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

Dynamic Black-Box Performance Model Estimation for Self-Tuning Regulators

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

2 Author(s)
Karlsson, M. ; HP Labs, Palo Alto, CA ; Covell, M.

Methods for automatically managing the performance of computing services must estimate a performance model of that service. This paper explores properties that are necessary for performance model estimation of black-box computer systems when used together with adaptive feedback loops. It shows that the standard method of least-squares estimation often gives rise to models that make the control loop perform the opposite action of what is desired. This produces large oscillations and bad tracking performance. The paper evaluates what combination of input and output data provides models with the best properties for the control loop. Plus, it proposes three extensions to the controller that makes it perform well, even when the model estimated would have degraded performance. Our proposed techniques are evaluated with an adaptive controller that provides latency targets for workloads on black-box computer services under a variety of conditions. The techniques are evaluated on two systems: a three-tier e-commerce site and a Web server. Experimental results show that our best estimation approach improves the ability of the controller to meet the latency goals significantly. Previously oscillating workload latencies are with our techniques smooth around the latency targets

Published in:

Autonomic Computing, 2005. ICAC 2005. Proceedings. Second International Conference on

Date of Conference:

13-16 June 2005

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.