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

Prediction f based models for evaluating backfilling scheduling policies

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)
Guim, F. ; Barcelona Supercomput. Center, Barcelona ; Corbalan, J. ; Labarta, J.

The research on the usage of prediction techniques in HPC scheduling policies rather than user estimates has increased it relevance these recent years. In the coming scheduling architectures, like grids and very heterogeneous computational resources, such techniques are having a crucial relevance due to users in most of the cases will not have enough information or enough skills for specify for how long will their jobs run. Many studies have analyzed the impact of the user runtime estimates accuracy in the performance of the scheduling policies. Using user runtime estimation models, such as the f-model, researchers have evaluated how the accuracy of the runtime estimates provided by the user at the job submission can affect the performance of the backfilling policies and its variants. However, these traditional estimation models can not applied to backfilling scheduling policies that use runtime predictions rather than user estimates. Clearly, predictions can not be characterized with these models. For instance because the underestimation of the runtime is not considered by them and obviously it can occurs. In this paper we describe and evaluate a set of f-model based prediction models that characterize the behavior that prediction techniques have shown in HPC centers. They have been designed for evaluate scheduling policies that use predictions rather than user estimates.

Published in:

Parallel and Distributed Computing, Applications and Technologies, 2007. PDCAT '07. Eighth International Conference on

Date of Conference:

3-6 Dec. 2007

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.