Jeremy T. Bradley

TL;DR: This paper shows formally that for a large class of models, this fluid-flow analysis can be directly derived from the stochastic process algebra model as an approximation to the mean number of component types within the model.

TL;DR: This work generates a differential equation-based model of infection based solely on the underlying process description of the infection agent model from a high-level process model expressed in the PEPA process algebra, which extends existing population infection dynamics models of Internet worms by explicitly using frequency-based spread of infection.

TL;DR: In this article, a technique for defining and extracting passage-time densities from high-level stochastic process algebra models is presented, which can process PEPA-specified passage time densities and models by compiling the PEPA model and passage specification into the DNAmaca formalism.

TL;DR: This work shows how fluid-approximation techniques may be used to extract passage-time measures from performance models, focusing on two types of passage measure: passage times involving individual components, as well as passage times which capture the time taken for a population of components to evolve.

TL;DR: This paper describes how soft performance bounds can be expressed for software systems using stochastic probes over Stochastic process algebra models using a regular expression syntax that describes the behaviour that must be observed before a performance measurement can be started or stopped.