Reliability Of Large Networks Research Articles

A Network-gene-mutation-based model for large network dynamic application reliability evaluation

Network evolution model (NEM) is a framework proposed recently for dynamic application reliability evaluation which integrates various factors that affect application failure. The existing component-based NEM relies on an exhaustive enumeration of all potential states of network components, which can lead to a computational bottleneck, especially when dealing with large networks. Therefore, to tackle this challenge, this paper presents a Network-gene-mutation-based NEM. The model introduces novel concepts of Network-cells and Network-genes inspired by the gene-cell-function model framework used in system biology to address complex disease problems. Network-cells are constructed according to the statistical classification of components. Network-genes are constructed by the average failure rate of components within a Network-cell. Dynamic network states are simulated by the Network-gene-mutation function of Network-cells instead of the failure and repair function of network components. Validation of our proposed model is performed through the comparison with benchmark models. The results demonstrate the validity of our proposed model in calculating dynamic application reliability. Besides, the comparison results with the component-based NEM also suggest that our proposed model reduces the time complexity of the algorithm from square order [Formula: see text] to constant order [Formula: see text] and improves the simulation efficiency by roughly 104. Applied to networks of varying scales, our proposed model proves to be an efficient method for calculating dynamic application reliability in large networks. In summary, this paper offers a promising solution to address the computational bottlenecks encountered in the component-based NEM when evaluating the reliability of dynamic applications.

Stochastic Simulation on System Reliability and Component Probabilistic Importance of Road Network

Because of the combination explosion problem, it is difficult to use probability analytical method to calculate the system reliability of large networks. The paper develops a stochastic simulation (Monte Carlo-based) method to study the system reliability and component probabilistic importance of the road network. The proposed method considers the characteristics of the practical road network as follows: both link (roadway segment) and node (intersection) components are emphasized in the road network; the reliability for a link or node component may be at the in-between state; namely, its reliability value is between 0 and 1. The method is then implemented using the object-oriented programming language C++ and integrated into a RARN-MGG (reliability analysis of road network using Monte Carlo, GIS, and grid) system. Finally, two numerical examples based on a simple road network and a large real road network, respectively, are carried out to characterize the feasibility and to demonstrate the strength of the stochastic simulation method.

Calculation of node-pair reliability in large networks with unreliable nodes : Don Torrieri. IEEE Transactions on Reliability, 43, 3, 375 (September 1994)

Calculation of node-pair reliability in large networks with unreliable nodes : Don Torrieri. IEEE Transactions on Reliability, 43, 3, 375 (September 1994)

Calculation of node-pair reliability in large networks with unreliable nodes

A new efficient method that compensates for unreliable nodes in network reliability computations is presented. This method can be embedded in the modified Dotson algorithm or any algorithm that generates a symbolic reliability expression for networks with perfect nodes. Its cost increases linearly with the number of links, and the effect of unreliable nodes can be directly computed. This method supplants the Aggarwal method and other methods of compensating for unreliable nodes in the calculation of node-pair reliability. When combined with the modified Dotson algorithm, this method provides accurate reliability estimates for networks so large that the Theologou-Carlier algorithm cannot complete its computations in a reasonable amount of time. For such networks, the new method can be embedded in the modified Dotson algorithm to estimate both the node-pair reliability and the error in this estimate even if the algorithm is terminated before completion. >