Package org.palladiosimulator.solver.transformations.pcm2lqn

Class Pcm2LqnStrategy

java.lang.Object
org.palladiosimulator.solver.transformations.pcm2lqn.Pcm2LqnStrategy
All Implemented Interfaces:
SolverStrategy
public class Pcm2LqnStrategy extends Object implements SolverStrategy
This is an excerpt of Heiko's dissertation (see below for link) The Layered Queueing Network (LQN) model is a performance model in the class of extended queueing networks. It is a popular model with widespread use [BDIS04]. Like the PCM, it specifically targets analysing the performance of distributed systems. While ordinary queueing networks model software structures only implicitly via resource demands to service centers, LQNs model a system as a layered hierarchy of interacting software entities, which produce demands for the underlying physical resources such as CPUs or hard disks. Therefore, LQNs reflect the structure of distributed systems more naturally than ordinary queueing networks. In particular, they model the routing of jobs in the network more realistically. In the context of this work, a model transformation from PCM instances (with computed context models) to LQNs has been implemented. The transformation offers at least two advantages: First, it enables comparing the concepts of the PCM with concepts of LQNs, which can be considered as a state-of-the-art performance model. Second, the transformation makes the sophisticated analytical solvers and simulation tools for LQNs available to the PCM. Other than SREs, LQNs support concurrent behaviour, different kinds of workloads, asynchronous interactions, and different scheduling strategies. Therefore, it is possible to derive performance metrics such as resource utilizations and throughput from PCM instances, which is not possible with SREs. However, LQNs are restricted to exponential distributions and mean-values analysis as discussed later. The chapter 6.4 in Heiko's dissertation will first provide some background about LQNs and their development in recent years (Chapter 6.4.2). Then, it will describe the syntax and (informal) semantics of LQNs using the LQN meta-model and several examples (Chapter 6.4.3). Chapter 6.4.4 briefly describes two performance solvers for LQNs, before Chapter 6.4.5 presents the mapping from PCM instances to LQN instances. Finally, Chapter 6.4.6 compares the PCM model with the LQN model, as well as the existing PCM solvers with two available LQN solvers.