurrency and Computation: Practice and Experience, 15: 1169–1190. doi: 10.1002/cpe.786

Author Information

  1. 1

    University of Applied Sciences Braunschweig/Wolfenbüttel, Germany

  2. 2

    University of Leipzig, Institute of Computer Science, Augustusplatz 10–11, D-04109 Leipzig, Germany

*Fachhochschule Braunschweig/Wolfenbüttel, Department of Business Management, University of Applied Sciences, Robert-Koch-Platz 10–14, D-38440 Wolfsburg, Germany

Publication History

  1. Issue published online: 12 AUG 2003
  2. Article first published online: 12 AUG 2003
  3. Manuscript Accepted: 26 MAY 2003
  4. Manuscript Revised: 2 APR 2003
  5. Manuscript Received: 15 DEC 2002

Funded by

  • Deutsche Forschungsgemeinschaft. Grant Number: Ra497/10

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Keywords:

  • parallel database systems;
  • query optimization;
  • load balancing;
  • data warehousing;
  • Shared Disk architecture;
  • disk contention

Abstract

Parallel processing is a key to high performance in very large data warehouse applications that execute complex analytical queries on huge amounts of data. Although parallel database systems (PDBSs) have been studied extensively in the past decades, the specifics of load balancing in parallel data warehouses have not been addressed in detail.

In this study, we investigate how the load balancing potential of a Shared Disk (SD) architecture can be utilized for data warehouse applications. We propose an integrated scheduling strategy that simultaneously considers both processors and disks, regarding not only the total workload on each resource but also the distribution of load over time. We evaluate the performance of the new method in a comprehensive simulation study and compare it to several other approaches. The analysis incorporates skew aspects and considers typical data warehouse features such as star schemas with large fact tables and bitmap indices. Copyright © 2003 John Wiley & Sons, Ltd.

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