基于无效状态空间的多状态网络可靠性评估

在应用d-最小割(路)集计算多状态网络可靠度精确值算法中,运用容斥原理求解d-最小割(路)集较为复杂。为此,提出一种不需d-最小割(路)集直接计算多状态网络可靠度精确值的算法。该算法按一定规则分割状态空间,在此基础上生成无效状态空间,通过迭代计算直接获得可靠度精确值,同时通过定义边的容量下界及剩余网络。实例分析结果表明,运用该算法可减少计算量,并能精确求解d-最小割(路)集。     

多状态网络可靠度下界的矩阵分解算法

为减少计算多状态网络可靠度精确值的复杂性,提出基于分解计算多状态网络不可靠度精确值的思想,在此基础上提出一个求解多状态网络不可靠度动态上界(对应于可靠度动态下界)的算法.算法先通过分解运算去除某些边引起的d-最小割集之间的相关性,将网络不可靠度转化为多个互斥事件的概率之和,再应用MESP界求取这些事件的概率,计算网络不可靠度上界,对应得到可靠度下界,并计算了得到的可靠度下界与精确值间的绝对误差界.通过定义d-最小割集矩阵,利用矩阵分解实现算法,结构清晰、便于编程计算.相关引理的证明及算例分析表明随着分解的深入,算法能够得到满足精度要求的可靠度下界.     

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.     

基于网络化简和向量集分解的网络两终端可靠度算法*

可靠度是衡量网络性能最重要的指标之一,不交和算法和因子分解算法是计算网络可靠度最重要的两种方法。不交和算法需要提前枚举网络所有极小路或极小割,因子分解算法虽然不需要枚举极小路或极小割,但每次只能分解一条边的状态。为了克服这两种算法的不足,基于网络化简和向量集分解,提出一个计算网络可靠度的高效、实用算法。该算法具有如下特点：a）算法首先求得网络的不可靠度,进而可得网络的可靠度;b）算法不需要提前枚举网络所有极小路和极小割;c）通过引入网络化简操作和向量集分解方法,算法每次可以分解多条边的状态,从而能更快速、     

基于二进制的集合运算研究

通过比较二进制与集合之间的内在联系,提出了基于二进制的集合运算思想,给出了基于二进制的各种集合运算算法,该算法有效解决了传统集合操作算法中运算速度慢,效率低的不足,并提供了求幂集,交集,并集等集合运算算法的c语言源程序。     

基于d-分隔的Credal推理网络约简算法研究

针对Credal网络推理应用中出现的Credal集顶点组合爆炸等问题,系统地提出了Credal推理网络约简的概念,并利用CredaI网络中变量间的d-分隔特性,通过变量间独立性验证,设计了求解大规模Credal推理网络约简的算法.应用实例表明,该算法简化了目标网络,有效规避了Credal集顶点组合爆炸问题,对特定推理问题具有适用性和可行性,提高了Credal网络推理的效率.     

逆P（ρ（σ）,η（τ））-集合与其代数性质

将迁移的随机特性引入逆P-集合（ XˉF,XˉFˉ）,给出了逆P（ρ（σ）,η（τ））-集合的概念与结构;讨论了内逆P（ρ（σ）,η（τ））-集合与逆P-集合的关系,即逆P-集合是逆P（ρ（σ）,η（τ））-集合的特例,逆P（ρ（σ）,η（τ））-集合是逆P-集合的推广;给出逆P（ρ（σ）,η（τ））-集合的交集、并集、补集与差集四种运算,讨论了逆P（ρ（σ）,η（τ））-集合的代数性质。     

BNs中基于分层排序的条件独立分析与应用

贝叶斯网络（BN）是图论和概率论有机融合的概率图形模型。D-分割（directional separation）和ud-分割（unidirectional separation）是判断贝叶斯网络中条件独立的两个重要的准则。讨论了贝叶斯网络中d-分割和ud-分割的关系,得到d-分割是ud-分割的充分不必要条件。介绍分层排序的定义并给出了d-分割和ud-分割同时成立的条件。通过对贝叶斯网络的节点进行分层排序,能够得到该贝叶斯网络的拓扑序列并且能够快速找到d-分割和ud-分割集识别有向因果效应。     

