Frequency-domain modeling and analysis of network reliability

The probabilistic reliability estimation of complex system is complicated due to uncertainty of failure data, modeling or human failure. In this paper, reliability of components of complex network system is considered as fuzzy in nature to reduce the uncertainty. Trapezoidal fuzzy number is used to represent component's reliability of network system. Then the reliability of the network system is assembled with fuzzy reliability of components and evaluated by Zadeh's extension principle. Fuzzy reliability of the network system becomes an interval by α-cuts operation. Interval nonlinear programming is used to evaluate the optimum network's system reliability with interval valued cost constraint. The above approach is explained in details through a numerical example of very useful power system network of electrical engineering.

Satellite communication networks with onboard processing (OBP) satellites can provide high-speed data transmission rates and global service coverage with reduced propagation delays. This article proposes a means of analyzing the quantitative reliability of the satellite communication network systems. First, we identify the four major factors that affect the quality of network services: the OBP states, uplink channels, downlink channels, and uplink packet collision losses. Based on these four factors, a Markov model is derived to analyze the probability distributions of various network states. Analytic results show the network reliability with respect to the OBP structures and space radiation environments. The relationship between network reliability and throughput according to the packet traffic load is also quantitatively analyzed. Finally, based on the developed model, a method is suggested for iteratively updating the reliability distribution of network systems affected by changes in the four factors as well as network access time changes. The numerical results show an indication of the satellite network reliability to provide references whether adjusted elements of the four factors are eligible to sustain reliable end-to-end services.

Reliability analysis of networks using stochastic model

The reliability of network systems of various structures has been studied by many researchers. However, most of the works just consider the reliability of a single network system. In practice, different networks may be interdependent such that the failure in one network may result in the failure in another network. The cascading failures have been shown to be catastrophic by some researchers. However, the quantitative evaluation for the reliability of interdependent networks has not been proposed. In this paper, a multi-valued decision diagram based approach is presented to evaluate the reliability of interdependent networks. Illustrative examples are proposed to demonstrate the application of the framework.

Systems of interconnected components can often be usefully represented by networks. It is practically important to calculate the reliability of such network systems when the components are subject to (statistically independent) failures. We define several commonly occurring reliability models and summarize the computational complexity of calculating their associated reliability measures. Some general techniques for exact and approximate calculation of network reliability are surveyed.

Systems of interconnected components can often be usefully represented by networks. It is practically important to calculate the reliability of such network systems when the components are subject to (statistically independent) failures. We define several commonly occurring reliability models and summarize the computational complexity of calculating their associated reliability measures. Some general techniques for exact and approximate calculation of network reliability are surveyed.

The insertion of communication networks in closed-loop of digital control systems makes the system synthesis and analysis complex. In this paper, we consider the combination of classical reliability, risk and safety evaluation with typical control system through digital networked control systems. The main purpose is to study the influence of transmission delays and packet dropouts on the reliability of digital networked control systems by using a modified statistical method-Monte Carlo simulation. The behaviors of two types of fault are modelled by different statistical distribution. The control strategies we apply for digital control systems are Proportion Integration Differentiation control strategies, generated by Genetic Algorithm. In terms of a realistic system model, we evaluate and compare the influences on the reliability of digital networked control systems with different control strategies from quantitative aspect. Numerical examples are presented to illustrate the effectiveness and correctness of the approach.

ABSTRACTIn the process of reliability analysis of computer network, there is the high probability of network failure in the application process by using the current analysis method. In this paper, a reliability analysis method of computer network based on intelligent cloud computing method is proposed. The proposed method is integrated with the theoretical idea of analytic hierarchy process. From the research angle of network reliability, the reliability model of computer network is set up by using the failure criterion of computer network, and the intelligent cloud computing method is used to calculate the reliability state parameters of the network. According to the obtained state parameters of network reliability analysis, the reliability analysis matrix of computer network is established, and then to accurately complete the reliability analysis of computer network. The simulation results show that the reliability analysis method of computer network based on intelligent cloud computing is highly accurate and robust.

An efficient Boolean algebraic algorithm for the symbolic reliability and sensitivity analysis of coherent two-terminal networks with s independent components is described. The algorithm is also applicable to a fault tree model without NOT gates. The algorithm uses the concept originally proposed by A. Grnarov, L. Kleinrock, and M. Gerla (1979). After the algorithm is presented, the errors in the original technique are illustrated by two examples. The algorithm is extended t compute the reliability importance of a given component (sensitivity of system reliability to a given component's reliability). A computer program implementing the modified algorithm is used to solve and obtain measured time complexities for a large set of network and fault tree models.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

: Analysis of network reliability is of major importance in computer, communication and power networks. Even the simplest models lead to computational problems which are NP-hard for general networks, although polynomial-time algorithms do exist for certain network configurations such as 'ladders' and 'wheels' and for some series-parallel structures such as the well-known 'two-terminal' series-parallel networks. In this paper, we show that a class of series-parallel networks, for which only exponentially complex algorithms were previously known, can be analyzed in polynomial time. In doing this, we introduce a new reliability-preserving graph reduction of general applicability and produce a linear-time algorithm for computing the reliability of any graph with an underlying series-parallel structure.

