1 / 13

VESTA: A Statistical Model-checker and Analyzer for Probabilistic Systems

VESTA: A Statistical Model-checker and Analyzer for Probabilistic Systems. Authors: Koushik Sen Mahesh Viswanathan Gul Agha University of Illinois at Urbana-Champaign. YoungMin Kwon. Vesta Tool. Input: A probabilistic model M given as

yitro
Download Presentation

VESTA: A Statistical Model-checker and Analyzer for Probabilistic Systems

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. VESTA: A Statistical Model-checker and Analyzer for Probabilistic Systems Authors: Koushik Sen Mahesh Viswanathan Gul Agha University of Illinois at Urbana-Champaign YoungMin Kwon

  2. Vesta Tool • Input: A probabilistic model M given as • a Java class on which one can perform discrete-event simulation • a CTMC model in a special language (similar to that used in PRISM) • a Probabilistic Rewrite Theory in Maude

  3. Vesta Tool • Input: A probabilistic model M given as • a Java class on which one can perform discrete-event simulation • a CTMC model in a special language (similar to that used in PRISM) • a Probabilistic Rewrite Theory in Maude • Input: A formula F in Continuous Stochastic Logic (CSL) or Probabilistic Computation Tree Logic (PCTL) • Vesta can model check F against M, i.e. check if M ² F

  4. Vesta Tool • Input: A probabilistic model M given as • a Java class on which one can perform discrete-event simulation • a CTMC model in a special language (similar to that used in PRISM) • a Probabilistic Rewrite Theory in Maude • Input: A formula F in Continuous Stochastic Logic (CSL) or Probabilistic Computation Tree Logic (PCTL) • Vesta can model check F against M, i.e. check if M ² F • Input: An expression E in Quantitative Temporal Expressions (QuaTEx) • Vesta can compute the expected value of E

  5. Model Assumption • Sample execution paths can be generated through discrete-event simulation • Execution paths are sequences of the form = s0! s1! s2! … where each si is a state of the model and ti2R>0 is the time spent in the state si before moving to the state si+1 • A probability space can be defined on the execution paths of the model in such a way that the paths satisfying any path formula in our concerned logic (CSL or PCTL), is measurable t0 t1 t2

  6. Continuous Stochastic Logic (CSL) and PCTL •  ::= true | a | Æ | : | PQ p() •  ::=  U<t |  U  | X  where Q2 {<,>,¸,·} • P< 0.5(§full) • Probability that queue becomes full is less than 0.5 • P>0.98(: retransmit U receive) • Probability that a message is eventually received successfully without any need for retransmission is greater than 0.98

  7. Model Checking: Main Result Summarized • Our algorithm A takes as input • a stochastic model M, • a formulain CSL, • error bounds  and, and • three other parameters 1, 2, and ps. • The result of model checking is denoted by A1,2,ps(M, ,,) • can be either true or false. • Details in [Sen et al. CAV’05]

  8. Model Checking: Main Result Summarized Theorem: If the model M satisfies the following conditions • C1: For every subformula of the form P¸ p in the formula  and for every state s in M, the probability that a path from s satisfies  must not lie in the range [ (p-1-)/(1-),(p+1)/(1-)] • C2: For any subformula of the form 1 U 2 and for every state s in M, the probability that a path from s satisfies 1 U 2 must not lie in the range (0, 2/((1-ps)N-1qN-1)], where N is the number of states in the model M and q is the smallest non-zero transition probability in M • Then the algorithm provides the following guarantees • R1 : • Pr[A1,2,ps(M, ,,) = true | M 2] · • Pr[A1,2,ps(M, ,,) = false | M²] ·

  9. Quantitative Queries Using QuaTEx • What is the expected number of clients that successfully connect to S? CountConnected() = if completed() then count() else ° (CountConnected()) fi; eval E[CountConnected()]

  10. Quantitative Queries Using QuaTEx • What is the probability that a client connected to S within 10 seconds after it initiated the connection request? Prob() = if globaltime()>10 then 0.0 else if connected() then 1.0 else ° (Prob()) fi fi; eval E[Prob()]

  11. Evaluation of QuaTEx • The expected value of a QuaTEx expression is statistically evaluated with respect to two parameters  and  provided as input. • We approximate the expected value by the mean of n samples such that the size of (1−)100% confidence interval for the expected value computed from the samples is bounded by. • Details in [Agha et al. QAPL’05]

  12. Vesta Screenshot

  13. Conclusion • Vesta 2.0 supports • statistical model checking of probabilistic systems • query various quantitative aspects of a probabilistic system • The tool is available for download at http://osl.cs.uiuc.edu/~ksen/vesta2/

More Related