Regrouping metric-space search index for search engine size adaptation


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.

Publication DOI:
Divisions: Engineering & Applied Sciences > Computer Science
Additional Information: © Springer International Publishing Switzerland
Event Title: 8th international conference on Similarity Search and Applications
Event Type: Other
Event Dates: 2015-10-12 - 2015-10-14
ISBN: 978-3-319-25086-1, 978-3-319-25087-8
Full Text Link: http://link.spr ... -319-25087-8_26
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2015-10-17
Authors: Al Ruqeishi, Khalil
Konečný, Michal ( 0000-0003-2374-9017)



Version: Accepted Version

License: Creative Commons Attribution

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