A recursive variance-reduction algorithm for estimating communication-network reliability

Cancela, Héctor; Khadiri, Mohamed El

doi:10.1109/24.475978

Cited by 48 publications

(31 citation statements)

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“…The second MC method is the RVR which associates every output random variable with a variance, and limits the results in order to obtain a greater precision. Therefore RVR will give a more accurate DCR estimation than the CMC method when using the same number of random samples [4]. However as we will show in the next sections, the CMC approach will yield excellent results when parallelism is applied.…”

Section: Monte Carlo Techniques To Evaluate the Dcrmentioning

confidence: 98%

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Monte Carlo Estimation of the Diameter-Constrained Reliability of Networks Using Parallelism

Lin¹

2016

SIURO

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Abstract. In this paper we study an heuristic approach, based on parallelism, to estimate the Diameter-Constrained Reliability (DCR) of a given undirected probabilistic graph G = (V, E), with two terminal nodes s and t, a given diameter bound D, and where edges are assigned independent probabilities of failure (node are assumed to be perfect). Since exact methods to evaluate the DCR are computationally expensive (i.e., NP-hard), we propose to implement a Monte Carlo (M C) method based upon MPI parallel processing to estimate the reliability. We conduct computational tests on several topologies while considering different factors such as number of cluster nodes utilized, number of trials performed by the cluster nodes, different ranges generated by random number functions, and then we determine how these factors affect the estimation of the DCR.

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Section: Monte Carlo Techniques To Evaluate the Dcrmentioning

confidence: 98%

“…In Section 2 we will give a background for an exact method (Factoring Theorem) of evaluation and MC techniques to estimate the DCR [4,8]. In Section 3 we present the pseudo-code of our implementation using MPI parallel processing.…”

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confidence: 99%

Monte Carlo Estimation of the Diameter-Constrained Reliability of Networks Using Parallelism

Lin¹

2016

SIURO

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“…This method has been originally proposed by Héctor Cancela and Mohammed El Khadiri for the classical network reliability model [5]. The key idea is to find a cutset C = {e 1 , .…”

Section: Recursive Variance Reductionmentioning

confidence: 99%

“…The reasons to choose IS and RVR is that they present high performance under highly robust scenarios. The first method, RVR, received a distinction (i.e., a paper award) for its originally concept of network reliability [5]. On the other hand, the main concept of IS is to modify the probabilities, in such a way that the probability of rare events is increased.…”

Section: Introductionmentioning

confidence: 99%

A Hostile model for network reliability analysis

Lena

Robledo

Romero³

2017

Croat. Oper. Res. Rev.

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Abstract. In reliability analysis, the goal is to determine the probability of consistent operation of a system. We introduce the Hostile model, where the system under study is a network, and all the components may fail (both sites and links), except for a distinguished subset of sites, called terminals. The Hostile model includes the Classical Reliability model as a particular case. As a corollary, the exact reliability evaluation of a network in the Hostile model belongs to the list of N P-hard computational problems. Traditional methods for the classical reliability model such as Crude Monte Carlo, Importance Sampling and Recursive Variance Reduction are here adapted for the Hostile model. The performance of these methods is finally discussed using real-life networks.

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“…Systems that can Several papers have been devoted to its évaluation when nodes do not limit flow transmission and when arcs capacities are discrete, multi-valued and statistically independent random variables [1,7,11,15,16]. When all arcs have only two possible capacities 1 or 0 and the demand is d = 1, the problem becomes the source-terminal reliability problem [3,6,10] which is NP-hard [2], Consequently, the gênerai case considered here is also an NPhard problem. This implies that the computational time will be prohibitive when the network size is large [8].…”

Section: Introductionmentioning

confidence: 99%

A Monte Carlo simulation of the flow network reliability using importance and stratified sampling

Bulteau¹,

Khadiri²

1998

RAIRO-Oper. Res.

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A recursive variance-reduction algorithm for estimating communication-network reliability

Cited by 48 publications

References 0 publications

Monte Carlo Estimation of the Diameter-Constrained Reliability of Networks Using Parallelism

Monte Carlo Estimation of the Diameter-Constrained Reliability of Networks Using Parallelism

A Hostile model for network reliability analysis

A Monte Carlo simulation of the flow network reliability using importance and stratified sampling

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