Regrouping metric-space search index for search engine size adaptation

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Abstract

This work contributes to the development of search engines that self-adapt their size in response to fluctuations in workload. Deploying a search engine in an Infrastructure as a Service (IaaS) cloud facilitates allocating or deallocating computational resources to or from the engine. In this paper, we focus on the problem of regrouping the metric-space search index when the number of virtual machines used to run the search engine is modified to reflect changes in workload. We propose an algorithm for incrementally adjusting the index to fit the varying number of virtual machines. We tested its performance using a custom-build prototype search engine deployed in the Amazon EC2 cloud, while calibrating the results to compensate for the performance fluctuations of the platform. Our experiments show that, when compared with computing the index from scratch, the incremental algorithm speeds up the index computation 2–10 times while maintaining a similar search performance.

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Publication date17 Oct 2015
Publication titleSimilarity search and applications : 8th international conference, SISAP 2015, Glasgow, UK, October 12-14, 2015, proceedings
EditorsGiuseppe Amato, Richard Connor, Fabrizio Falchi, Claudio Gennaro
Place of PublicationChem (CH)
PublisherSpringer
Pages271-282
Number of pages12
ISBN (Electronic)978-3-319-25087-8
ISBN (Print)978-3-319-25086-1
Original languageEnglish
Event8th international conference on Similarity Search and Applications - Glasgow, United Kingdom
Duration: 12 Oct 201514 Oct 2015

Publication series

NameLecture notes in computer science
PublisherSpringer
Volume9371
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th international conference on Similarity Search and Applications
Abbreviated titleSISAP 2015
CountryUnited Kingdom
CityGlasgow
Period12/10/1514/10/15

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© Springer International Publishing Switzerland

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