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Nam, Beomseok
Data Intensive Computing Lab
Research Interests
  • Distributed and parallel computing, high performance computing, database systems, OS and storage systems

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

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Title
High-throughput query scheduling with spatial clustering based on distributed exponential moving average
Author
Nam, BeomseokHwang, DeukyeonKim, JinwoongShin, Minho
Keywords
Application Servers; Cached data; Distributed caching; Distributed query; Dynamic Query; Exponential moving averages; High-throughput; Hotspots; Load balance; Multiple query optimizations; Processing performance; Query patterns; Query processing system; Query scheduling; Round Robin; Spatial clustering
Issue Date
201210
Publisher
SPRINGER
Citation
DISTRIBUTED AND PARALLEL DATABASES, v.30, no.5-6, pp.401 - 414
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.
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DOI
http://dx.doi.org/10.1007/s10619-012-7098-y
ISSN
0926-8782
Appears in Collections:
ECE_Journal Papers

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