By Topic

Computing median values in a cloud environment using GridBatch and MapReduce

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)
Huan Liu ; Accenture Technol. Labs., San Jose, CA, USA ; Orban, D.

Traditional enterprise software is built around a dedicated high-performance infrastructure and it cannot map to an infrastructure cloud directly without a significant performance loss. Although MapReduce holds the promise as a viable approach, it lacks building blocks that enable high-performance optimization, especially in a shared infrastructure. Following on our previous work, we introduce another building block called the block level operator (BLO) and we show how it can be applied to solve a real enterprise application of finding the medians in a large data set. We propose two efficient approaches to compute medians, one using MapReduce and the other using the BLO. We compare the two approaches, as well as with that of using the traditional enterprise software stack, and show that our approach using the BLO gives an order of magnitude of improvement.

Published in:

Cluster Computing and Workshops, 2009. CLUSTER '09. IEEE International Conference on

Date of Conference:

Aug. 31 2009-Sept. 4 2009