Abstract
We present a new methodology based on the stochastic ordering, algorithmic derivation of simpler Markov chains and numerical analysis of these chains. The performance indices defined by reward functions are stochastically bounded by reward functions computed on much simpler or smaller Markov chains. This leads to an important reduction on numerical complexity. Stochastic bounds are a promising method to analyze QoS requirements. Indeed it is sufficient to prove that a bound of the real performance satisfies the guarantee.
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Fourneau, J.M., Pekergin, N. (2002). An Algorithmic Approach to Stochastic Bounds. In: Calzarossa, M.C., Tucci, S. (eds) Performance Evaluation of Complex Systems: Techniques and Tools. Performance 2002. Lecture Notes in Computer Science, vol 2459. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45798-4_4
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DOI: https://doi.org/10.1007/3-540-45798-4_4
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