Stochastic fault trees for cross-layer power management of WSN monitoring systems
ETFA'09 Proceedings of the 14th IEEE international conference on Emerging technologies & factory automation
A framework for simulation and symbolic state space analysis of non-markovian models
SAFECOMP'11 Proceedings of the 30th international conference on Computer safety, reliability, and security
Performance evaluation of schedulers in a probabilistic setting
FORMATS'11 Proceedings of the 9th international conference on Formal modeling and analysis of timed systems
Thin and thick timed regular languages
FORMATS'11 Proceedings of the 9th international conference on Formal modeling and analysis of timed systems
Proceedings of the 5th International ICST Conference on Performance Evaluation Methodologies and Tools
As soon as probable: optimal scheduling under stochastic uncertainty
TACAS'13 Proceedings of the 19th international conference on Tools and Algorithms for the Construction and Analysis of Systems
Non-markovian analysis for model driven engineering of real-time software
Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering
Transient analysis of networks of stochastic timed automata using stochastic state classes
QEST'13 Proceedings of the 10th international conference on Quantitative Evaluation of Systems
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Quantitative evaluation of models with generally-distributed transitions requires analysis of non-Markovian processes that may be not isomorphic to their underlying untimed models and may include any number of concurrent non-exponential timers. The analysis of stochastic Time Petri Nets copes with the problem by covering the state space with stochastic-classes, which extend Difference Bounds Matrices (DBM) with a state probability density function. We show that the state-density function accepts a continuous piecewise representation over a partition in DBM-shaped sub-domains. We then develop a closed-form symbolic calculus of state-density functions assuming that model transitions have expolynomial distributions. The calculus shows that within each sub-domain the state-density function is a multivariate expolynomial function and makes explicit how this form evolves through subsequent transitions. This enables an efficient implementation of the analysis process and provides the formal basis that supports introduction of an approximate analysis based on Bernstein Polynomials. The approximation attacks practical and theoretical limits in the applicability of stochastic state-classes, and devises a new approach to the analysis of non Markovian models, relying on approximations in the state space rather than in the structure of the model.