Performance Model Solution

In the process of software performance modeling and analysis, although these two activities do not act in a strict pipeline, once generated/built (at whatever level of abstraction in the software lifecycle) a performance model has to be solved to get the values of performance indices of interest. It is helpful to recall here that the main targets of a performance model solution are the values of performance indices. The existing literature is rich of methodologies, techniques and tools for solving a wide variety of performance models. This is a very active research topic and, despite the complexity of problems encountered in this direction, in the last few decades very promising results have been obtained. Moreover, new tools have been developed to support this key step of software performance process. Therefore, the contents of this chapter are not limited to the basics of model solution techniques, and a short summary of the major tools for model solution is also provided.

[1]  Stephen Gilmore,et al.  The PEPA Workbench: A Tool to Support a Process Algebra-based Approach to Performance Modelling , 1994, Computer Performance Evaluation.

[2]  Christel Baier,et al.  Validation of Stochastic Systems: A Guide to Current Research (Lecture Notes in Computer Science) , 2004 .

[3]  Matteo Sereno.  Towards a Product Form Solution for Stochastic Process Algebras , 1995, Comput. J..

[4]  Roland N. Ibbett,et al.  Technical note: a hierarchical computer architecture design and simulation environment , 1998, TOMC.

[5]  Holger Hermanns,et al.  Compositional performance modelling with the TIPPtool , 2000, Perform. Evaluation.

[6]  Connie U. Smith,et al.  New Book - Performance Solutions: A Practical Guide to Creating Responsive, Scalable Software , 2001, Int. CMG Conference.

[7]  Edward D. Lazowska,et al.  Quantitative system performance - computer system analysis using queueing network models , 1983, Int. CMG Conference.

[8]  John G. Kemeny,et al.  Finite Markov chains , 1960 .

[9]  Kishor S. Trivedi.  Probability and Statistics with Reliability, Queuing, and Computer Science Applications , 1984 .

[10]  Gianfranco Balbo,et al.  Combining Queueing Networks and Generalized Stochastic Petri Nets for the Solution of Complex Models of System Behavior , 1988, IEEE Trans. Computers.

[11]  Jane Hillston,et al.  Product Form Solution for a Class of PEPA Models , 1999, Perform. Evaluation.

[12]  W. D. Ray,et al.  Stochastic Models: An Algorithmic Approach , 1995 .

[13]  Terry Williams,et al.  Probability and Statistics with Reliability, Queueing and Computer Science Applications , 1983 .

[14]  Giovanni Chiola,et al.  GreatSPN 1.7: Graphical Editor and Analyzer for Timed and Stochastic Petri Nets , 1995, Perform. Evaluation.

[15]  R. J. Pooley.  An Introduction to Programming in Simula , 1987 .

[16]  J. Banks,et al.  Discrete-Event System Simulation , 1995 .

[17]  Prashant J. Shenoy,et al.  Chameleon: Application-Level Power Management , 2008, IEEE Transactions on Mobile Computing.

[18]  Peter G. Harrison,et al.  Exploiting Quasi-reversible Structures in Markovian Process Algebra Models , 1995, Comput. J..

[19]  Marco Bernardo.  Twotowers 3.0: enhancing usability , 2003, 11th IEEE/ACM International Symposium on Modeling, Analysis and Simulation of Computer Telecommunications Systems, 2003. MASCOTS 2003..

[20]  Simonetta Balsamo,et al.  Combining stochastic process algebras and queueing networks for software architecture analysis , 2002, WOSP '02.