ACM Home Page
Please provide us with feedback. Feedback
Automatic generation of layered queuing software performance models from commonly available traces
Full text PdfPdf (385 KB)
Source Workshop on Software and Performance archive
Proceedings of the 5th international workshop on Software and performance table of contents
Palma, Illes Balears, Spain
Pages: 147 - 158  
Year of Publication: 2005
ISBN:1-59593-087-6
Authors
Tauseef A. Israr  IBM Canada, Ottawa, Canada
Danny H. Lau  Carleton University, Ottawa, Canada
Greg Franks  Carleton University, Ottawa, Canada
Murray Woodside  Carleton University, Ottawa, Canada
Sponsors
SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
SIGSOFT: ACM Special Interest Group on Software Engineering
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 7,   Downloads (12 Months): 47,   Citation Count: 2
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues  

Tools and Actions: Review this Article  
Save this Article to a Binder    Display Formats: BibTex  EndNote ACM Ref   
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1071021.1071037
What is a DOI?

ABSTRACT

Performance models of software designs can give early warnings of problems such as resource saturation or excessive delays. However models are seldom used because of the considerable effort needed to construct them. Software Architecture and Model Extraction (SAME) is a lightweight model building technique that extracts communication patterns from executable designs or prototypes that use message passing, to develop a Layered Queuing Network model in an automated fashion. It is a formal, traceable model building process. The transformation follows a series of well-defined transformation steps, from input domain, (an executable software design or the implementation of software itself) to output domain, a Layered Queuing Network (LQN) Performance model. The SAME technique is appropriate for a message passing distributed system where tasks interact by point-to-point communication. With SAME, the performance analyst can focus on the principles of software performance analysis rather than model building.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

1
 
2
 
3
G. Franks, S. Majumdar, J. Neilson, D. Petriu, J. Rolia, and M. Woodside. Performance Analysis of Distributed Server Systems, Proc. Sixth International Conference on Software Quality (6ICSQ), Ottawa, Ontario, 1996, pp. 15--26.
 
4
F. Hayes-Roth, and D. Waterman. Principles of pattern-directed interface systems. In D. Waterman and F. Hayes-Roth, editors, Pattern-Distributed Inference Systems, pages 577--601. Academic Press, 1978.
 
5
 
6
 
7
IBM, IBM Rational PurifyPlus for Windows: Quantify Component, http://www.pts.com/wp2292.cfm?det=y
 
8
T. A. Israr. Lightweight Technique for Extracting Software Architecture and Performance Models from Traces. Master's Thesis, Carleton University, 2001.
 
9
R. Jain, The Art of Computer Systems Performance Analysis. John Wiley & Sons Inc., 1991
 
10
R. Klar, A. Quick, F. Soetz, "Tools for a Model-driven Instrumentation for Monitoring", Proc. 5th Int. Conf. on Modeling Techniques and Tools for Computer Performance Evaluation (TOOLS91), pp. 165--180 Elsevier, 1992.
11
 
12
 
13
Object Management Group, UML Profile for Schedulability, Performance, and Time Specification, OMG Adopted Specification ptc/02-03-02, July 1, 2002.
 
14
The Open Group, Systems Management: Application Response Measurement (ARM) API, Technical Standard, July 1998.
 
15
 
16
D. A. Reed, R. A. Aydt. R. J. Noe, P. C. Roth, K. A. Shields, B. Schwartz, and L. F. Tavera, Scalable Performance Analysis: The Pablo Performance Analysis Environment, Proc Scalable Parallel Libraries Conference, Starkville, MS, Oct. 1993, IEEE Computer Society Press, pp. 104--113
 
17
 
18
19
 
20
 
21
 
22
C. U. Smith and L. G. Williams, Performance Solutions. Addison-Wesley, 2002.
 
23


Collaborative Colleagues:
Tauseef A. Israr: colleagues
Danny H. Lau: colleagues
Greg Franks: colleagues
Murray Woodside: colleagues