Package org.palladiosimulator.solver.transformations.pcm2regex

Class Pcm2RegExStrategy

java.lang.Object
org.palladiosimulator.solver.transformations.pcm2regex.Pcm2RegExStrategy
All Implemented Interfaces:
SolverStrategy
public class Pcm2RegExStrategy extends Object implements SolverStrategy
This is an excerpt of Heiko's dissertation (see below for link) The Stochastic Regular Expression (SRE) model is an analytical performance model in the class of semi-Markov processes [Tri01]. It consists of a discrete time Markov-chain (DTMC) to model state transitions, but the sojourn time in each state can follow arbitrary probability distributions instead of being limited to exponential distributions as in Markov chains. Furthermore, SREs are hierarchically structured and do not allow cycles in the embedded DTMC for more accurate predictions. Chapter 6.3.3 will provide the syntax and semantics of SREs, afterwards Chapter 6.3.4 shows how to compute overall sojourn times with SREs. Only a partial transformation of PCM instances to SREs is possible, because of the model's limited expressiveness. The transformation is straight-forward, as the control flow modelling of PCM instances and SREs are closely aligned. Chapter 6.3.5 will describe the transformation PCM2SRE. While allowing accurate predictions by supporting arbitrary distribution functions for timing values, SRE are limited to analysing single-user scenarios. They do not include queues or control flow forks, and cannot express contention effects due to concurrent requests. However, they provide a fast method of producing performance predictions during early development stages, as they are usually more quickly solved than running a simulation. Chapter 6.3.6 discusses the assumptions underlying SREs in detail. The SRE model will be used for a performance prediction in a case study in Chapter 7.3.3.