By Topic

Large-scale distributed storage for highly concurrent Mapreduce applications

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

3 Author(s)
Moise, D. ; INRIA, IRISA, Rennes, France ; Antoniu, G. ; Bouge, L.

A large part of today's most popular applications are data-intensive. Whether they are scientific applications or Internet services, the data volume they process is continuously growing. Two main aspects arise when trying to accomodate the size of the data: processing the computation in a manner that is efficient both in terms of resources and time, and providing storage capable to deal with the requirements of data-intensive applications. Since the input data is large, the computation, which is, in most cases straightforward, is distributed across hundreds or thousands of machines; thus, the application is split into tasks that run in parallel on different machines, tasks that will need to access the data in a highly concurrent manner.

Published in:

Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW), 2010 IEEE International Symposium on

Date of Conference:

19-23 April 2010