By Topic

Load Balancing Query Processing in Metric-Space Similarity Search

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Gil-Costa, V. ; Yahoo! Res. Latin America, Univ. of San Luis Argentina, San Luis Argentina, Argentina ; Marin, M.

Metric-space similarity search has been proven suitable for searching large collections of complex objects such as images. A number of distributed index data structures and respective parallel query processing algorithms have been proposed for clusters of distributed memory processors. Previous work has shown that best performance is achieved when using global indexing as opposed to local indexing. However global indexing is prone to performance degradation when query load becomes unbalanced across processors. This paper proposes a query scheduling algorithm that solves this problem. It adaptively load balances processing of user queries that are dynamically skewed towards particular sections of the distributed index. Sections highly hit by queries can be kept replicated. Experimental results show that with 1%-10% replication performance improves significantly (e.g., 35%) under skewed work-loads.

Published in:

Cluster, Cloud and Grid Computing (CCGrid), 2012 12th IEEE/ACM International Symposium on

Date of Conference:

13-16 May 2012