Delivery reliability of computer networks for data transmission within the permitted packet error rate and latency

From the viewpoint of quality of service, packet error rate (PER) and latency are both critical performance indicators to assess internet quality for supervisor and customers. A computer system is usually modeled as a network topology with arcs and vertices where each arc denotes a delivery medium and each vertex denotes an Internet data center. Each component (arc and vertex) of a network should be considered as multi-state owing to the failure, partial failure, maintenance, etc., of the components. Evaluating the delivery reliability of a network with imperfect vertices is a complicated process. This type of network, called a multi-state imperfect vertex computer network, is addressed in this paper. We study how data can be delivered through multiple minimal paths simultaneously within the permitted PER and latency. An algorithm is proposed to assess delivery reliability. To show the efficiency and effectiveness of the proposed solution, we implemented the proposed solution on practical computer networks.     

基于Z语言和贝叶斯网络的存储系统可靠性评估方法

现有的存储系统可靠性评估方法难以动态地描述其可靠性特征,也难以反映其与时间的关系;针对此问题,提出了一种结合Z语言、连续时间马尔科夫链以及贝叶斯网络的可靠性评估方法;该方法将存储系统抽象为两级,分为功能单元和存储系统整体两部分;在针对功能单元进行可靠性评估时,建立了一种基于Z语言的可靠性模型,由于其关于时间的状态转移符合Markov链的性质,因此引入连续时间马尔科夫链进行评估;为了更清晰地表达存储系统与各个功能单元之间的多状态关系,构建了功能单元失效率与存储系统整体失效率之间的贝叶斯网络;基于贝叶斯网络计算出存储系统的整体可靠性,并得到其可靠性关键模块;最后,通过实例分析与计算表明,基于提出的可靠性评估方法能够准确描述存储系统的状态转移,可以量化计算存储系统的可靠性,得到其可靠度,具有一定的实用价值。     

双层多态加权$k/n$系统可用性模型与冗余设计优化

鉴于当前多态系统可用性模型刻画分层性能要求的不足,定义一种更加通用的双层多态加权$k/n$系统,提出新的分层运算符结合通用生成函数的方法解决权重的跨层次依赖问题,建立系统可用度模型;针对多态系统冗余设计面临的“子系统和部件综合选择问题”,构建可用度约束下的系统总成本优化模型,利用遗传算法编程获取各子系统和部件的经济数量;以某供电系统为例验证所提出模型及方法的正确性和有效性,并对比说明双层性能要求对系统可用性和经济性的影响. 研究成果可为复杂系统在不同性能要求下的设计优化提供决策支持.     

基于模糊着色Petri网的多状态系统可靠性分析

在多状态系统中,由于性能退化、局部失效等原因,系统或元件会表现出从完全失效到完美工作之间的一系列过渡状态,这些过渡状态信息将直接影响系统的可靠性。针对这一问题,提出一种模糊着色Petri网模型,该模型通过对模糊状态及状态变迁进行建模来刻画多状态系统的可靠性特征。在这种模糊着色Petri网模型中,变迁阈值会随着节点状态模糊信息的动态变化而发生变化,为此提出自适应模糊推理算法进行阈值调整,为多状态系统模型的可靠性分析及性能优化提供指导。通过对多状态系统可靠性分析进行验证,表明研究提出的模糊着色Petri网及参数调整策略合理有效,有利于提高多状态系统整体性能。     

Uncertainty Analysis in Software Reliability Modeling by Bayesian Analysis with Maximum-Entropy Principle

