Parametric Markov chains occur quite naturally in various applications: they can be used for a conservative analysis of probabilistic systems (no matter how the parameter is chosen, the system works to specification); they can be used to find optimal settings for a parameter; they can be used to visualise the influence of system parameters; and they can be used to make it easy to adjust the analysis for the case that parameters change. Unfortunately, these advancements come at a cost: parametric model checking is-or rather was-often slow. To make the analysis of parametric Markov models scale, we need three ingredients: clever algorithms, the right data structure, and good engineering. Clever algorithms are often the main (or sole) selling point; and we face the trouble that this paper focuses on -the latter ingredients to efficient model checking. Consequently, our easiest claim to fame is in the speedup we have often realised when comparing to the state of the art.
Nature-inspired synchronisation protocols have been widely adopted to achieve consensus within wireless sensor networks. We assess the power consumption of such protocols, particularly the energy required to synchronise all nodes across a network. We use the widely adopted model of bio-inspired, pulse-coupled oscillators to achieve network-wide synchronisation and provide an extended formal model of just such a protocol, enhanced with structures for recording energy usage. Exhaustive analysis is then carried out through formal verification, utilising the PRISM model-checker to calculate the resources consumed on each possible system execution. This allows us to assess a range of parameter instantiations and to explore trade-offs between power consumption and time to synchronise. This provides a principled basis for the formal analysis of a much broader range of large-scale network protocols.
The analysis of parametrised systems is a growing field in verification, but the analysis of parametrised probabilistic systems is still in its infancy. This is partly because it is much harder: while there are beautiful cut-off results for non-stochastic systems that allow to focus only on small instances, there is little hope that such approaches extend to the quantitative analysis of probabilistic systems, as the probabilities depend on the size of a system. The unicorn would be an automatic transformation of a parametrised system into a formula, which allows to plot, say, the likelihood to reach a goal or the expected costs to do so, against the parameters of a system. While such analysis exists for narrow classes of systems, such as waiting queues, we aim both lower-stepwise exploring the parameter space-and higher-considering general systems. The novelty is to heavily exploit the similarity between instances of parametrised systems. When the parameter grows, the system for the smaller parameter is, broadly speaking, present in the larger system. We use this observation to guide the elegant state-elimination method for parametric Markov chains in such a way, that the model transformations will start with those parts of the system that are stable under increasing the parameter. We argue that this can lead to a very cheap iterative way to analyse parametric systems, show how this approach extends to reconfigurable systems, and demonstrate on two benchmarks that this approach scales.
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