Extracting passage times from PEPA models with the HYDRA tool: a case study

Passage time densities and quantiles are important performance metrics which are increasingly used in specifying service level agreements (SLAs) and benchmarks. PEPA is a popular stochastic process algebra and a powerful formalism for describing performance models of communication and computer systems. We present a case study passage time analysis of an 82,944 state PEPA model using the HYDRA tool. HYDRA specialises in passage time analysis of large Markov systems based on stochastic Petri nets. By using the new Imperial PEPA compiler (ipc), we can construct a HYDRA model from a PEPA model and obtain passage time densities based on the original PEPA description.

[1]  J. Hillston.  Compositional Markovian Modelling Using a Process Algebra , 1995 .

[2]  William J. Knottenbelt,et al.  Generalised Markovian analysis of timed transition systems , 1996 .

[3]  Micha Yadin,et al.  Randomization Procedures in the Computation of Cumulative-Time Distributions over Discrete State Markov Processes , 1984, Oper. Res..

[4]  Peter G. Harrison,et al.  Passage time distributions in large Markov chains , 2002, SIGMETRICS '02.

[5]  Kishor S. Trivedi,et al.  Numerical transient analysis of markov models , 1988, Comput. Oper. Res..

[6]  Peter G. Harrison,et al.  HYDRA: HYpergraph-Based Distributed Response-Time Analyzer , 2003, PDPTA.

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

[8]  William John Knottenbelt,et al.  Parallel performance analysis of large Markov models , 1999 .

[9]  Stephen Gilmore,et al.  Experiences with the PEPA performance modelling , 1999, IEE Proc. Softw..

[10]  Stephen Gilmore,et al.  Specifying Performance Measures for PEPA , 1999, ARTS.

[11]  Peter G. Harrison,et al.  Performance queries on semi-Markov stochastic Petri nets with an extended continuous stochastic logic , 2003, 10th International Workshop on Petri Nets and Performance Models, 2003. Proceedings..

[12]  Stephen Gilmore,et al.  Experiences with the PEPA performance modelling , 1999, IEE Proc. Softw..

[13]  Winfried K. Grassmann,et al.  Means and variances of time averages in Markovian environments , 1987 .

[14]  Jane Hillston,et al.  A compositional approach to performance modelling , 1996 .