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남범석

Nam, Beomseok
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dc.citation.endPage 414 -
dc.citation.number 5-6 -
dc.citation.startPage 401 -
dc.citation.title DISTRIBUTED AND PARALLEL DATABASES -
dc.citation.volume 30 -
dc.contributor.author Nam, Beomseok -
dc.contributor.author Hwang, Deukyeon -
dc.contributor.author Kim, Jinwoong -
dc.contributor.author Shin, Minho -
dc.date.accessioned 2023-12-22T04:40:55Z -
dc.date.available 2023-12-22T04:40:55Z -
dc.date.created 2013-06-18 -
dc.date.issued 2012-10 -
dc.description.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. -
dc.identifier.bibliographicCitation DISTRIBUTED AND PARALLEL DATABASES, v.30, no.5-6, pp.401 - 414 -
dc.identifier.doi 10.1007/s10619-012-7098-y -
dc.identifier.issn 0926-8782 -
dc.identifier.scopusid 2-s2.0-84868352826 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/3570 -
dc.identifier.url http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84868352826 -
dc.identifier.wosid 000308226500007 -
dc.language 영어 -
dc.publisher SPRINGER -
dc.title High-throughput query scheduling with spatial clustering based on distributed exponential moving average -
dc.type Article -
dc.description.isOpenAccess FALSE -
dc.relation.journalWebOfScienceCategory Computer Science, Information Systems; Computer Science, Theory & Methods -
dc.relation.journalResearchArea Computer Science -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.subject.keywordAuthor Distributed query scheduling -
dc.subject.keywordAuthor Multiple query optimization -
dc.subject.keywordAuthor Spatial clustering -
dc.subject.keywordAuthor Cache aware load balancing -

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