Process Algebra and Probabilistic Methods. Performance Modelling and Verification

We review high-level specification formalisms for Markovian performability models, thereby emphasising the role of structuring concepts as realised par excellence by stochastic process algebras. Symbolic representations based on decision diagrams are presented, and it is shown that they quite ideally support compositional model construction and analysis.

[1]  Wang Yi,et al.  Uppaal in a nutshell , 1997, International Journal on Software Tools for Technology Transfer.

[2]  Jeff Magee,et al.  Concurrency - state models and Java programs , 2006 .

[3]  Thomas A. Henzinger,et al.  Symbolic Model Checking for Real-Time Systems , 1994, Inf. Comput..

[4]  Daniel Lehmann,et al.  On the advantages of free choice: a symmetric and fully distributed solution to the dining philosophers problem , 1981, POPL '81.

[5]  Gerard J. Holzmann,et al.  Design and validation of computer protocols , 1991 .

[6]  Roberto Segala,et al.  Formal verification of timed properties of randomized distributed algorithms , 1995, PODC '95.

[7]  R. Segala,et al.  Automatic Verification of Real-Time Systems with Discrete Probability Distributions , 1999, ARTS.

[8]  Mihaela Sighireanu,et al.  On the Introduction of Exceptions in E-LOTOS , 1996, FORTE.

[9]  Guy L. Steele,et al.  The Java Language Specification , 1996 .

[10]  Joost-Pieter Katoen,et al.  MoDeST - A Modelling and Description Language for Stochastic Timed Systems , 2001, PAPM-PROBMIV.

[11]  C. Harvey,et al.  Performance engineering as an integral part of system design , 1986 .

[12]  Isaac Saias,et al.  Proving probabilistic correctness statements: the case of Rabin's algorithm for mutual exclusion , 1992, PODC '92.

[13]  Hans A. Hansson Time and probability in formal design of distributed systems , 1991, DoCS.

[14]  Michael Ben-Or,et al.  Another advantage of free choice (Extended Abstract): Completely asynchronous agreement protocols , 1983, PODC '83.

[15]  Roberto Segala,et al.  Modeling and verification of randomized distributed real-time systems , 1996 .

[16]  C. A. R. Hoare,et al.  Communicating sequential processes , 1978, CACM.

[17]  Holger Hermanns,et al.  Interactive Markov Chains , 2002, Lecture Notes in Computer Science.

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

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

[20]  William H. Sanders,et al.  Stochastic Activity Networks: Structure, Behavior, and Application , 1985, PNPM.

[21]  J. Kemeny,et al.  Denumerable Markov chains , 1969 .

[22]  Martin L. Puterman,et al.  Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .

[23]  Vassilis Mertsiotakis,et al.  Approximate analysis methods for stochastic process algebras , 1998 .

[24]  Domenico Ferrari Considerations on the insularity of performance evaluation , 1986, IEEE Transactions on Software Engineering.

[25]  Joost-Pieter Katoen,et al.  An algebraic approach to the specification of stochastic systems , 1998, PROCOMET.

[26]  Cyrus Derman,et al.  Finite State Markovian Decision Processes , 1970 .

[27]  Roberto Segala,et al.  Verification of the randomized consensus algorithm of Aspnes and Herlihy: a case study , 2000, Distributed Computing.

[28]  Marta Z. Kwiatkowska,et al.  Verifying Quantitative Properties of Continuous Probabilistic Timed Automata , 2000, CONCUR.