2,877 research outputs found
Extension of PRISM by Synthesis of Optimal Timeouts in Fixed-Delay CTMC
We present a practically appealing extension of the probabilistic model
checker PRISM rendering it to handle fixed-delay continuous-time Markov chains
(fdCTMCs) with rewards, the equivalent formalism to the deterministic and
stochastic Petri nets (DSPNs). fdCTMCs allow transitions with fixed-delays (or
timeouts) on top of the traditional transitions with exponential rates. Our
extension supports an evaluation of expected reward until reaching a given set
of target states. The main contribution is that, considering the fixed-delays
as parameters, we implemented a synthesis algorithm that computes the
epsilon-optimal values of the fixed-delays minimizing the expected reward. We
provide a performance evaluation of the synthesis on practical examples
Discrete-time rewards model-checked
This paper presents a model-checking approach for analyzing discrete-time Markov reward models. For this purpose, the temporal logic probabilistic CTL is extended with reward constraints. This allows to formulate complex measures – involving expected as well as accumulated rewards – in a precise and succinct way. Algorithms to efficiently analyze such formulae are introduced. The approach is illustrated by model-checking a probabilistic cost model of the IPv4 zeroconf protocol for distributed address assignment in ad-hoc networks
Approximate performability and dependability analysis using generalized stochastic Petri Nets
Since current day fault-tolerant and distributed computer and communication systems tend to be large and complex, their corresponding performability models will suffer from the same characteristics. Therefore, calculating performability measures from these models is a difficult and time-consuming task.\ud
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To alleviate the largeness and complexity problem to some extent we use generalized stochastic Petri nets to describe to models and to automatically generate the underlying Markov reward models. Still however, many models cannot be solved with the current numerical techniques, although they are conveniently and often compactly described.\ud
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In this paper we discuss two heuristic state space truncation techniques that allow us to obtain very good approximations for the steady-state performability while only assessing a few percent of the states of the untruncated model. For a class of reversible models we derive explicit lower and upper bounds on the exact steady-state performability. For a much wider class of models a truncation theorem exists that allows one to obtain bounds for the error made in the truncation. We discuss this theorem in the context of approximate performability models and comment on its applicability. For all the proposed truncation techniques we present examples showing their usefulness
A uniformization-based algorithm for model checking the CSL until operator on labeled queueing networks
We present a model checking procedure for the CSL until operator on the CTMCs that underlie Jackson queueing networks. The key issue lies in the fact that the underlying CTMC is infinite in as many dimension as there are queues in the JQN. We need to compute the transient state probabilities for all goal states and for all possible starting states. However, for these transient probabilities no computational procedures are readily available. The contribution of this paper is the proposal of a new uniformization-based approach to compute the transient state probabilities. Furthermore, we show how the highly structured state space of JQNs allows us to compute the possible infinite satisfaction set for until formulas. A case study on an e-business site shows the feasibility of our approach
Optimizing Performance of Continuous-Time Stochastic Systems using Timeout Synthesis
We consider parametric version of fixed-delay continuous-time Markov chains
(or equivalently deterministic and stochastic Petri nets, DSPN) where
fixed-delay transitions are specified by parameters, rather than concrete
values. Our goal is to synthesize values of these parameters that, for a given
cost function, minimise expected total cost incurred before reaching a given
set of target states. We show that under mild assumptions, optimal values of
parameters can be effectively approximated using translation to a Markov
decision process (MDP) whose actions correspond to discretized values of these
parameters
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