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We are interested in the analysis of very large continuous-time Markov chains (CTMCs) with many distinct rates. Such models arise naturally in the context of reliability analysis, e.g., of computer network performability analysis, of power grids, of computer virus vulnerability, and in the study of crowd dynamics. We use abstraction techniques together with novel algorithms for the computation of...
This paper investigates quantitative dependability metrics for distributed algorithms operating in the presence of sporadic or frequently occurring faults. In particular, we investigate necessary revisions of traditional fairness assumptions in order to arrive at useful metrics, without adding hidden assumptions that may obfuscate their validity. We formulate faulty distributed algorithms as Markov...
Model checkers for concurrent probabilistic systems have become very popular within the last decade. The study of long-run average behavior has however received only scant attention in this area, at least from the implementation perspective. This paper studies the problem of how to efficiently realize an algorithm for computing optimal long-run average reward values for concurrent probabilistic systems...
We develop a compositional behavioural model that integrates a variation of probabilistic automata into a conservative extension of interactive Markov chains. The model is rich enough to embody the semantics of generalised stochastic Petri nets. We define strong and weak bisimulations and discuss their compositionality properties. Weak bisimulation is partly oblivious to the probabilistic branching...
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