By Topic

Performance modeling in industry: a case study on storage virtualization

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
$33 $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)
Nikolaus Huber ; Karlsruhe Institute of Technology, IPD, Karlsruhe, Germany ; Steffen Becker ; Christoph Rathfelder ; Jochen Schweflinghaus
more authors

In software engineering, performance and the integration of performance analysis methodologies gain increasing importance, especially for complex systems. Well-developed methods and tools can predict non-functional performance properties like response time or resource utilization in early design stages, thus promising time and cost savings. However, as performance modeling and performance prediction is still a young research area, the methods are not yet well-established and in wide-spread industrial use. This work is a case study of the applicability of the Palladio Component Model as a performance prediction method in an industrial environment. We model and analyze different design alternatives for storage virtualization on an IBM* system. The model calibration, validation and evaluation is based on data measured on a System z9* as a proof of concept. The results show that performance predictions can identify performance bottlenecks and evaluate design alternatives in early stages of system development. The experiences gained were that performance modeling helps to understand and analyze a system. Hence, this case study substantiates that performance modeling is applicable in industry and a valuable method for evaluating design decisions.

Published in:

2010 ACM/IEEE 32nd International Conference on Software Engineering  (Volume:2 )

Date of Conference:

2-8 May 2010