Continuous-time Markov chains (CTMCs) have been widely used to determine system performance and dependability characteristics. Their analysis most often concerns the computation of steady-state and transient-state probabilities. This paper introduces a branching temporal logic for expressing real-time probabilistic properties on CTMCs and presents approximate model checking algorithms for this logic. The logic, an extension of the continuous stochastic logic CSL of Aziz et al., contains a time-bounded until operator to express probabilistic timing properties over paths as well as an operator to express steady-state probabilities. We show that the model checking problem for this logic reduces to a system of linear equations (for unbounded until and the steady-state operator) and a Volterra integral equation system (for time-bounded until). We then show that the problem of model-checking timebounded until properties can be reduced to the problem of computing transient state probabilities for CTMCs. This allows the verification of probabilistic timing properties by efficient techniques for transient analysis for CTMCs such as uniformization. Finally, we show that a variant of lumping equivalence (bisimulation), a well-known notion for aggregating CTMCs, preserves the validity of all formulas in the logic.

. The verification of continuous-time Markov chains (CTMCs) against continuous stochastic logic (CSL) [3, 6], a stochastic branchingtime temporal logic, is considered. CSL facilitates among others the specification of steady-state properties and the specification of probabilistic timing properties of the form P# #p(#1 U I #2 ), for state formulas #1 and #2 , comparison operator ##, probability p, and real interval I. The main result of this paper is that model checking probabilistic timing properties can be reduced to the problem of computing transient state probabilities for CTMCs. This allows us to verify such properties by using e#cient techniques for transient analysis of CTMCs such as uniformisation. A second result is that a variant of ordinary lumping equivalence (i.e., bisimulation), a well-known notion for aggregating CTMCs, preserves the validity of all CSL-formulas. In 12th Annual Symposium on Computer Aided Verification, CAV 2000, c # Springer-Verlag 2000 Chicago,...

. Markov chains are widely used in the context of performance and reliability evaluation of systems of various nature. Model checking of such chains with respect to a given (branching) temporal logic formula has been proposed for both the discrete [17, 6] and the continuous time setting [4, 8]. In this paper, we describe a prototype model checker for discrete and continuous-time Markov chains, the Erlangen--Twente Markov Chain Checker (E MC 2 ), where properties are expressed in appropriate extensions of CTL. We illustrate the general benefits of this approach and discuss the structure of the tool. Furthermore we report on first successful applications of the tool to non-trivial examples, highlighting lessons learned during development and application of E T MC 2 . 1 Introduction Markov chains are widely used as simple yet adequate models in diverse areas, ranging from mathematics and computer science to other disciplines such as operations research, industrial engine...

Abstract. Considering functional correctness and performance evaluation in a common framework is desirable, both for scientific and economic reasons. In this paper, we describe how the Cadp toolbox, originally designed for verifying the functional correctness of Lotos specifications, can also be used for performance evaluation. We illustrate the proposed approach by the performance study of the Scsi-2 bus arbitration protocol. 1

. Stochastic process algebra have been proven useful because they allow behaviour-oriented performance and reliability modelling. As opposed to traditional performance modelling techniques, the behaviouroriented style supports composition and abstraction in a natural way. However, analysis of stochastic process algebra models is state-oriented, because standard numerical analysis is typically based on the calculation of (transient and steady) state probabilities. This shift of paradigms hampers the acceptance of the process algebraic approach by performance modellers. In this paper, we develop an entirely behaviour-oriented analysis technique for stochastic process algebra. The key contribution is an action-based temporal logic to describe behaviours-of-interest, together with a model checking algorithm to derive the probability with which a stochastic process algebra model exhibits a given behaviour-of-interest. 1 Introduction The analysis of systems with respect to their performance...

We introduce a rewrite-based specification language for modelling probabilistic concurrent and distributed systems. The language, based on PMaude, has both a rigorous formal basis and the characteristics of a high-level rule-based programming language. Furthermore, we provide tool support for performing discrete-event simulations of models written in PMaude, and for statistically analyzing various quantitative aspects of such models based on the samples that are generated through discrete-event simulation. Because distributed and concurrent communication protocols can be modelled using actors (concurrent objects with asynchronous message passing), we provide an actor PMaude module. The module aids writing specifications in a probabilistic actor formalism. This allows us to easily write specifications that are purely probabilistic – and not just non-deterministic. The absence of such (un-quantified) non-determinism in a probabilistic system is necessary for a form of statistical analysis that we also discuss. Specifically, we introduce a query language called Quantitative Temporal Expressions (or QuaTEx in short), to query various quantitative aspects of a probabilistic model. We also describe a statistical technique to evaluate QuaTEx expressions for a probabilistic model. 1

This paper describes how to employ Multi Terminal Binary Decision Diagrams (MTBDD) for the construction and analysis of a general class of models that exhibit stochastic, probabilistic and non-deterministic behaviour. It is shown how the notorious problem of state space explosion can be circumvented by compositionally constructing symbolic (i.e. MTBDD-based) representations of complex systems from small-scale components. We emphasise, however, that compactness of the representation can only be achieved if heuristics are applied with insight into the structure of the system under investigation. We report on our experiences concerning compact representation, performance analysis and verification of performability properties.

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.

Markov chains (and their extensions with rewards) have been widely used to determine performance, dependability and performability characteristics of computer communication systems, such as throughput, delay, mean time to failure, or the probability to accumulate at least a certain amount of reward in a given time.

This paper presents algorithms and experimental results for model-checking continuous -time Markov chains (CTMCs) based on a structured analysis approach. In this approach, a CTMC is represented as a term in Kronecker algebra that reects the component structure of the system model. Such representations can be obtained in a natural way from various high-level speci cation formalisms, such as stochastic extensions of Petri nets, process algebras or activity networks. Properties are expressed in Continuous Stochastic Logic (CSL) which includes means to express transient, steady-state and path performance measures. This paper describes novel model-checking algorithms for CSL that fully exploit the compositional description of the CTMC. This yields an eective way to combat the state-space explosion problem and enables the model-checking of fairly large Markov chains. Furthermore, we show how state-space aggregation (modulo bisimulation) and the elimination of vanishing states can be done in a component-wise manner. To demonstrate the applicability of the approach, and to assess the eciency of our algorithms, we analyze a stochastic Petri net-model of a workstation cluster system and a simple queueing network.