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

An Anomaly Detection Framework for Autonomic Management of Compute Cloud Systems

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)
Smith, D. ; Dept. of Comput. Sci. & Eng., New Mexico Inst. of Min. & Technol., NM, USA ; Qiang Guan ; Song Fu

In large-scale compute cloud systems, component failures become norms instead of exceptions. Failure occurrence as well as its impact on system performance and operation costs are becoming an increasingly important concern to system designers and administrators. When a system fails to function properly, health-related data are valuable for troubleshooting. However, it is challenging to effectively detect anomalies from the voluminous amount of noisy, high-dimensional data. The traditional manual approach is time-consuming, error-prone, and not scalable. In this paper, we present an autonomic mechanism for anomaly detection in compute cloud systems. A set of techniques is presented to automatically analyze collected data: data transformation to construct a uniform data format for data analysis, feature extraction to reduce data size, and unsupervised learning to detect the nodes acting differently from others. We evaluate our prototype implementation on an institute-wide compute cloud environment. The results show that our mechanism can effectively detect faulty nodes with high accuracy and low computation overhead.

Published in:

Computer Software and Applications Conference Workshops (COMPSACW), 2010 IEEE 34th Annual

Date of Conference:

19-23 July 2010

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.