ScienceDirect® Home Skip Main Navigation Links
You have guest access to ScienceDirect. Find out more.
 
Home
Browse
My Settings
Alerts
Help
 Quick Search
 Search tips (Opens new window)
    Clear all fields    
advertisementadvertisement
Computers & Operations Research
Volume 15, Issue 6, 1988, Pages 489-496
 
Font Size: Decrease Font Size  Increase Font Size
 Abstract - selected
Purchase PDF (806 K)

  E-mail Article   
  Add to my Quick Links   
Bookmark and share in 2collab (opens in new window)
Request permission to reuse this article
  Cited By in Scopus (0)
 
 
 
Related Articles in ScienceDirect
View More Related Articles
 
Special issue
View Record in Scopus
 
doi:10.1016/0305-0548(88)90045-7    How to Cite or Link Using DOI (Opens New Window)
Copyright © 1988 Published by Elsevier Science Ltd.

A compumetrical approach for analysis and clustering of computer system performance variables*1

Niv Ahituva, , Yoav Benjaminib, and Magid Igbariac, §

a Computers and Information Systems Program, Faculty of Management, Tel Aviv University, Tel Aviv 69978, Israel b Department of Statistics, Tel Aviv University, Tel Aviv 69978, Israel c Department of Management and Organizational Sciences, College of Business and Administration, Drexel University, Philadelphia, PA 19104, U.S.A.

Available online 16 May 2003.

Purchase the full-text article



References and further reading may be available for this article. To view references and further reading you must purchase this article.

Abstract

Various statistical models have been constructed for analyzing the workload variables of a computer system, but most of these models fail to analyze each variable separately and identify job groups by hardware consumption patterns. In this paper we propose a compumetrical approach to analyze the computer system performance variables and to cluster the jobs into homogeneous groups. It involves using univariable and multivariable analysis and graphical methods for analyzing the variables. This approach enables us to explore data thoroughly, to look for patterns and clusters, to confirm or disprove the expected hardware consumption, and to discover new phenomena.

Article Outline

• References

 
Home
Browse
My Settings
Alerts
Help
Elsevier.com (Opens new window)
About ScienceDirect  |  Contact Us  |  Information for Advertisers  |  Terms & Conditions  |  Privacy Policy
Copyright © 2008 Elsevier B.V. All rights reserved. ScienceDirect® is a registered trademark of Elsevier B.V.