Model Checking Continuous-Time Markov Chains by Transient Analysis

The verification of continuous-time Markov chains (CTMCs) against continuous stochastic logic (CSL) [3,6], a stochastic branching-time temporal logic, is considered. CSL facilitates among others the specification of steady-state properties and the specification of probabilistic timing properties of the form \({\cal P}_{\bowtie p}(\Phi_1 \, {\cal U}^{I} \, \Phi_2)\), for state formulas Φ1 and Φ2, comparison operator ⋈, probability p, and real interval I. The main result of this paper is that model checking probabilistic timing properties can be reduced to the problem of computing transient state probabilities for CTMCs. This allows us to verify such properties by using efficient techniques for transient analysis of CTMCs such as uniformisation. A second result is that a variant of ordinary lumping equivalence (i.e., bisimulation), a well-known notion for aggregating CTMCs, preserves the validity of all CSL-formulas.

[1]  Adnan Aziz,et al.  It Usually Works: The Temporal Logic of Stochastic Systems , 1995, CAV.

[2]  A. Jensen,et al.  Markoff chains as an aid in the study of Markoff processes , 1953 .

[3]  Joost-Pieter Katoen,et al.  Process algebra for performance evaluation , 2002, Theor. Comput. Sci..

[4]  Donald Gross,et al.  The Randomization Technique as a Modeling Tool and Solution Procedure for Transient Markov Processes , 1984, Oper. Res..

[5]  Kim G. Larsen,et al.  Bisimulation through Probabilistic Testing , 1991, Inf. Comput..

[6]  Jane Hillston,et al.  A compositional approach to performance modelling , 1996 .

[7]  Christel Baier,et al.  Approximate Symbolic Model Checking of Continuous-Time Markov Chains , 1999, CONCUR.

[8]  Peter Buchholz Markovian process algebra: Composition and equiva-lence , 1994 .

[9]  P. Buchholz Exact and ordinary lumpability in finite Markov chains , 1994, Journal of Applied Probability.

[10]  Boudewijn R. Haverkort,et al.  Performance of computer communication systems - a model-based approach , 1998 .

[11]  Christel Baier,et al.  On the Logical Characterisation of Performability Properties , 2000, ICALP.

[12]  Robert K. Brayton,et al.  Verifying Continuous Time Markov Chains , 1996, CAV.

[13]  Mihalis Yannakakis,et al.  Verifying temporal properties of finite-state probabilistic programs , 1988, [Proceedings 1988] 29th Annual Symposium on Foundations of Computer Science.

[14]  Boudewijn R. Haverkort,et al.  Probabilistic Evaluation for the Analytical Solution of Large Markov Models: Algorithms and Tool Support , 1996 .

[15]  Holger Hermanns,et al.  A Markov Chain Model Checker , 2000, TACAS.

[16]  Edmund M. Clarke,et al.  Characterizing Finite Kripke Structures in Propositional Temporal Logic , 1988, Theor. Comput. Sci..

[17]  Enrico Macii,et al.  Algebric Decision Diagrams and Their Applications , 1997, ICCAD '93.

[18]  Holger Hermanns,et al.  Bisimulation Algorithms for Stochastic Process Algebras and Their BDD-Based Implementation , 1999, ARTS.

[19]  William J. Stewart,et al.  Introduction to the numerical solution of Markov Chains , 1994 .

[20]  Masahiro Fujita,et al.  Multi-Terminal Binary Decision Diagrams: An Efficient Data Structure for Matrix Representation , 1997, Formal Methods Syst. Des..

[21]  Marco Ajmone Marsan,et al.  Modelling with Generalized Stochastic Petri Nets , 1995, PERV.

[22]  Peter W. Glynn,et al.  Computing Poisson probabilities , 1988, CACM.

[23]  Joost-Pieter Katoen,et al.  Automated compositional Markov chain generation for a plain-old telephone system , 2000, Sci. Comput. Program..

[24]  Randal E. Bryant,et al.  Graph-Based Algorithms for Boolean Function Manipulation , 1986, IEEE Transactions on Computers.

[25]  Ignas G. Niemegeers,et al.  Performability Modelling Tools and Techniques , 1996, Perform. Evaluation.

[26]  R. I. Bahar,et al.  Algebraic decision diagrams and their applications , 1993, Proceedings of 1993 International Conference on Computer Aided Design (ICCAD).