Several probabilistic simulation relations for probabilistic systems are defined and evaluated according to two criteria: compositionality and preservation of "interesting" properties. Here, the interesting properties of a system are identified with those that are expressible in an untimed version of the Timed Probabilistic concurrent Computation Tree Logic (TPCTL) of Hansson. The definitions are made, and the evaluations carried out, in terms of a general labeled transition system model for concurrent probabilistic computation. The results cover weak simulations, which abstract from internal computation, as well as strong simulations, which do not.

This paper surveys the theoretical developments in the field of stochastic process algebras, process algebras where action occurrences may be subject to a delay that is determined by a random variable. A huge class of resource-sharing systems --- like large-scale computers, client-server architectures, networks --- can accurately be described using such stochastic specification formalisms.

The notion of process equivalence of probabilistic processes is sensitive to the exact probabilities of transitions. Thus, a slight change in the transition probabilities will result in two equivalent processes being deemed no longer equivalent. This instability is due to the quantitative nature of probabilistic processes. In a situation where the process behaviour has a quantitative aspect there should be a more robust approach to process equivalence. This paper studies a metric between labelled Markov processes. This metric has the property that processes are at zero distance if and only if they are bisimilar. The metric is inspired by earlier work on logics for characterizing bisimulation and is related, in spirit, to the Hutchinson metric.

We survey various notions of probabilistic automata and probabilistic bisimulation, accumulating in an expressiveness hierarchy of probabilistic system types. The aim of this paper is twofold: On the one hand it provides an overview of existing types of probabilistic systems and, on the other hand, it explains the relationship between these models.

. In this paper we study the extension of classical testing theory to a probabilistic process algebra. We consider a generative interpretation of probabilities for a language with two choice operators (one internal and the other external), which are annotated with a probability p 2 (0; 1). We define a testing semantics for our language, and we write P passp T to denote that the process P passes the test T with a probability p. We also give a set of essential tests which has the same strength as the full family of tests. Next we give an alternative characterization of the testing semantics, based on the idea of acceptance sets, and we prove that the new equivalence is equal to the testing equivalence. Finally, we present a fully abstract denotational semantics based on acceptance trees. 1 Introduction During the last years there has been a great activity devoted to the study of time and probabilistic extensions of concurrent processes. These extensions are very adequate for the spe...

We present a randomized self-stabilizing leader election protocol and a randomized self-stabilizing token circulation protocol under an arbitrary scheduler on anonymous and unidirectional rings of any size. These protocols are space optimal. We also give a formal and complete proof of these protocols.

We propose decision algorithms for bisimulation relations de ned on probabilistic automata, a model for concurrent nondeterministic systems with randomization. The algorithms decide both strong and weak bisimulation relations based on deterministic as well as randomized schedulers. These algorithms extend and complete other known algorithms for simpler relations and models. The algorithm we present for strong probabilistic bisimulation has polynomial time complexity, while the algorithm for weak probabilistic bisimulation is exponential; however we argue that the latter is feasible in practice.

A self-stabilizing algorithm, regardless of the initial system state, converges in finite time to a set of states that satisfy a legitimacy predicate without the need for explicit...

This work focuses on self-stabilizing algorithms for mutual exclusion and leader election -- two fundamental tasks for distributed systems. Self-stabilizing systems are able to recover by themselves, regaining their consistency from any initial or intermediary faulty configuration.

This paper provides an elementary introduction to the probabilistic automaton (PA) model, which has been developed by Segala. We describe how distributed systems with discrete probabilities can be modeled and analyzed by means of PAs. We explain how the basic concepts for the analysis of nonprobabilistic automata can be extended to probabilistic systems. In particular, we treat the parallel composition operator on PAs, the semantics of a PA as a set of trace distributions, an extension of the PA model with time and simulation relations for PAs. Finally, we give an overview of various other state based models that are used for the analysis of probabilistic systems.