Cart (Loading....) | Create Account
Close category search window
 

Optimal schedules for cycle-stealing in a network of workstations with a bag-of-tasks workload

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

1 Author(s)
Rosenberg, A.L. ; Dept. of Comput. Sci., Massachusetts Univ., Amherst, MA, USA

We refine the model underlying our prior work on scheduling bag-of-tasks ("embarrassingly parallel") workloads via cycle-stealing in networks of workstations (S.N. Bhatt et al., 1997; A.L. Rosenberg, 1999), obtaining a model wherein the scheduling guidelines of Rosenberg produce optimal schedules for every such cycle-stealing opportunity. We thereby render prescriptive the descriptive model of those sources. Although computing optimal schedules usually requires the use of general function-optimizing methods, we show how to compute optimal schedules efficiently for the broad class of opportunities whose durations come from a concave probability distribution. Even when no such efficient computation of an optimal schedule is available, our refined model often suggests a natural notion of approximately optimal schedule, which may be efficiently computable. We illustrate such efficient approximability via the important class of cycle-stealing opportunities whose durations come from a heavy-tailed distribution. Such opportunities do not admit any optimal schedule, nor even a natural notion of approximately optimal schedule, within the model of Bhatt and Rosenberg. Within our refined model, though, we derive computationally simple schedules for heavy-tailed opportunities, which can be "tuned" to accomplish an expected amount of work that is arbitrarily close to optimal

Published in:

Parallel and Distributed Systems, IEEE Transactions on  (Volume:13 ,  Issue: 2 )

Date of Publication:

Feb 2002

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.