Andrea Marin

In the last 15 years, several research efforts have been directed towards the representation and the analysis of metabolic pathways by using Petri nets. The goal of this paper is twofold. First, we discuss how the knowledge about metabolic pathways can be represented with Petri nets. We point out the main problems that arise in the construction of a Petri net model of a metabolic pathway and we outline some solutions proposed in the literature. Second, we present a comprehensive review of recent research on this topic, in order to assess the maturity of the field and the availability of a methodology for modelling a metabolic pathway by a corresponding Petri net.

Abstract In this paper we study product-form conditions for generalized stochastic Petri net models. We base our results on the reversed compound agent theorem (RCAT) that has been recently formulated in the stochastic process algebra research field. In previous ...

This book constitutes the refereed proceedings of the 17th International Conference on Analytical and Stochastic Modeling Techniques and Applications, ASMTA 2010, held in Cardiff, UK, in June 2010. The 28 revised full papers presented were carefully reviewed and selected from numerous submissions for inclusion in the book. The papers are organized in topical sections on queueing theory, specification languages and tools, telecommunication systems, estimation, prediction, and stochastic modelling.

In the last few years some novel approaches have been developed to analyse Markovian stochastic models with product-form solutions. In particular RCAT [4] has proved to be a very powerful result capable to derive most of the well-known product-forms previously formulated in queueing theory or stochastic Petri net analysis contexts as well as new ones. The main idea is to define a joint-process as a cooperation among a set of models and give the condition for and the expression of the equilibrium probability distribution of the joint-states as product of the equilibrium distributions of each model considered in isolation. This paper aims to formulate an approach to deal with models whose transition rates depend on the resulting joint-states. In practice, we extend what has been introduced to solve the same problem for queueing networks [8,9] and stochastic Petri nets [5]. However, since RCAT is more general than the results that are derived for a specific model, we show that some conditions on the transition rate specification that are not present in the original formulation arise. Several examples are given to point out the application of this result and strength the intuition about the implications of the formulated conditions.

The purpose of this tutorial is to survey queueing networks, a class of stochastic models extensively applied to represent and analyze resource sharing systems such as communication and computer systems. Queueing networks (QNs) have been proved to be a powerful and versatile tool for system performance evaluation and prediction. First we briefly survey QNs that consist of a single service center, i.e., the basic queueing systems defined under various hypotheses, and we discuss their analysis to evaluate a set of performance indices, such as resource utilization and throughput and customer response time. Their solution is based on the introduction of an underlying stochastic Markov process. Then, we introduce QNs that consist of a set of service centers representing the system resources that provide service to a collection of customers that represent the users. Various types of customers define the customers classes in the network that are gathered in chains. We consider various analytical methods to analyze QNs with single-class and multiple-class. We mostly focus on product-form QNs that have a simple closed form expression of the stationary state distribution that allows to define efficient algorithms to evaluate average performance measures. We review the basic results, stating from the BCMP theorem that defines a large class of product-form QNs, and we present the main solution algorithms for single-class e multiple-class QNs. We discuss some interesting properties of QNs including the arrival theorem, exact aggregation and insensitivity. Finally, we discuss some particular models of product-form QNs that allow to represent special system features such as state-dependent routing, negative customers, customers batch arrivals and departures and finite capacity queues. The class of QN models is illustrated through some application examples of to analyze computer and communication systems.

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 Muntz’s property \(M\Rightarrow M\) and prove that the RANDOM is the only non-preemptive, non-priority, probabilistic discipline that fulfils the \(M\Rightarrow M\) 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.

Markovian models play a pivotal role in system performance evaluation field. Several high level formalisms are capable to model systems consisting of some interacting sub-models, but often the resulting underlying process has a number of states that makes the computation of the solution unfeasible. Product-form models consist of a set of interacting sub-models and have the property that their steady-state solution is the product of the sub-model solutions considered in isolation and opportunely parametrised. The computation of the steady-state solution of a composition of arbitrary and possibly different types of models in product-form is still an open problem. It consists of two parts: a) deciding whether the model is in product-form and b) in this case, compute the stationary distribution efficiently. In this paper we propose an algorithm to solve these problems that extends that proposed in [14] by allowing the sub-models to have infinite state spaces. This is done without a-priori knowledge of the structure of the stochastic processes underlying the model components. As a consequence, open models consisting of non homogeneous components having infinite state space (e.g., a composition of G-queues, G-queues with catastrophes, Stochastic Petri Nets with product-forms) may be modelled and efficiently studied.

Abstract Performance evaluation of systems including, among others, components based on wireless links plays an important role both in the software and hardware engineering. In both cases the earlier evaluation of performance characteristics has been proved to be a key-...