Reliability Of Complex Networks Research Articles

Interactive Network Learning: An Assessment of EVE-NG Platform in Educational Settings

Abstract This article compares EVE-NG, a network emulation software, to other commonly used network simulation tools such as Cisco Packet Tracer and GNS3. EVE-NG’s features, benefits, and drawbacks are assessed in terms of system requirements, support platforms, web interface, console access, topology management, and advanced lab functionalities. EVE-NG is tested by the students in the real-world application, and feedback from a group of students is gathered regarding their satisfaction and suggestions for improvement. The results of this study show that EVE-NG provides a robust and versatile tool for simulating complex network scenarios, despite compatibility, reliability, and performance issues. It is concluded that EVE-NG has the potential to improve student learning outcomes in computer networks; however, ongoing development and refinement are necessary to meet user expectations and needs.

A comprehensive approach for assessing the reliability of complex networks using OANN approach

The optimistic model of network reliability assumes the nodes to be trustworthy while assigning a failure probability to the network's connections. For calculating a network's reliability, the persistent condition is used to guarantee that no two edges have the same failure probability. Accurately estimating the network's reliability across all of its endpoints is an NP-hard problem. The study of network reliability centers on problems arising from topological isolation of individual data nodes. Analysis of network reliability spans the phases of building, deploying, and testing a network. Issues with connection, capacity, and trip time are all metrics used in analyses of computer network reliability. In this paper, an Optimal ANN strategy is proposed as a means of assessing the reliability of the network. In this research, we analyze the methods used in and findings from recent studies on trustworthiness. The term "Optimized Artificial Neural Network" (OANN) is used to describe a strategy that takes into account aspects of both neural networks and another way for measuring trustworthiness. Results are well tested on network of 2^8nodes (mesh network) and hyper-tree network of n nodes. A network's performance may be evaluated with the help of the suggested method, which also contributes to the calculation of its cost and reliability metrics.

Optimization of complex system reliability

The issue of optimizing the reliability of complex networks is one of the contemporary issues, which has evolved with the development of methodologies for solutions with the development of modern engineering technology. Algorithms that mimic nature are among the most powerful algorithms used in optimization. In this study, the bat algorithm was presented to obtain the best reliability for a complex network that was selected at the lowest possible cost using three different cost functions, the logarithmic, the exponential and the tangent functions. Where we got different results according to the cost function used and the comparison was made between those results and we were found that the result of the logarithmic function is the best.

A modified technique to compute the minimal path sets for the reliability of complex network

Consider a complex network whose components either work or fail, there probabilities are known. Our paper study two methods for the exact computation of the reliability network, the first one is a decomposition method which may be used even after the community can go through no further modular decomposition, is based on a partition the vertices of network into sets that can be analyzed them sequentially. This method involves choosing one component as a key and then calculate the reliability network twice the first when the key work and the other if the key failed. These two probabilities are then combined to obtain the reliability network. The second method is inclusion-exclusion method which is one of the earliest techniques to compute complex network reliability expressions using the probability laws. We get the same polynomial by using two methods. In both methods, we need to calculate all of the minimum paths so we calculated them by use the adjacency matrix.

Use of Bees Colony algorithm to allocate and improve reliability of complex network

The allocation of reliability and optimization for each part of the complex system has been determined in this paper, in order to solve the problem of allocation and to improve network stability we will use Bees Colony Optimization (BCO). The three cost functions are also introduced (exponential behavior with feasibility factor model, exponential behavior model and logarithmic model). After solving the allocation problem. The aim of this paper is to compare the effects of the three cost functions using Bees Colony algorithm in terms of reliability allocation and optimization to determine which is more efficient.

