# LFCS Seminar: Richard Hayden

Scalable Performance Analysis of Massively Parallel Stochastic Systems

Performance analysis has always suffered from the state-space explosion

problem which directly prohibits the scalability of stochastic modelling

as a tool for resolving resource provisioning and quality of service

questions in massively parallel computer and communication systems. This

is especially true when applied to the recent ubiquitous breed of

distributed and peer-to-peer systems.

One way around these scalability limitations are asymptotic techniques

formally justified by functional laws of large numbers often termed

variously ``fluid'' or ``mean-field'' analysis. These techniques have

their roots in classical heavy-traffic analysis in the context of

queueing networks, and also borrow from ideas in chemistry and biology.

Such approaches have recently experienced something of a revival in the

context of general massive interacting computational systems such as

might be specified formally using a stochastic process algebra or

stochastic Petri nets.

In this talk, we will introduce these approaches and showcase some of

the methods and the results which can be obtained. Furthermore, we will

introduce the freely available Grouped PEPA Analyser (GPA) tool which

provides efficient implementation of a wide range of these techniques.