Probabilistic component-based analysis for networks

Time-constrained networks have demanded so far for deterministic modeling and analysis in order to guarantee their worst-case behavior. With this work we intend to apply both probabilistic modeling and probabilistic analyses to investigate such networks. The probabilistic framework we propose aims at guaranteeing confidence levels, in the form of probabilities, to the network timing constraints; the deterministic case remain a particular case, the worst-case, within the probabilistic framework. We focus on probabilistic bounds for defining probabilistic interfaces to network components and we study the way that probabilities propagate within networks by accounting for the dependences and the interactions between network components. Finally, we define and apply probabilistic performance metrics for evaluating network behavior with different degree of confidence due to the probabilities.