By Topic

Computation and communication schedule optimization for jobs with shared data

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)
En-Jan Chou ; Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Univ. Taipei, Taipei ; Pangfeng Liu ; Jan-Jan Wu

Almost every computation job requires input data in order to find the solution, and the computation cannot proceed without the required data becoming available. As a result a proper interleaving of data transfer and job execution has a significant impact on the overall efficiency. In this paper we analyze the computational complexity of the shared data job scheduling problem, with and without consideration of storage capacity constraint. We show that if there is an upper bound on the server capacity, the problem is NP-complete, even when each job depends on at most three data. On the other hand, if there is no upper bound on the server capacity, we show that there exists an efficient algorithm that gives optimal job schedule when each job depends on at most two data. We also give an efficient heuristic algorithm that gives good schedule for cases where there is no limit on the number of data a job may access.

Published in:

Parallel and Distributed Systems, 2007 International Conference on  (Volume:2 )

Date of Conference:

5-7 Dec. 2007