Computer Network Reliability Research Articles

Reliability Assessment for a Stochastic Air Transport Network with Multiple Demands

Air transport networks play a significant role in the modern tourism industry, and their operational performance and stability can be measured by reliability analysis. This study, from the perspective of a travel agency, attempts to assess the reliability of a stochastic air transport network (SATN) by considering multiple passenger demands generated by both the starting airport and several connection airports. The SATN is modeled as a stochastic flow network comprised of several nodes and arcs where nodes represent airports, and arcs represent flights connecting two airports. Each flight is associated with a predetermined stochastic capacity following a specific probability distribution, departure time, arrival time, and discount fare. Accordingly, network reliability refers to the probability that the network can successfully transport passengers from their respective source airports to the destination airport within the given travel time and travel cost limit. An algorithm in terms of the concept of lower boundary point is developed to compute network reliability. Furthermore, a simple example and a case study are presented to demonstrate the proposed reliability method and the managerial implications of reliability for travel agencies.

Network reliability evaluation of a supply chain under supplier sustainability

The United Nations’ sustainable development goals have brought attention to the pressing issue of enhancing supply chain sustainability and ensuring equitable order allocation. In contrast to the previous research, this study involves sustainability in network reliability evaluation. A new algorithm is proposed for computing network reliability, the probability that the current supply chain not only meets demand, budget, and suppliers’ production capacity but also considers the level of supplier sustainability. In the constructed multistate supply chain network (MSCN), a node represents a supplier, warehouse, assembler, or market, and a route connecting the nodes represents a carrier. The capacity of each carrier, represented by the available delivery containers, should not be deterministic as they may be partially reserved by other markets. Grounded in the Method Based on the Removal Effects of Criteria (MEREC) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), the supplier sustainability can be assessed, facilitating the selection of sustainable suppliers and the determination of feasible order allocations, which are essential for various industries. An audio production cooperation is used as a case study to demonstrate the utility of the proposed algorithm.

Computer Software Maintenance and Optimization Based on Improved Genetic Algorithm

Optimizing computer software maintenance is the key goal, which also ensures dependable and consistent network performance. In order to increase genetic operations and evaluate the satisfaction and fitness index functions, this article employs an improved genetic algorithm. Utilizing the network's performance and controlling restrictions through controlled data iterations, the architecture is refined. The study also finds a link between the number of iterations and the rate of network optimization, supporting the results of the genetic algorithm. The results show that the reliability of the network system decreases as the number of genetic operation repeats increases. If a critical point is reached, the enhancement in network reliability tends to level off due to hardware constraints or other relevant factors. Notably, the study identifies the maximum attainable value of network reliability at 0.894, precisely at 100 iterations. These conclusions offer an essential framework for optimizing the design of computer network reliability, emphasizing the necessity of a well-balanced approach to genetic algorithm-based optimization.

Reliability assessment and optimization of computer networks based on neural networks

Abstract Amidst the swift evolution of computer technologies, the prevalence of computer networks has become pivotal across diverse sectors, increasingly reliant on their robust functionality. This study rigorously evaluates and enhances the reliability of computer networks by leveraging an index system and an evaluation model devised through neural networks. To strengthen network dependability, this research employs a Hopfield neural network to address multi-constraint Quality of Service (QoS) multicast routing challenges, thereby elevating network reliability. Simulation experiments demonstrate that the Hopfield neural network effectively mitigates network latency and exhibits superior convergence performance compared to conventional QoS multicast routing methods. Further, this paper applies the neural network-based evaluation model to analyze the reliability of the air traffic network within the H-area after integrating the optimized computer network framework. It is observed that the most significant contributor to traffic flow loss is the network's degree value. An analysis of traffic flow density, employing actual sector flow data, reveals that high traffic volumes typically precipitate congestion. Nonetheless, the traffic flow density value consistently exceeds 100, suggesting that the enhanced computer network model holds practical applicability in real-world scenarios.

Optimisation of computer network reliability based upon sensor technology and genetic algorithm

Optimisation of computer network reliability based upon sensor technology and genetic algorithm

Mitigating DDoS Attacks via AI Detection and SDN Response

Distributed Denial of Service (DDoS) attacks pose a serious ongoing threat to the stability and reliability of computer networks. These attacks have advanced significantly in scale and complexity over the past decades.

Criteria of reliability of computer networks and assessment quality of service

A modern computer network counts tens of millions of servers and hundreds of millions of workstations. Network technologies have found widespread use in industry and business, so network reliability has become one of the most pressing problems.
 In general, the reliability of all systems is determined by the reliability of their constituent elements. Different elements have different reliability. In many cases, reliability and reliability distributions are determined empirically.
 If we define the reliability of the system as the possibility of communication between different nodes and workstations, then the reliability of the Internet turns out to be equal to the whole. Unless your workstation is among thousands of nodes, you cannot guarantee the absolute reliability of the network, so it is clear that both definitions are completely unacceptable.
 The selection of criteria for determining the reliability and performance of information computer networks is one of the main problems of information systems

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.

