Maria Simonetta Balsamo

On the characterization of probabilistic queueing disciplines.

Probabilistic queueing disciplines are used for modeling several system behaviors. In particular, under a set of assumptions, it has been proved that if the choice of the customer to serve after a job completion is uniform among the queue population, then the model has a BCMP-like product-form solution. In this paper we address the problem of characterizing the probabilistic queueing disciplines that can be embedded in a BCMP queueing network maintaining the product-form property. We base our result on Muntzs property

On Transforming UML models into performance models

M\Rightarrow M

Deriving Performance Models from Software Architecture Specifications

and prove that the RANDOM is the only non-preemptive, non-priority, probabilistic discipline that fulfils the

A tool for the numerical solution of cooperating Markov chains in product-form

M\Rightarrow M

Towards Performance Evaluation of Mobile Systems in UML

property with a class independent exponential server. Then we observe that the FCFS and RANDOM discipline share the same product-form conditions and a set of relevant performance indices when embedded in a BCMP queueing network. We use a simulator to explore the similarities of these disciplines in non-product-form contexts, i.e., under various non-Poisson arrival processes.