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The analysis of probabilistic schedulability explores all possible combinations of the probabilities of task attributes, which can easily lead to exponential computation time  . In this paper, we present a flexible schedulability analysis framework for periodic and sporadic tasks having probabilistic attributes where the computation time scales linearly in the size of analyzed systems. The framework is given in terms of a set of Parameterized Stopwatch Automata (PSA) models, which leads todoi:10.1109/isorc.2015.21 dblp:conf/isorc/BoudjadarKDLMNS15 fatcat:uzwgaddppreg3dufw7ygm3hn2a