Research Article
An Aggregation Technique for Large-Scale PEPA Models with Non-Uniform Populations
@INPROCEEDINGS{10.4108/icst.valuetools.2013.254397, author={Jane Hillston and Alireza Pourranjbar}, title={An Aggregation Technique for Large-Scale PEPA Models with Non-Uniform Populations}, proceedings={7th International Conference on Performance Evaluation Methodologies and Tools}, publisher={ICST}, proceedings_a={VALUETOOLS}, year={2014}, month={1}, keywords={pepa aggregation population models markov process modelling}, doi={10.4108/icst.valuetools.2013.254397} }- Jane Hillston
Alireza Pourranjbar
Year: 2014
An Aggregation Technique for Large-Scale PEPA Models with Non-Uniform Populations
VALUETOOLS
ACM
DOI: 10.4108/icst.valuetools.2013.254397
Abstract
Performance analysis based on modelling consists of two major steps: model construction and model analysis. Formal modelling techniques significantly aid model construction but can exacerbate model analysis. In particular, here we consider the analysis of large-scale systems which consist of one or more entities replicated many times to form large populations. The replication of entities in such models can cause their state spaces to grow exponentially to the extent that their exact stochastic analysis becomes computationally expensive or even infeasible.
In this paper, we propose a new approximate aggregation algorithm for a class of large-scale PEPA models. For a given model, the method quickly checks if it satisfies a syntactic condition, indicating that the model may be solved approximately with high accuracy. If so, an aggregated CTMC is generated directly from the model description. This CTMC can be used for efficient derivation of an approximate marginal probability distribution over some of the model's populations. In the context of a large-scale client-server system, we demonstrate the usefulness of our method.