In software reliability modeling, the parameters of the model are typically estimated from the test data of the corresponding component. However, the widely used point estimators are subject to random variations in the data, resulting in uncertainties in these estimated parameters. Ignoring the parameter uncertainty can result in grossly underestimating the uncertainty in the total system reliability. This paper attempts to study and quantify the uncertainties in the software reliability modeling of a single component with correlated parameters and in a large system with numerous components. Another characteristic challenge in software testing and reliability is the lack of available failure data from a single test, which often makes modeling difficult. This lack of data poses a bigger challenge in the uncertainty analysis of the software reliability modeling. To overcome this challenge, this paper proposes utilizing experts' opinions and historical data from previous projects to complement the small number of observations to quantify the uncertainties. This is done by combining the maximum-entropy principle (MEP) into the Bayesian approach. This paper further considers the uncertainty analysis at the system level, which contains multiple components, each with its respective model/parameter/ uncertainty, by using a Monte Carlo approach. Some examples with different modeling approaches (NHPP, Markov, Graph theory) are illustrated to show the generality and effectiveness of the proposed approach. Furthermore, we illustrate how the proposed approach for considering the uncertainties in various components improves a large-scale system reliability model.     

Routing policy of stochastic-flow networks under time threshold and budget constraint

The quickest path problem is to find a path which sends a given amount of data from the source to the sink such that the transmission time is minimized. More specifically, the capacity of each arc in the network is assumed to be deterministic. However, in many real-life networks such as computer systems, telecommunication systems, etc., the capacity of each arc is stochastic due to failure, maintenance, etc. Such a network is named as stochastic-flow network. Hence, the minimum transmission time is not a fixed number. The purpose of this paper is to provide a decision procedure for a stochastic-flow network under the time and budget constraints. We try to evaluate the probability that d units of data can be sent through the network under both time threshold and budget according to the routing policy. Such a probability is named the system reliability, which is a performance index to measure the system quality. An efficient algorithm is proposed to derive the optimal routing policy with highest system reliability. The sensitive analysis can be conducted to improve the most important component which increases the system reliability most significantly.     

对弧进行删除的Bayes网络近似方法

弧的删除是一种对Bayes网络模型进行近似的方法。文中以Kullback-Leibler偏差作为近似网络和原网络概率分布误差的测度,给出了近似网络在此测度意义下的最优参数。同时,也给出了通过对原网络删除多条弧进行近似的启发式算法,当给定一个误差上界时,可以使用此算法寻找满足误差要求的近似网络。     

基于边状态枚举计算多状态网络可靠度动态界

为降低计算多状态网络可靠度的复杂性,综合考虑网络中具有多态性的边处于各中间状态的概率及从某中间状态转换到相邻状态对网络性能的影响,提出了一种基于边状态枚举计算多状态网络可靠度上下界的算法.该算法首先令网络中各边仅取完全工作和完全失效两种状态,将处于中间状态的概率分别叠加到完全工作和完全失效状态的概率上,得到可靠度上下界的初始值;而后按照对可靠度影响递减的顺序迭代枚举边的中间状态,通过集合间的比较,计算可靠度上下界的改变值,同时获得不断减小的可靠度上界和不断增加的可靠度下界,使其最终收敛于可靠度精确值.该算法不需提前求取网络d-最小割(路)集,且枚举较少的网络状态即可得到紧凑的可靠度上下界.相关引理的证明及算例分析验证了该算法的正确性和有效性.     

Research on the Application of Bayesian Network in Agriculture Intelligent System

Bayesian network is a strong tool for uncertain knowledge representation and inference. This paper mainly introduces some technologies and methods about Bayesian network based on intelligent system. In the construction of Bayesian network, divorcing technology and noisy-or technology are used. In the inference of Bayesian network, VE algorithm and sampling algorithm are introduced. Finally, Bayesian network construction component and inference component are developed. Then an expert system about cow disease diagnosis is constructed based on the two components.     

Reliability evaluation and component importance measure for manufacturing systems based on failure losses

Little work has been done to assess the reliability of a vital system like the manufacturing system. In this article, a novel and effective system reliability evaluation method in terms of failure losses has been proposed for manufacturing systems of job shop type, and then the failure losses based component importance measure (CIM) is used for importance analysis of equipment. The former indicates the present system reliability situation and the latter points the way to reliability improvement efforts. In this scheme, the problem is described and modeled by a dynamic directed network. Consider that the actual processing time of machines is to contribute to failure occurrence, it is used to calculate the failure times and failure losses. The obtained total failure times and failure losses of the system are applied to evaluate its reliability. Techniques to estimate two kinds of failure losses based CIMs are presented. They offer guidelines to realize system reliability growth cost-effectively. A case study of a real job shop is provided as an example to demonstrate the validity of the proposed methods. Comparison to other commonly used methods shows the efficiency of the proposed methods.     

