8,822 research outputs found
A model checker for performance and dependability properties
Markov chains are widely used in the context of
performance and reliability evaluation of systems of various
nature. Model checking of such chains with respect to
a given (branching) temporal logic formula has been proposed
for both the discrete [8] and the continuous time setting
[1], [3]. In this short paper, we describe the prototype
model checker for discrete and continuous-time
Markov chains, where properties are expressed in appropriate
extensions of CTL.We illustrate the general benefits
of this approach and discuss the structure of the tool
A probabilistic model checking approach to analysing reliability, availability, and maintainability of a single satellite system
Satellites now form a core component for space
based systems such as GPS and GLONAS which provide
location and timing information for a variety of uses. Such
satellites are designed to operate in-orbit and have lifetimes of
10 years or more. Reliability, availability and maintainability
(RAM) analysis of these systems has been indispensable in
the design phase of satellites in order to achieve minimum
failures or to increase mean time between failures (MTBF)
and thus to plan maintainability strategies, optimise reliability
and maximise availability. In this paper, we present formal
modelling of a single satellite and logical specification of
its reliability, availability and maintainability properties. The
probabilistic model checker PRISM has been used to perform
automated quantitative analyses of these properties
A tool for model-checking Markov chains
Markov chains are widely used in the context of the performance and reliability modeling of various systems. Model checking of such chains with respect to a given (branching) temporal logic formula has been proposed for both discrete [34, 10] and continuous time settings [7, 12]. In this paper, we describe a prototype model checker for discrete and continuous-time Markov chains, the Erlangen-Twente Markov Chain Checker EÎMC2, where properties are expressed in appropriate extensions of CTL. We illustrate the general benefits of this approach and discuss the structure of the tool. Furthermore, we report on successful applications of the tool to some examples, highlighting lessons learned during the development and application of EÎMC2
Dynamical systems with heavy-tailed random parameters
Motivated by the study of the time evolution of random dynamical systems
arising in a vast variety of domains --- ranging from physics to ecology ---,
we establish conditions for the occurrence of a non-trivial asymptotic
behaviour for these systems in the absence of an ellipticity condition. More
precisely, we classify these systems according to their type and --- in the
recurrent case --- provide with sharp conditions quantifying the nature of
recurrence by establishing which moments of passage times exist and which do
not exist. The problem is tackled by mapping the random dynamical systems into
Markov chains on with heavy-tailed innovation and then using
powerful methods stemming from Lyapunov functions to map the resulting Markov
chains into positive semi-martingales.Comment: 24 page
Bounded Model Checking of GSMP Models of Stochastic Real-Time Systems
Model checking is a popular algorithmic verification technique for checking temporal requirements of mathematical models of systems. In this paper, we consider the problem of verifying bounded reachability properties of stochastic real-time systems modeled as generalized semi-Markov processes (GSMP). While GSMPs is a rich model for stochastic systems widely used in performance evaluation, existing model checking algorithms are applicable only to subclasses such as discrete-time or continuous-time Markov chains. The main contribution of the paper is an algorithm to compute the probability that a given GSMP satisfies a property of the form “can the system reach a target before time T within k discrete events, while staying within a set of safe states”. For this, we show that the probability density function for the remaining firing times of different events in a GSMP after k discrete events can be effectively partitioned into finitely many regions and represented by exponentials and polynomials. We report on illustrative examples and their analysis using our techniques
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