Performance by unified model analysis (PUMA)

Evaluation of non-functional properties of a design (such as performance, dependability, security, etc.) can be enabled by design annotations specific to the property to be evaluated. Performance properties, for instance, can be annotated on UML designs by using the "UML Profile for Schedulability, Performance and Time (SPT)". However the communication between the design description in UML and the tools used for non-functional properties evaluation requires support, particularly for performance where there are many alternative performance analysis tools that might be applied. This paper describes a tool architecture called PUMA, which provides a unified interface between different kinds of design information and different kinds of performance models, for example Markov models, stochastic Petri nets and process algebras, queues and layered queues.The paper concentrates on the creation of performance models. The unified interface of PUMA is centered on an intermediate model called Core Scenario Model (CSM), which is extracted from the annotated design model. Experience shows that CSM is also necessary for cleaning and auditing the design information, and providing default interpretations in case it is incomplete, before creating a performance model.

[1]  Raffaela Mirandola,et al.  Deriving a queueing network based performance model from UML diagrams , 2000, WOSP '00.

[2]  C. Murray Woodside,et al.  Performance analysis of distributed server systems , 2000 .

[3]  C. Murray Woodside,et al.  Software performance models from system scenarios , 2005, Perform. Evaluation.

[4]  Dorina C. Petriu,et al.  Software Performance Models from System Scenarios in Use Case Maps , 2002, Computer Performance Evaluation / TOOLS.

[5]  Javier Campos,et al.  From UML activity diagrams to Stochastic Petri nets: application to software performance engineering , 2004, WOSP '04.

[6]  Dorina C. Petriu,et al.  Performance Analysis with UML , 2003, UML for Real.

[7]  Moreno Marzolla,et al.  Simulation Modeling of UML Software Architectures , 2003 .

[8]  Ray Jain,et al.  The art of computer systems performance analysis - techniques for experimental design, measurement, simulation, and modeling , 1991, Wiley professional computing.

[9]  Dorina C. Petriu,et al.  Applying the UML Performance Profile: Graph Grammar-Based Derivation of LQN Models from UML Specifications , 2002, Computer Performance Evaluation / TOOLS.

[10]  Marco Ajmone Marsan,et al.  Modelling with Generalized Stochastic Petri Nets , 1995, PERV.

[11]  Connie U. Smith,et al.  Performance Engineering of Software Systems , 1990, SIGMETRICS Perform. Evaluation Rev..

[12]  Jing Xu,et al.  Performance Analysis of a Software Design Using the UML Profile for Schedulability, Performance, and Time , 2003, Computer Performance Evaluation / TOOLS.

[13]  López-GraoJuan Pablo,et al.  From UML activity diagrams to Stochastic Petri nets , 2004 .

[14]  Susanna Donatelli,et al.  From UML sequence diagrams and statecharts to analysable petri net models , 2002, WOSP '02.

[15]  C. Murray Woodside,et al.  A Metamodel for Generating Performance Models from UML Designs , 2004, UML.

[16]  Giuliana Franceschinis,et al.  The PSR Methodology: Integrating Hardware and Software Models , 1996, Application and Theory of Petri Nets.

[17]  C. U. Smith,et al.  Performance model interchange format (PMIF 2.0): XML definition and implementation , 2004 .

[18]  Stephen Gilmore,et al.  Analysing UML 2.0 activity diagrams in the software performance engineering process , 2004, WOSP '04.