计算网络两终端可靠度的新分解算法

网络可靠度是衡量网络性能的一个核心指标,随着网络模型被广泛应用于现实生活,人们对网络可靠度的研究也越来越重视。针对不交和算法和因子分解算法在计算网络可靠度方面存在的不足,给出一个计算网络两终端可靠度的新分解算法。该算法具有如下的优点：不需要提前枚举网络的所有极小路和所有极小割;通过引入网络化简操作和新的分解技术。该算法每次可以分解多条边的状态,从而它能够更快速、更高效地去分解网络的状态向量集,使得网络可靠度的计算更简单,更高效。通过实例以及和其他算法的比较验证了所提出算法的正确性和有效性。     

基于数据仓库的高效关联规则的挖掘

提出在基于数据仓库的关联规则发现中生成最小关联规则集来代替完全关联规则集，最小关联规则集必须具备两个条件：（1）最小关联规则集是最小、最简单的关联规则集合；（2）最小关联规则集与完全关联规则集有相同的置信度。通过最小关联规则集，可以有效地剪除弱关联规则，大幅度减少候选频繁项目集，从而提高规则发现效率。是后，在传统经典算法Apriori基础上设计了一个相应的高效算法。     

Robust feedforward and recurrent neural network based dynamic weighted combination models for software reliability prediction

Traditional parametric software reliability growth models (SRGMs) are based on some assumptions or distributions and none such single model can produce accurate prediction results in all circumstances. Non-parametric models like the artificial neural network (ANN) based models can predict software reliability based on only fault history data without any assumptions. In this paper, initially we propose a robust feedforward neural network (FFNN) based dynamic weighted combination model (PFFNNDWCM) for software reliability prediction. Four well-known traditional SRGMs are combined based on the dynamically evaluated weights determined by the learning algorithm of the proposed FFNN. Based on this proposed FFNN architecture, we also propose a robust recurrent neural network (RNN) based dynamic weighted combination model (PRNNDWCM) to predict the software reliability more justifiably. A real-coded genetic algorithm (GA) is proposed to train the ANNs. Predictability of the proposed models are compared with the existing ANN based software reliability models through three real software failure data sets. We also compare the performances of the proposed models with the models that can be developed by combining three or two of the four SRGMs. Comparative studies demonstrate that the PFFNNDWCM and PRNNDWCM present fairly accurate fitting and predictive capability than the other existing ANN based models. Numerical and graphical explanations show that PRNNDWCM is promising for software reliability prediction since its fitting and prediction error is much less relative to the PFFNNDWCM.     

Forecasting of Software Reliability Using Neighborhood Fuzzy Particle Swarm Optimization Based Novel Neural Network

This paper proposes an artificial neural network (ANN) based software reliability model trained by novel particle swarm optimization (PSO) algorithm for enhanced forecasting of the reliability of software. The proposed ANN is developed considering the fault generation phenomenon during software testing with the fault complexity of different levels. We demonstrate the proposed model considering three types of faults residing in the software. We propose a neighborhood based fuzzy PSO algorithm for competent learning of the proposed ANN using software failure data. Fitting and prediction performances of the neighborhood fuzzy PSO based proposed neural network model are compared with the standard PSO based proposed neural network model and existing ANN based software reliability models in the literature through three real software failure data sets. We also compare the performance of the proposed PSO algorithm with the standard PSO algorithm through learning of the proposed ANN. Statistical analysis shows that the neighborhood fuzzy PSO based proposed neural network model has comparatively better fitting and predictive ability than the standard PSO based proposed neural network model and other ANN based software reliability models. Faster release of software is achievable by applying the proposed PSO based neural network model during the testing period.      