Impacts of service feature on vulnerability analysis of high-speed rail network

While previous literatures have analyzed the vulnerability of high-speed rail network (HSRN), they rarely consider its service feature. Obviously, one of the key features of HSRN is offering passengers fast travel services. This study selects the passenger flow and travel time to describe the service feature of HSRN, and constructs a multiple-weight HSRN. Then, the service performance is taken as the indicator of HSRN's vulnerability by innovatively integrating complex network theory and reliability theory. Using the daily operation data of China's HSRN (CHSRN) merged with the coordinate data and statistics data in November 2018, this study assesses its vulnerability under natural hazards and manmade accidents. Afterwards, the critical component and critical area within CHSRN are identified by the spatially localized failures (SLFs) model. Comparing with the results obtained by previous methods, this study finds that the vulnerability of CHSRN is always overestimated, and some critical components are overlooked regardless of its service feature. Furthermore, we also find that the critical areas within CHSRN mostly lie on the Yangtze River Delta as considering the service feature, while they are scattered along Beijing-Guangzhou railway line or Beijing-Shanghai railway line as ignoring the feature. These findings can help administrators develop different strategies to better prioritize the allocation of limited maintenance resources and mitigate the vulnerability of HSRN.

A Comparison Between Genetic Algorithm and Practical Swarm to Investigate the Reliability Allocation of Complex Network

This paper focuses on the reliability of complex networks, which has been calculated as a complex system by using minimal cuts. The reliability of this system has been assigned to study the possible approaches to reliability values based on the reduction of total costs in this system. The genetic algorithm (GA) has been used to solve the allocation problem. Reliability and ability to solve complex problems and optimization also found in structures of engineering. Generally high implementation costs require high further duplicates. In addition to using GA, optimizing the swarm (PSO) and this algorithm is based on the action of the organisms living that accumulate. PSO is very close to GA because of the fact that these evolutionary approaches are all strategies, Using some probabilistic and some probable values. The main objective of this article is to investigate and compare between two algorithms GA and PSO.

Optimization processes of tangible and intangible networks through the Laplace problems for regular lattices with multiple obstacles along the way

A systematic approach is proposed to the theme of safety, reliabil-ity and global quality of complex networks (material and immaterial) bymeans of special mathematical tools that allow an adequate geometriccharacterization and study of the operation, even in the presence of mul-tiple obstacles along the path. To that end, applying the theory of graphsto the problem under study and using a special mathematical model basedon stochastic geometry, in this article we consider some regular latticesin which it is possible to schematize the elements of the network, withthe fundamental cell with six, eight or 2(n + 2) obstacles, calculating theprobability of Laplace. In this way it is possible to measure the "degree ofimpedance" exerted by the anomalies along the network by the obstaclesexamined. The method can be extended to other regular or irregular ge-ometric gures, whose union together constitutes the examined network,allowing to optimize the functioning of the complex system considered.

Continuous Phase Transition of k-Partite Networks

Our daily lives rely on all kinds of complex networks. However, complex networks in reality often suffer from random failures and/or attacks that can cause failures to their nodes/edges. A network can totally lose its functionalities due to nodes/edges failures. It is, therefore, of great significance to assess the robustness of complex networks confronted with failures. The network robustness analysis is helpful for assuring the reliability of complex networks. In our previous work, we had experimentally investigated the robustness of a special type of networks called k-partite networks. Simulation results have discovered that k-partite networks are robust to random node failures. In this work, we aim to investigate the underlying mathematical principle for the robustness property of k-partite networks. To do so, we have developed the mathematical theory to calculate the robustness of k-partite networks to random node failures. The developed theory mathematically uncovers the continuous phase transition of the robustness of k-partite networks under random node failures. In order to check if the developed theory is correct, we experimentally test the robustness of computer generated k-partite networks. The experimental results and the one calculated using the proposed theory comply with each other, which proves that the proposed theory is correct.

ASSESSMENT OF RELIABILITY IN LOCAL SYSTEMS WITH RENEWABLE ENERGY SYSTEMS

The use of renewable energy installations in local systems affects the reliability of power supply. At the moment, in order to assess the reliability of power supply in local systems with renewables, there is a large number of indicators that are not directly related to each other. Reliability is one of the key concepts in electricity. The reliability of the power system is a feature of the continuous supply of electricity to consumers to meet their needs. The concept of reliability includes such features as failure-free, durability and maintainability. But this terminology has not become commonplace, it has not embraced or streamlined many terms for reliability. Thus, many authors use the concepts of structural, mode (functional) and balance reliability. This separation is conditional, first, to simplify the solution of the task of assessing the reliability of complex networks, and secondly, based on quantitative assessments of the components, measures can be taken to improve the level of reliability. Changes in energy sector related to the implementation of market relations and disyributed generation require an update of terminology that can be used to decompose the reliability assessment task. Because assessing the reliability of distribution grids in today's environment is quite a challenge. The purpose of the article is to analyze the reliability of power supply for local systems with renewables. One of the main issues in this topic, despite the large amount of research work involved, is the uncertainty of the method for calculating the reliability of power supply in Microgrids. The paper assesses the reliability of power supply and proves the need to introduce new indicators for local systems with renewables, since distributed energy sources, including renewables, affects systems reliability. Such indicators will allow assessing the reliability by each factor individually, and will show the impact of each factor on the overall state of the system.