基于构件影响因子的软件可靠性评估方法

为提高构件式软件系统可靠性评估的准确性,使软件系统的优化效率得到提高,提出一种基于构件影响因子的软件可靠性评估方法.基于构件式软件系统具有的复杂网络特性,使用引入构件转移概率的加权PageRank算法评估构件的影响因子,将构件的影响因子引入到离散时间马尔科夫链的可靠性评估模型中,评估软件系统的可靠性.实验结果表明,该方...     

Automated nonlinear system modelling with multiple neural networks

This article discusses the identification of nonlinear dynamic systems using multi-layer perceptrons (MLPs). It focuses on both structure uncertainty and parameter uncertainty, which have been widely explored in the literature of nonlinear system identification. The main contribution is that an integrated analytic framework is proposed for automated neural network structure selection, parameter identification and hysteresis network switching with guaranteed neural identification performance. First, an automated network structure selection procedure is proposed within a fixed time interval for a given network construction criterion. Then, the network parameter updating algorithm is proposed with guaranteed bounded identification error. To cope with structure uncertainty, a hysteresis strategy is proposed to enable neural identifier switching with guaranteed network performance along the switching process. Both theoretic analysis and a simulation example show the efficacy of the proposed method.     

System reliability for non-repairable multi-state series–parallel system using fuzzy Bayesian inference based on prior interval probabilities

In this article, the fuzzy concepts are applied in analysis of the system reliability problem. The fuzzy number is used to construct the fuzzy reliability of the non-repairable multi-state series–parallel system (NMSS). The fuzzy failure rate function is represented by an exponential fuzzy number. By using this innovative approach, the fuzzy system reliability of NMSS is created. In order to analyse this fuzzy system reliability, the fuzzy Bayesian point estimate of fuzzy system reliability is made by the conventional Bayesian formula. And, the posterior fuzzy system reliability of NMSS is developed by Bayesian inference with fuzzy probabilities. Finally, the performance of the method is measured by the mean square error of fuzzy Bayesian point estimate for the fuzzy system reliability of NMSS.     

面向维修过程的多态混联系统综合重要度计算方法

在对综合重要度（Integrated importance measure,IIM）计算方法研究的基础上,面向维修过程,分别给出了多态并-串联和串-并联系统综合重要度具体化计算公式及其推论. 在假设组（部）件的状态转移过程为不可约连续时间马氏链的条件下,推论给出了多态并-串联和串-并联系统组（部）件综合重要度的等价计算公式,等价计算公式将重要度计算时间复杂度从平方阶降为线性阶和常数阶. 数值仿真演绎了多态混联系统重要度计算过程,验证了综合重要度计算公式及其等价计算公式的一致性.     

Importance analysis by logical differential calculus

The physical objects or processes include many interconnected components representing a complex systems. Their reliability analysis usually considers two states interpreted as failure and operability. They are described in terms of the binary mathematical model. Importance analysis of the system elements is a traditional component of the reliability analysis. It enables one to estimate the impact of individual components on the system’s operability or failure. The present paper proposed a new approach to analysis and estimation of the system importance on the basis of the logical differential calculus.     

Penalty guided bees search for redundancy allocation problems with a mix of components in series–parallel systems

This paper uses a penalty guided strategy based on an artificial bee colony algorithm (PGBC) to solve the redundancy allocation problem (RAP) in reliability series–parallel systems. The penalty strategy was designed to eliminate the equalities in constraints and formulate new objective operators which guarantee feasibility within a reasonable execution time. The PGBC is used to deal with two kinds of RAPs with a mix of components. In the first example, the RAPs are designed to find the appropriate mix of components and redundancies within a system in order to either minimize the cost in the context of a minimum level of reliability, or maximize reliability subject to a maximum cost and weight. The second example involves RAPs of multi-state series–parallel reliability structures, wherein each subsystem can consist of a maximum of two types of redundant components. The objective is to minimize the total investment cost of system design while satisfying system reliability constraints and the consumer load demands. There are five multi-state system design problems which have been solved for illustration in this example. The experimental results show that the PGBC can significantly outperform other existing methods in the literature with less cost, higher reliability, and a significantly shorter computational time.     

