By Topic

Job scheduling is more important than processor allocation for hypercube computers

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)
Krueger, P. ; Dept. of Comput. & Inf. Sci., Ohio State Univ., Columbus, OH, USA ; Lai, Ten-Hwang ; Dixit-Radiya, V.A.

Managing computing resources in a hypercube entails two steps. First, a job must be chosen to execute from among those waiting (job scheduling). Next a particular subcube within the hypercube must be allocated to that job (processor allocation). Whereas processor allocation has been well studied, job scheduling has been largely neglected. The goal of this paper is to compare the roles of processor allocation and job scheduling in achieving good performance on hypercube computers. We show that job scheduling has far more impact on performance than does processor allocation. We propose a new family of scheduling disciplines, called Scan, that have particular performance advantages. We show that performance problems that cannot be resolved through careful processor allocation can be solved by using Scan job-scheduling disciplines. Although the Scan disciplines carry far less overhead than is incurred by even the simplest processor allocation strategies, they are far more able to improve performance than even the most sophisticated strategies. Furthermore, when Scan disciplines are used, the abilities of sophisticated processor allocation strategies to further improve performance are limited to negligible levels. Consequently, a simple O(n) allocation strategy can be used in place of these complex strategies

Published in:

Parallel and Distributed Systems, IEEE Transactions on  (Volume:5 ,  Issue: 5 )