Fractal-Based Reliability Measure for Heterogeneous Manufacturing Networks

Nowadays, production-oriented manufacturing is transforming to service-oriented one, which leads to an increasing demand for high reliability of manufacturing systems. However, a popular approach to measuring terminal reliability of complex networked systems is based on graph theory, which has been shown to be an NP-hard problem. Though the NP-hard problem can be avoided by employing a statistical measure method for terminal reliability of random networks based on percolation theory, there is still lack of a general assessment approach to calculating terminal reliability of heterogeneous complex networks in intelligent manufacturing. In this paper, we propose a novel fractal-based approach to measuring the terminal reliability of heterogeneous networks. With help of the renormalization procedure that coarse grains a network into boxes containing nodes within given lateral size and inverse renormalization, which gives a fractal network growth model, a fractal network approximation of an arbitrary complex network is obtained. This fractal network topology can be described by a superposition of fractal elements based on fractal theory. Following this description, terminal reliability is the function of reliability of fractal elements. Then, a reliability assessment algorithm with computational complexity $O(N^2)$ based on fractal elements is developed. Numerical simulation is performed on a real network and a fractal network to validate the effectiveness of our method.

What network motifs tell us about resilience and reliability of complex networks

Network motifs are often called the building blocks of networks. Analysis of motifs has been found to be an indispensable tool for understanding local network structure, in contrast to measures based on node degree distribution and its functions that primarily address a global network topology. As a result, networks that are similar in terms of global topological properties may differ noticeably at a local level. This phenomenon of the impact of local structure has been recently documented in network fragility analysis and classification. At the same time, many studies of networks still tend to focus on global topological measures, often failing to unveil hidden mechanisms behind vulnerability of real networks and their dynamic response to malfunctions. In this paper, a study of motif-based analysis of network resilience and reliability under various types of intentional attacks is presented, with the goal of shedding light on local dynamics and vulnerability of networks. These methods are demonstrated on electricity transmission networks of 4 European countries, and the results are compared with commonly used resilience and reliability measures.

Analytical probability propagation method for reliability analysis of general complex networks

Reliability analysis of complex networks is often limited by large and exponentially increasing computational requirements with system size. In this paper, a new approximated analytical method the authors call the probability propagation method (PrPm) is proposed to calculate the reliability of general complex networks. The proposed method originates from the idea of belief propagation for inference in network graphs to pass a joint probability distribution between nodes in the network. At each step, the distribution is updated and passed as the message to its direct neighbors. After the message passes to the terminal node, an approximation of the network reliability is found. In this paper, the derived updating rules for message passing are provided, as well as a precise formulation of the error compared to the exact solution. The method is applied to three test applications: an example from a previous study on network reliability, a power distribution network, and a general grid network. Results from these applications show high accuracy for the proposed method compared to exact solutions where possible for comparison. In addition, the authors show orders of magnitude increases in computational efficiency of PrPm compared to existing approaches. This includes reducing the computational cost for analyses from an exponential increase in computation time with the size of the system to a quartic increase. The proposed PrPm enables accurate and computationally tractable reliability assessments of larger, complex networks.

Complex Network Reliability Analysis based on Entropy Theory

Complex Network Reliability Analysis based on Entropy Theory

Approximate Method to Evaluate Reliability of Complex Networks

This paper deals with the issue of reliability evaluation in complex networks, in which both link and node failures are considered, and proposes an approximate method based on the minimal paths between two specified nodes. The method requires an algorithm for transforming the set of minimal paths into a sum of disjoint products (SDP). To reduce the computation burden, in the first stage, only the links of the network are considered. Then, in the second stage, each term of the set of disjoint link-products is separately processed, taking into consideration the reliability values for both links and adjacent nodes. In this way, a reliability expression with a one-to-one correspondence to the set of disjoint products is obtained. This approximate method provides a very good accuracy and greatly reduces the computation for complex networks.

