Practical applications of probabilistic model checking to communication protocols

Probabilistic model checking is a formal verification technique for the analysis of systems that exhibit stochastic behaviour. It has been successfully employed in an extremely wide array of application domains including, for example, communication and multimedia protocols, security and power management. In this chapter we focus on the applicability of these techniques to the analysis of communication protocols. An analysis of the performance of such systems must successfully incorporate several crucial aspects, including concurrency between multiple components, real-time constraints and randomisation. Probabilistic model checking, in particular using probabilistic timed automata, is well suited to such an analysis. We provide an overview of this area, with emphasis on an industrially relevant case study: the IEEE 802.3 (CSMA/CD) protocol. We also discuss two contrasting approaches to the implementation of probabilistic model checking, namely those based on numerical computation and those based on discrete-event simulation. Using results from the two tools PRISM and APMC, we summarise the advantages, disadvantages and trade-offs associated with these techniques

Modeling and Analysis of Probabilistic Real-time Systems through Integrating Event-B and Probabilistic Model Checking

Event-B is a formal method used in the development of safety critical systems. However, these systems may introduce uncertainty, and need also to meet real-time requirements, which make their modeling and analysis a challenging task. Existing works on extending Event-B with probability and time did not address both probability and time in a single framework. Besides, they did focus the most on extending the language itself, not on integrating the extended Event-B with verification. In this paper, we aim to represent both probability and time in the Event-B language, and we will show how such a representation can be automatically translated into Probabilistic Timed Automata (PTA) described in the language of the probabilistic model checker PRISM. This translation would allow us to analyze probabilistic, as well as time-bounded probabilistic reachability properties of probabilistic real-time systems through the Probabilistic Timed CTL (PTCTL) logic

Quantitative Verification: Formal Guarantees for Timeliness, Reliability and Performance

Computerised systems appear in almost all aspects of our daily lives, often in safety-critical scenarios such as embedded control systems in cars and aircraft or medical devices such as pacemakers and sensors. We are thus increasingly reliant on these systems working correctly, despite often operating in unpredictable or unreliable environments. Designers of such devices need ways to guarantee that they will operate in a reliable and efficient manner. Quantitative verification is a technique for analysing quantitative aspects of a system's design, such as timeliness, reliability or performance. It applies formal methods, based on a rigorous analysis of a mathematical model of the system, to automatically prove certain precisely specified properties, e.g. ``the airbag will always deploy within 20 milliseconds after a crash'' or ``the probability of both sensors failing simultaneously is less than 0.001''. The ability to formally guarantee quantitative properties of this kind is beneficial across a wide range of application domains. For example, in safety-critical systems, it may be essential to establish credible bounds on the probability with which certain failures or combinations of failures can occur. In embedded control systems, it is often important to comply with strict constraints on timing or resources. More generally, being able to derive guarantees on precisely specified levels of performance or efficiency is a valuable tool in the design of, for example, wireless networking protocols, robotic systems or power management algorithms, to name but a few. This report gives a short introduction to quantitative verification, focusing in particular on a widely used technique called model checking, and its generalisation to the analysis of quantitative aspects of a system such as timing, probabilistic behaviour or resource usage. The intended audience is industrial designers and developers of systems such as those highlighted above who could benefit from the application of quantitative verification,but lack expertise in formal verification or modelling

Evaluation de la robustesse d'un ordonnancement par Automates Temporisés Stochastiques

National audienceLes modèles et outils des SystèmesSystèmes`Systèmesà Evénéments Discrets (SED) ont montré leur apport et leur efficacité pour la modélisation et la résolution deprobì emes d'ordonnancement dans le domaine des systèmes manufacturiers de production. Leur principal atout réside dans leur capacitécapacité`capacitéà appréhender naturellement les dynamiques sous-jacentes aux ressources de production ainsi que les logiques de configuration des ateliers (Job-shop, Flow-shop, Open-shop, hybrides...). De plus, les extensions stochastiques des modèles de SED offrent d'intéressantes perspectives pour la prise en compte de l'incertain en ordonnancement : incertitudes sur les ressources (durée opératoires, aléas de fonctionnement, pannes...) mais aussi sur la de-mande (variabilité importante des produits, personnalisation de masse...). L'objectif de cet article est de démontrer la faisabilité d'une approche basée sur les automates tem-porisés stochastiques et sur des techniques de model-checking statistique pourévaluerpourévaluer la robustesse d'un ordonnancement facè a des aléas en se restreignant, dans le cadre de cettécetté etude, aux incertitudes sur les durées opératoires