Differential Bisimulation for a Markovian Process Algebra

  • Giulio Iacobelli
  • Mirco Tribastone
  • Andrea Vandin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9234)

Abstract

Formal languages with semantics based on ordinary differential equations (ODEs) have emerged as a useful tool to reason about large-scale distributed systems. We present differential bisimulation, a behavioral equivalence developed as the ODE counterpart of bisimulations for languages with probabilistic or stochastic semantics. We study it in the context of a Markovian process algebra. Similarly to Markovian bisimulations yielding an aggregated Markov process in the sense of the theory of lumpability, differential bisimulation yields a partition of the ODEs underlying a process algebra term, whereby the sum of the ODE solutions of the same partition block is equal to the solution of a single (lumped) ODE. Differential bisimulation is defined in terms of two symmetries that can be verified only using syntactic checks. This enables the adaptation to a continuous-state semantics of proof techniques and algorithms for finite, discrete-state, labeled transition systems. For instance, we readily obtain a result of compositionality, and provide an efficient partition-refinement algorithm to compute the coarsest ODE aggregation of a model according to differential bisimulation.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Giulio Iacobelli
    • 1
  • Mirco Tribastone
    • 2
  • Andrea Vandin
    • 2
  1. 1.Computing and Systems EngineeringFederal University of Rio de JaneiroRio de JaneiroBrazil
  2. 2.IMT Institute for Advanced Studies LuccaLuccaItaly

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