Research on reliability of complex network for estimating network reliability

Network reliability is an important index in measuring the reliability of large-sized network, but network reliability calculation is a NP-hard problem, and simulation is a feasible approach to estimating network reliability. Aiming at the problem of reliability evaluation in a complex network, dev elop a general scheme that combines Crude Monte Carlo and event-driven, and a novel reliability assessment method based on event-driven is put forward. The unbiased and the accurate estimation of the proposed method are analyzed from a theoretical point of view. Experimental results demonstrate that the proposed method is more efficient than other algorithms, such as high simulation efficiency, fine estimation accuracy and greatly reducing the algorithm complexity.

The Research of Computer Complex Network Reliability Evaluation Method Based on GABP Algorithm

With the wide application of computer network, network security issues become increasingly prominent. Currently, the most commonly used network security evaluation method is risk assessment method, as well as to test and evaluation system of safety degree by software vulnerability scanning tools and so on. However, network security is related to computer, communication, physics, mathematics, biology, management, social, and many other fields, which is complicated system engineering. As a result, poor operational safety exist in the existing method, thus in this paper, the research of computer complex network reliability evaluation method based on GABP algorithm is put forward.

Simulation Research of Space-Time Evolution of Emergency Logistics Network Reliability Based on Complex Network Theory

We propose the conception and evaluation indexes of emergency logistics network connecting reliability to construct evaluation index system of complex network reliability, and describe these indexes quantitatively to evaluate the network connecting reliability. Moreover, the network topological model and the simulation methods of reliability measurement when the network is under attack are present. Finally, we take three classical emergency logistics networks as examples, and through emulation analysis we obtain the connecting reliability changing situation of these three networks under random attack, the changing curve of the ratio of effective demand nodes and emergence supply mileage of emergency logistics network with same network density but different forms, and then evaluate the emergency logistics network connecting reliability. This can provide references for the designing of emergency logistics network with high reliability and analysis means for research in other fields.

Network reliability evaluation

AbstractThis article, beyond presenting a spectrum of network reliability methods studied in the past decades, describes a scalable innovative ‘overlap technique’ to tackle large complex networks' reliability evaluation difficulties, which cannot be handled by straightforward reliability block diagramming (RBD) techniques used for the simple parallel‐series topologies. Examples are shown on how to apply the overlap algorithm to compute the ingress‐egress reliability. Monte Carlo simulations demonstrate the methods discussed. (1) Static (time independent), (2) dynamic (time dependent) using a versatile Weibull distribution to represent the multiple stages of network components from infancy to useful life period and to wear‐out, and (3) multistate versions to include derated behavior beyond conventional working and nonworking states, are illustrated for calculating the directional source‐target (s‐t) reliability of complex networks by using the Java software ERBDC: Exact Reliability Block Diagramming Calculator. Copyright © 2010 John Wiley & Sons, Inc.This article is categorized under: Statistical and Graphical Methods of Data Analysis > Reliability, Survivability, and Quality Control

A Particle Swarm Optimization Approach Based on Monte Carlo Simulation for Solving the Complex Network Reliability Problem

Reliability optimization has been a popular area of research, and received significant attention due to the critical importance of reliability in various kinds of systems. Most network reliability optimization problems are only focused on solving simple structured networks (e.g., series-parallel networks) of which the reliability function can be easily obtained in advance. However, modern networks are usually very complex, and it is impossible to calculate the exact network reliability function by using traditional analytical methods in limited time. Hence, a new particle swarm optimization (PSO) based on Monte Carlo simulation (MCS), named MCS-PSO, has been proposed to solve complex network reliability optimization problems. The proposed MCS-PSO can minimize cost under reliability constraints. To the best of our knowledge, this is the first attempt to use PSO combined with MCS to solve complex network reliability problems without requiring knowledge of the reliability function in advance. Compared with previous works to solve this problem, the proposed MCS-PSO can have better efficiency by providing a better solution to the complex network reliability optimization problem.