Continuous-state system-reliability: an interpolation approach

Brunelle, R.D.; Kapur, Kailash C.

doi:10.1109/24.722286

Cited by 24 publications

(7 citation statements)

References 31 publications

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“…The current research on network reliability focuses on binary state and multi-state network analysis and can not accurately describe continuous change of performance of real networks [6], [7]. We analyze the feasibility of the multi-state network reliability methods [4], [5] and the continuum-state system reliability method [3], [7], [11], [12] to solve the continuum-state network reliability and present this model to overcome the shortcomings of the above methods. Defining link performance as the effect to the total length of all pairs of network nodes' shortest communication path reflects the topology between links, so eliminating the need of complex topology transition the network performance is obtained by the linear superposition of link performance.…”

Section: E the Detailed Model Solutionmentioning

confidence: 99%

“…So the node and link importance has to be derived before the evaluation of network reliability. Currently, many researchers present the evaluation methods for node [8], [10] and link [9], [11], [12] importance. The importance can only reflect the individual characteristics of network components but can not be taken as the evaluation rule of the network reliability.…”

Section: B Illustration Of the Proposed Algorithmmentioning

confidence: 99%

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Continuum-State Communication Network Reliability Model

Jiang

Song

2010

2010 International Conference on Multimedia Information Networking and Security

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Real-world network and components exhibit continuous change of performance, it can not be described accurately by currently extensively used binary state and multi-state models. This paper presents a network reliability model of continuum-state, estimating the network reliability with network performance and demand lack. The link performance is defined as its influence of the total length of the shortest peer-to-peer communication path, which reflects the network topology, so the network performance can be acquired by linear addition of the link performance without the complicated network topology conversion. The boundary method for analyzing the reliability model of continuum-state system is applied to this model to verify the availability. The Monte-Carlo simulation is also introduced which gains more accurate result than the boundary method.

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Section: E the Detailed Model Solutionmentioning

confidence: 99%

Section: B Illustration Of the Proposed Algorithmmentioning

confidence: 99%

Continuum-State Communication Network Reliability Model

Jiang

Song

2010

2010 International Conference on Multimedia Information Networking and Security

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“…As was discussed in Brunelle and Kapur [25], continuum structure functions which are continuous functions of their rt components may be developed based upon a finite set of customer-specified data points to which scattered data interpolation is applied (261. Although this produces only an approximation to the underlying structure function, it should be noted that if the underlying structure function is indeed continuum rather than discrete that the traditional approach of abandoning the continuum model in favor of a discretized model is in itself an approximation, and depending on how the discretization is performed may sometimes be guaranteed to be a poorer approximation than a scattered-data approach would produce.…”

Section: Scattered Data Interpolationmentioning

confidence: 99%

Review and classification of reliability measures for multistate and continuum models

Brunelle

Kapur

1999

IIE Transactions

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In this paper we examine a variety of customer-centered reliability measures, generalized to multistate and continuum contexts. A classification scheme is proposed for these measures, and a new measure based on customer-usage patterns is defined. In each case, reliability measures are defined such that they are equally valid for binary, multistate, continuum, and mixed systems. Techniqucs which facilitate the use of continuum models are discussed, and time-dynamic, infinite-time, and partial-information modeling considerations are also addressed.

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“…However, we are approximating the reliability function and not the structure function (Gnedenko and Ushakov, 1995). Second, we do not have a system of continuously varying states (Brunelle and Kapur, 1998; and references therein) or a system of ®nite number of (effective) states. Our rules for generating weights make them rational numbers and we have many (possibly in®nite but not continuous) effective system-states.…”

Section: Assigning Performance Effectiveness Indexmentioning

confidence: 99%

Reliability Measure Based on Average Loss of Capacity

Chakravarty

Ushakov

2002

Int Trans Operational Res

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We present an effective measure of system reliability based on average loss of call capacity that is appropriate for telecommunication hardware. We are led to consider a system consisting of units with many states with appropriately chosen performance effectiveness indices, while all atomic units are binary. In this respect, this effective measure of partial system availability is a variant of customer-centered reliability methodology. We also identify the importance of a new structure called a`pool' that behaves as a hybrid of series and parallel con®gurations. We present an algorithm for approximately calculating partial system unavailability, which solves the system decomposition problem for arbitrarily complex highly reliable systems made of hierarchically arranged k-out-of-n sub-structures. The algorithm agrees with our intuition for important special cases and has been programmed into an object oriented software tool. This method has been successfully used for the reliability analysis of Globalstar TM gateways.

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Continuous-state system-reliability: an interpolation approach

Cited by 24 publications

References 31 publications

Continuum-State Communication Network Reliability Model

Continuum-State Communication Network Reliability Model

Review and classification of reliability measures for multistate and continuum models

Reliability Measure Based on Average Loss of Capacity

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