Abstract: Estimating the reliability of a computer network has been a subject of great interest. It is a well known fact that this problem is NP-hard. In this paper we present a very efficient combinatorial approach for Monte Carlo reliability estimation of a network with unreliable nodes and unreliable edges. Its core is the computation of some network combinatorial invariants. These invariants, once computed, directly provide pure and simple framework for computation of network reliability. As a specific case of this approach we obtain tight lower and upper bounds for distributed network reliability (the so called residual connectedness reliability). We also present some simulation results.
Abstract: The increasing importance of FlexRay systems in
automotive domain inspires unceasingly relative researches. One
primary issue among researches is to verify the reliability of FlexRay
systems either from protocol aspect or from system design aspect.
However, research rarely discusses the effect of network topology on
the system reliability. In this paper, we will illustrate how to model
the reliability of FlexRay systems with various network topologies by
a well-known probabilistic reasoning technology, Bayesian Network.
In this illustration, we especially investigate the effectiveness of error
containment built in star topology and fault-tolerant midpoint
synchronization algorithm adopted in FlexRay communication
protocol. Through a FlexRay steer-by-wire case study, the influence
of different topologies on the failure probability of the FlexRay steerby-
wire system is demonstrated. The notable value of this research is
to show that the Bayesian Network inference is a powerful and
feasible method for the reliability assessment of FlexRay systems.
Abstract: This paper address the network reliability optimization
problem in the optical access network design for the 3G cellular
systems. We presents a novel 0-1 integer programming model for
designing optical access network topologies comprised of multi-rings
with common-edge in order to guarantee always-on services. The
results show that the proposed model yields access network
topologies with the optimal reliablity and satisfies both network cost
limitations and traffic demand requirements.
Abstract: An advanced Monte Carlo simulation method, called Subset Simulation (SS) for the time-dependent reliability prediction for underground pipelines has been presented in this paper. The SS can provide better resolution for low failure probability level with efficient investigating of rare failure events which are commonly encountered in pipeline engineering applications. In SS method, random samples leading to progressive failure are generated efficiently and used for computing probabilistic performance by statistical variables. SS gains its efficiency as small probability event as a product of a sequence of intermediate events with larger conditional probabilities. The efficiency of SS has been demonstrated by numerical studies and attention in this work is devoted to scrutinise the robustness of the SS application in pipe reliability assessment. It is hoped that the development work can promote the use of SS tools for uncertainty propagation in the decision-making process of underground pipelines network reliability prediction.