Performance engineering with product-form models

Abstract

Performance engineering plays a pivotal role in the successful design of software system and the software development process. Stochastic modelling has been widely applied to predict and evaluate or estimate system performance. We consider the specification of models in terms of compositions of simpler components and their efficient solution. Various formalisms or classes of stochastic models have been applied for system performance engineering and evaluation. These formalisms includes queueing networks, Stochastic Petri Nets, and Stochastic Process Algebras. Their dynamic behaviour can be usually represented by an underlying stochastic (Markov) process. For each formalism some classes of product-form models have been identified, starting from the first remarkable results for BCMP queueing networks. For some product-form models various efficient algorithms have been defined. We discuss the problem of identifying and characterize classes of product-form models. We compare the properties of the various modeling formalisms, their solution and the combination of product-form (sub)models into a heterogeneous model. We illustrate the application of product-form stochastic models for system performance engineering with some examples of tools for the solution of heterogeneous models formed by synchronized sub-models, and some practical applications.