Distributed and Parallel Databases

, Volume 30, Issue 5–6, pp 401–414

High-throughput query scheduling with spatial clustering based on distributed exponential moving average

  • Beomseok Nam
  • Deukyeon Hwang
  • Jinwoong Kim
  • Minho Shin
Article

Abstract

In distributed scientific query processing systems, leveraging distributed cached data is becoming more important. In such systems, a front-end query scheduler distributes queries among many application servers rather than processing queries in a few high-performance workstations. Although many query scheduling policies exist such as round-robin and load-monitoring, they are not sophisticated enough to exploit cached results as well as balance the workload. Efforts were made to improve the query processing performance using statistical methods such as exponential moving average. However, existing methods have limitations for certain query patterns: queries with hotspots, or dynamic query distributions. In this paper, we propose novel query scheduling policies that take into account both the contents of distributed caching infrastructure and the load balance among the servers. Our experiments show that the proposed query scheduling policies outperform existing policies by producing better query plans in terms of load balance and cache-hit ratio.

Keywords

Distributed query scheduling Multiple query optimization Spatial clustering Cache aware load balancing 

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Beomseok Nam
    • 1
  • Deukyeon Hwang
    • 1
  • Jinwoong Kim
    • 1
  • Minho Shin
    • 2
  1. 1.Electrical and Computer EngineeringUlsan National Inst. of Science and TechnologyUlsanKorea
  2. 2.Dept. of Computer EngineeringMyongji UniversityYonginKorea

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