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

An Integrated Data-Driven Framework for Computing System Management

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

5 Author(s)
Tao Li ; Sch. of Comput. Sci., Florida Int. Univ., Miami, FL, USA ; Wei Peng ; Perng, C. ; Sheng Ma
more authors

With advancement in science and technology, computing systems are becoming increasingly more complex with a growing number of heterogeneous software and hardware components. They are thus becoming more difficult to monitor, manage, and maintain. Traditional approaches to system management have been largely based on domain experts through a knowledge acquisition solution that translates domain knowledge into operating rules and policies. This process has been well known as cumbersome, labor intensive, and error prone. In addition, traditional approaches for system management are difficult to keep up with the rapidly changing environments. There is a pressing need for automatic and efficient approaches to monitor and manage complex computing systems. In this paper, we propose an integrated data-driven framework for computing system management by acquiring the needed knowledge automatically from a large amount of historical log data. Specifically, we apply text mining techniques to automatically categorize the log messages into a set of canonical categories, incorporate temporal information to improve categorization performance, develop temporal mining techniques to discover the relationships between different events, and take a novel approach called event summarization to provide a concise interpretation of the temporal patterns.

Published in:

Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on  (Volume:40 ,  Issue: 1 )

Date of Publication:

Jan. 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.