In this paper, we propose an algorithm to construct the ordered binary decision diagram (OBDD) representing the cut function of a terminal-pair network. The algorithm recognizes isomorphic sub-problems and thus avoids redundant computations. The system reliability could be efficiently computed by the OBDD. Finally, we propose an approach to compute the importance measures for multiple components by traversing the OBDD only once. The correctness and the effectiveness of our approach are demonstrated by experiments on 30 benchmark networks. The experimental results on a 2-by-100 lattice network, which has 2/sup 99/ paths or 10,000 cuts, show an impressive improvement compared to the previous works using the sum of disjoint products method that have exponential complexity. The CPU time of our method, including the calculation of not only the reliability but also the importance measures, for a 100-stage lattice network is only about 0.24 seconds. Thus, this approach is very helpful for the reliability and sensitivity analysis of large networks.

The goal of a reliable network is to ensure that all transmitted messages arrive at their intended destinations uncorrupted. Most network design techniques concern themselves with channel capacity allocation and cost constraints and ignore reliability analysis. Conversely, network reliability analysis methods focus on connectivity and ignore traffic loads and channel capacities. This paper presents a design strategy to enhance the reliability of an existing network by modifying its topology. This method takes into account traffic distributions. A new reliability metric is introduced which is the probability that all transmitted messages arrive at their intended destinations under the given traffic distribution. The design strategy adds links to the network to maximize the probability that all transmitted messages arrive at their intended destinations. The network design technique uses an approximate network reliability analysis algorithm to achieve quadratic-time performance. >

In view of the effect of DGs uncertainty on the reliability of the distribution network system, an integrated Markov model is put forward to evaluate the reliability of distribution network, considering the randomness of DG, DG mechanical failure rate and starting/switching refuse rate and other factors; In view of the randomness of the output of photovoltaic power, using the Cornish Fisher expansion method to generate the standard normal distribution of sample space in order to further analysis the effects of illumination intensity correlation coefficient of distribution system reliability. Through the simulation analysis of BUS6 IEEE-RBTS system, the simulation results show that the reliability of distribution network system is closely related to the distributed power access point, the DG capacity and the DG output correlation coefficient.

Recent earthquake events evidenced that damage of structural components in a lifeline network may cause prolonged disruption of lifeline services, which eventually results in significant socio‐economic losses in the affected area. Despite recent advances in network reliability analysis, the complexity of the problem and various uncertainties still make it a challenging task to evaluate the post‐hazard performance and connectivity of lifeline networks efficiently and accurately. In order to overcome such challenges and take advantage of merits of multi‐scale analysis, this paper develops a multi‐scale system reliability analysis method by integrating a network decomposition approach with the matrix‐based system reliability (MSR) method. In addition to facilitating system reliability analysis of large‐size networks, the multi‐scale approach enables optimizing the level of computational effort on subsystems; identifying the relative importance of components and subsystems at multiple scales; and providing a collaborative risk management framework. The MSR method is uniformly applied for system reliability analyses at both the lower‐scale (for link failure) and the higher‐scale (for system connectivity) to obtain the probability of general system events, various conditional probabilities, component importance measures, statistical correlation between subsystem failures and parameter sensitivities. The proposed multi‐scale analysis method is demonstrated by its application to a gas distribution network in Shelby County of Tennessee. A parametric study is performed to determine the number of segments during the lower‐scale MSR analysis of each pipeline based on the strength of the spatial correlation of seismic intensity. It is shown that the spatial correlation should be considered at both scales for accurate reliability evaluation. The proposed multi‐scale analysis approach provides an effective framework of risk assessment and decision support for lifeline networks under earthquake hazards. Copyright © 2009 John Wiley &amp; Sons, Ltd.

This paper proposes a reconfiguration optimization method for complex manufacturing systems in terms of reliability analysis. Some misunderstandings of network modelling and reliability analysis for manufacturing systems are clarified. A network model is constructed based on the material flow. Path-dependent and time-dependent characteristics of manufacturing networks are introduced to distinguish from complex network theory. Subsequently, reliability modelling method is proposed, wherein only processing time for individual work system is concluded to compute the reliability. Component importance measure method is given based on former reliability modelling and cost analysis, and it affords the basic input of reconfiguration optimization for manufacturing networks.