基于可靠性的5G-低轨星座网络切片映射算法

随着国家天地一体化信息网络重大项目的 推进,5G-低轨星座网络切片的可靠映射成为业内的研究热点.在基于软件定义网络(software-defined network,SDN)和网络功能虚拟化(network function virtualization,NFV)的5G-低轨星座集成网络架构下,将5G-低轨星座网络切片的可靠映射问题建模为一个混合整数线性规划问题.在此基础上,研究了切片请求的资源编排,进而提出了基于广度优先搜索的可靠映射算法.该算法综合考虑切片请求的可靠性阈值及虚拟网络功能(virtual network function,VNF)的资源需求,在虚拟网络映射中根据节点的可靠重要度对节点进行排序.仿真结果表明,该算法在满足可靠性阈值约束的条件下,能够最大化收益开销比,提高虚拟网络映射成功率,在切片可靠性及接受率等方面优于对比算法.     

Reliability assessment of a stochastic node-failure network with multiple sinks under tolerable error rate

Internet service providers and enterprise customers are increasingly focusing on the tolerable error rate during transmission. This has high significance especially in service-level agreements. This study concentrates on a stochastic node-failure network (SNFN) with multiple sinks, where each component (arc or node) has several possible capacities and a transmission error rate. Measuring the performance level for an SNFN is an important issue in the system-design and system diagnosis phases. This study evaluates the system reliability of a network, or the probability that the demand is fulfilled under the tolerable error rate. In terms of minimal paths, we propose an efficient algorithm to find all minimal capacity vectors, and then compute the system reliability in terms of all minimal capacity vectors using the recursive sum of disjoint products algorithm. Finally, we test the proposed algorithm for a benchmark network and two practical networks.     

Using minimal cuts to optimize network reliability for a stochastic computer network subject to assignment budget

In our modern society, information and data are usually transmitted through a computer network. Since the computer network's reliability has a great impact on the quality of data transmission, many organizations devote to evaluating or improving network reliability, especially for network reliability optimization. This study focuses on such a confronted problem that is to find the optimal transmission line assignment to the computer network such that network reliability is maximized subject to the budget constraint. Each transmission line owns several states due to failure, maintenance, etc., and thus the computer network associated with any transmission line assignment is called a stochastic computer network. Network reliability is the probability that the computer network can transmit the specified units of data successfully. Because the discussed problem is NP-hard, an optimization algorithm that integrates the genetic algorithm, minimal cuts and Recursive Sum of Disjoint Products is proposed. Experimental results illustrate the solution procedure and show that the proposed algorithm can be executed in a reasonable time.     

Design of multi-path data routing algorithm based on network reliability

Data routing through an interconnected network is important and this paper addresses the design of a multi-path data routing algorithm based on network reliability. Generally, multiple routes for a given source–terminal pair exist in a data network and the best possible route based on network metrics like hop-count, delay, traffic, queue, etc. is selected by a routing algorithm. Since network reliability incorporates all these metrics, the routing decision based on reliability seems to be the best possible option and a distributed routing algorithm based on the source–terminal (s–t) path reliability has been proposed in this work. Each node in the proposed routing generates an adjacency matrix of a network graph by periodically exchanging connection information with the adjacent nodes and selects multiple routes based on reliability of the paths. We propose an implementation of a two-path routing algorithm that instead of one includes two next-hop nodes in each node’s routing table. An example is given for further illustration of the proposed algorithm.     

A stochastic node-failure network with individual tolerable error rate at multiple sinks

Many enterprises consider several criteria during data transmission such as availability, delay, loss, and out-of-order packets from the service level agreements (SLAs) point of view. Hence internet service providers and customers are gradually focusing on tolerable error rate in transmission process. The internet service provider should provide the specific demand and keep a certain transmission error rate by their SLAs to each customer. This paper is mainly to evaluate the system reliability that the demand can be fulfilled under the tolerable error rate at all sinks by addressing a stochastic node-failure network (SNFN), in which each component (edge or node) has several capacities and a transmission error rate. An efficient algorithm is first proposed to generate all lower boundary points, the minimal capacity vectors satisfying demand and tolerable error rate for all sinks. Then the system reliability can be computed in terms of such points by applying recursive sum of disjoint products. A benchmark network and a practical network in the United States are demonstrated to illustrate the utility of the proposed algorithm. The computational complexity of the proposed algorithm is also analyzed.