Research on the application of artificial intelligence in computer network technology in the era of big data

SummaryIn order to improve the security and reliability of computer network, especially the three aspects of fault network diagnosis overall model, computer network link fault detection and computer network congestion prediction, we propose a fuzzy neural network identification and prediction model. In addition, we use rough artificial neural network (RANN) network link mixture to manage all subnets in the network for unified remote allocation network, so as to improve the quality and robustness of the network. The results show that the RANN learning and recognition algorithm we introduced achieves the best performance with 85.12% reduction in total time and 86.82% reduction in the number of steps. In addition, the average error rate of RANN is reduced by 45.94% compared with Lagrangian neural storage algorithm. The iteration number of fuzzy neural network (FNN) neural network prediction model based on incremental rule extraction algorithm is reduced by 28.93%, and the packet loss rate of network peak shaving module controlled by FNN prediction model is reduced to 0.272%, which is 87.69% lower than that of random early detection algorithm. This is expected to provide better performance for the orderly control of network failure, data flow and network data transmission with higher load in the future, and further improve the security and reliability of computer networks.

Future Direction of AI in Block-chain for security systems – A Comprehensive Report

Currently, blockchain is a game-changing technology that's revolutionizing the way applications are built because it eliminates the requirement for trust between network peers. Global and immutable repositories created by blockchain technology provide non-repudiation and accountability of the stored data. Because of this, processing and maintaining enormous volumes of data with ever-decreasing latencies are becoming more difficult. Therefore, artificial intelligence and machine learning approaches have made substantial advancements, paving the way for next-generation network infrastructure. The decentralization and tamper-proof nature of blockchain technology make it ideal for data exchange and privacy protection. This study paradigm may improve computer network reliability while also allowing new distributed and knowledge-driven security services and applications. Numerous issues are addressed in this work, including new cryptographic models for healthcare applications, intelligent threat-detection systems and novel approaches to consensus building in blockchains.

Evaluating multi-state systems reliability with a new improved method

The computation of network reliability for a system with many states is an NP-hard issue. Finding all the minimum path vectors (d-MPs) lower boundary points for each level d is one of the few approaches for computing such dependability. This research proposed enhancements to the technique described in Chen's "Searching for d-MPs with rapid enumeration" paper. We propose additional adjustments to the method that creates the flow vector F in this enhancement. This decreases the number of required steps and the temporal complexity of the method. Comparing the newly suggested approach to the old algorithm reveals that the adjustment has increased the enumeration's efficiency and degree of complexity.

Genetic Algorithm-based Reliability of Computer Communication Network

With the rapid development of network technology and computer technology, network applications can be seen everywhere. From life, finance to politics, the development of all walks of life has been inseparable from computer communication networks. Once there is a problem with the computer communication network, it will cause serious economic losses and may even cause national turmoil. Therefore, the reliability requirements of computer communication networks are getting higher and higher, and the reliability analysis and application of computer communication networks have become the focus of research. Based on genetic algorithm, this paper optimizes the reliability of computer communication networks. Firstly, the theory of reliability of computer communication network is briefly introduced, and the factors affecting reliability are simply analysed. Secondly, based on the influencing factors, the reliability optimization analysis model of computer communication network is established. Finally, the genetic algorithm is used to solve the established reliability optimization analysis model. The experimental results show that with the increase in the number of iterations of the genetic algorithm for data regression calculation, the algorithm reaches more than 0.99, and the optimal solution is obtained, which improves the reliability of the network, and the adaptability of the network link also increases. The genetic algorithm is applied to the reliability analysis of computer communication network, and the corresponding reliability optimization method is designed, which can improve the reliability of computer network while reducing the cost.

Optimisation of computer network reliability based upon sensor technology and genetic algorithm

Optimisation of computer network reliability based upon sensor technology and genetic algorithm

Computer Network Reliability Improvement Method Based on Genetic Algorithm

With the continuous growth of economy and the continuous development of society, once the computer came out, it has been rapidly developed and popularized, especially now, people’s life is more and more inseparable from the computer, and they have a serious dependence on the computer. Some industries even directly use the computer as the business pillar, once such industries leave the computing industry Therefore, the reliability of computer network is a very important topic. It is very important to improve the reliability of computer network. In order to explore the effect of genetic algorithm on improving the reliability of computer network, two experimental groups are set up in this paper. The experimental group adds genetic algorithm in its computer network operation, while the control group uses the traditional algorithm. Finally, the experimental results show that the accuracy and security of the computer network in the experimental group are higher than those in the control group. For example, the highest accuracy and security of the experimental group are 99.78% and 98.83% respectively, while the highest accuracy and security of the control group are 84.31% and 88.58%. These experimental data show that the genetic algorithm can play a positive effect on the security of computer network.

