TY  - JOUR
SP  - 866
KW  - performance modelling; stochastic process algebra; PEPA;   compositional modelling; continuous approximation

ID  - eprints2600
JF  - The Computer Journal
Y1  - 2012/09//
AV  - none
UR  - http://dx.doi.org/10.1093/comjnl/bxr094
IS  - 7
VL  - 55
SN  - 0010-4620
PB  - Oxford University Press
A1  - Hillston, Jane
A1  - Tribastone, Mirco
A1  - Gilmore, Stephen
EP  - 881
N2  - In this paper we report on progress in the use of stochastic process algebras for representing systems which contain many replications of components such as clients, servers and devices. Such systems have traditionally been difficult to analyse even when using high-level models because of the need to represent the vast range of their potential behaviour. Models of concurrent systems with many components very quickly exceed the storage capacity of computing devices even when efficient data structures are used to minimize the cost of representing each state. Here, we show how population-based models that make use of a continuous approximation of the discrete behaviour can be used to efficiently analyse the temporal behaviour of very large systems via their collective dynamics. This approach enables modellers to study problems that cannot be tackled with traditional discrete-state techniques such as continuous-time Markov chains. 
TI  - Stochastic process algebras: from individuals to populations
ER  -