基于GO法的瓦斯报警器可靠性分析

针对于瓦斯报警器的多态问题,采用了GO法对其可靠性进行分析。根据瓦斯报警器的工作原理与结构特点建立GO图。并在GO图的基础上对瓦斯报警器系统进行了定性和定量的分析运算,得到了瓦斯报警器的系统状态概率和系统的最小割集,用于分析报警器系统不同故障的产生原因。通过运算分析表明：GO法同样适用于多状态系统的可靠性分析。     

Performance evaluation of subsea BOP control systems using dynamic Bayesian networks with imperfect repair and preventive maintenance

The work presents a dynamic Bayesian networks (DBN) modeling of series, parallel and 2-out-of-3 (2oo3) voting systems, taking account of common-cause failure, imperfect coverage, imperfect repair and preventive maintenance. Seven basic events of one, two or three component failure are proposed to model the common-cause failure of the three-components-systems. The imperfect coverage is modeled in the conditional probability table by defining a coverage factor. A multi-state degraded component is used to model the imperfect repair and preventive maintenance. Using the proposed method, a DBN modeling of a subsea blowout preventer (BOP) control system is built, and the reliability and availability are evaluated. The mutual information is researched in order to assess the important degree of basic events. The effects of degradation probability, failure rate and mean time to repair (MTTR) on the performances are studied. The results show that the repairs and maintenance can improve the system performance significantly, whereas the imperfect repair cannot degrade the system performance significantly in comparison with the perfect repair, and the preventive maintenance can improve the system performance slightly in comparison with the imperfect repair. In order to improve the performance of subsea BOP control system, the single surface components and the components with all-common-cause failure should given more attention. The influence of degradation probability on the performance is in the order of PLC, PC and ES. The influence of failure rate and MTTR on the performance is in the order of PLC, ES, PC, DO, DI and AI.     

Reliability evaluation and optimal design in heterogeneous multi-state series-parallel systems

This paper addresses the heterogeneous redundancy allocation problem in multi-state series-parallel reliability structures with the objective to minimize the total cost of system design satisfying the given reliability constraint and the consumer load demand. The demand distribution is presented as a piecewise cumulative load curve and each subsystem is allowed to consist of parallel redundant components of not more than three types. The system uses binary capacitated components chosen from a list of available products to provide redundancy so as to increase system performance and reliability. The components are characterized by their feeding capacity, reliability and cost. A system that consists of elements with different reliability and productivity parameters has the capacity strongly dependent upon the selection of constituent components. A binomial probability based method to compute exact system reliability index is suggested. To analyze the problem and suggest an optimal/near-optimal system structure, an ant colony optimization algorithm has been presented. The solution approach consists of a series of simple steps as used in early ant colony optimization algorithms dealing with other optimization problems and offers straightforward analysis. Four multi-state system design problems have been solved for illustration. Two problems are taken from the literature and solved to compare the algorithm with the other existing methods. The other two problems are based upon randomly generated data. The results show that the method can be appealing to many researchers with regard to the time efficiency and yet without compromising over the solution quality.     

Reliability improvement of large multi-state series-parallel systems

In the paper, excess methods for improving the reliability of multi-state series-parallel systems are presented: for the hot reserve of single components, the cold reserve of single components, and the mixed (hot and cold) reserve of single components. A process is also introduced to improve the reliability of these methods by replacing their components with more reliable ones. New theorems for multi-state limit reliability functions in homogeneous and non-homogeneous series-parallel large systems composed of components with improved reliability are presented, and applied to compare the effects of these systems in different reliability improving methods.