Distributed Intrusion Detection Systems in Big Data: A Survey

We live in a time where data stream by the second, which makes intrusion detection a more difficult and tiresome task, and in turn intrusion detection systems require an efficient and improved detection mechanism to detect the intrusive activities. Moreover, handling the size, complexity, and availability of big data requires techniques that can create beneficial knowledge from huge streams of the information, which imposes the challenges on the process of both designing and management of both Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) in terms of performance, sustainability, security, reliability, privacy, energy consumption, fault tolerance, scalability, and flexibility. IDSs and IPSs utilize various methodologies to guarantee security, accessibility and reliability of enterprise computer networks. This paper presents a comprehensive study of the Distributed Intrusion Detection Systems in Big Data, and presents intrusion detection and prevention techniques that utilize machine learning, big data analytics techniques in distributed systems of the intrusion detection.

Modelling Reliability Characteristics of Technical Equipment of Local Area Computer Networks

Technical systems in the modern global world are rapidly evolving and improving. In most cases, these are large-scale multi-level systems and one of the problems that arises in the design process of such systems is to determine their reliability. Accordingly, in the paper, a mathematical model based on the Weibull distribution has been developed for determining a computer network reliability. In order to simplify calculating the reliability characteristics, the system is considered to be a hierarchical one, ramified to level 2, with bypass through the level. The developed model allows us to define the following parameters: the probability distribution of the count of working output elements, the availability function of the system, the duration of the system’s stay in each of its working states, and the duration of the system’s stay in the prescribed availability condition. The accuracy of the developed model is high. It can be used to determine the reliability parameters of the large, hierarchical, ramified systems. The research results of modelling a local area computer network are presented. In particular, we obtained the following best option for connecting workstations: 4 of them are connected to the main hub, and the rest (16) are connected to the second level hub, with a time to failure of 4818 h.

Investigating the Interaction between Energy Consumption, Quality of Service, Reliability, Security, and Maintainability of Computer Systems and Networks.

The Conference on Energy Consumption, Quality of Service, Reliability, Security, and Maintainability of Computer Systems and Networks (EQSEM) was held as a virtual conference on October 20–21, 2020. This paper summarises the objectives and proceedings of this conference. It then briefly presents the keynotes and other papers which were presented. Then, in the context of the EU-funded research project SDK4ED which motivated this conference, we outline several solutions that are being developed for managing the potential inter-dependencies and corresponding trade-offs between design-time and runtime qualities in software applications, and review the key functionalities that have been implemented within the SDK4ED integrated platform as of this writing. Then, we briefly introduce four papers among those presented at the EQSEM Conference that are included in this issue of the journal SN Computer Science, presenting relevant research achievements of the SDK4ED Project on software maintainability, reliability, and energy efficiency.

A Power-Efficient and Quantum-Resistant N-Bit Cryptography Algorithm

With rapid technological advancements and enhanced network growth, security contends to play a crucial role. A powerful network security tends to point out diverse mixture of threats and intimidations and blocks them from creeping and getting circulated into the network to preserve the reliability, confidentiality, integrity, and accessibility of computer networks by annihilating illegitimate admittance and corruption of critical information. Secure hash algorithms (SHA) are cryptographic hash functions used to produce a hash value of fixed output bit sizes. In this paper, an algorithm is proposed to strengthen the cryptographic systems by using reversible logic to generate higher and variable hash values, making it difficult to trace the keys. The proposed scheme is simulated and verified using FPGA Virtex ML505 board, the analysis of power and time of which is carried out using Genus tool, proving it to be efficient in terms of power, gate usage, garbage, and quantum cost.

An efficient algorithm for computing network reliability in small treewidth

We consider the classic problem of Network Reliability. A network is given together with a source vertex, one or more target vertices, and probabilities assigned to each of the edges. Each edge of the network is operable with its associated probability and the problem is to determine the probability of having at least one source-to-target path that is entirely composed of operable edges. This problem is known to be NP-hard.We provide a novel scalable algorithm to solve the Network Reliability problem when the treewidth of the underlying network is small. We also show our algorithm’s applicability for real-world transit networks that have small treewidth, including the metro networks of major cities, such as London and Tokyo. Our algorithm leverages tree decompositions to shrink the original graph into much smaller graphs, for which reliability can be efficiently and exactly computed using a brute force method. To the best of our knowledge, this is the first exact algorithm for Network Reliability that can scale to handle real-world instances of the problem.

Optimal computer network based on graph topology model

In large computer network design, it is desirable to have connections which achieve the most efficient and reliable communication. In such design, there are several factors which should be considered. Two of the factors which seem to appear most frequently are, (a) the number of connection which can be attached to a computer is limited, and (b) a short communication route between any two computers is required. Thus, it will end up with a large network subject to these constraints. The computer network can be modelled as a graph, where each computer is represented by a vertex, and the connection between the two computers is represented by an edge. In this paper, we compare the efficiency and reliability of computer networks based on their graph topology model. The method used in this study is literature study, designing the new topology, and making simulation and testing using a software simulator called Graphical Network Simulator 3 (GNS3). The results of this study are the comparison of the application of prism and petersen graph topologies in computer networks topology.