基于离散概率模型的二端网络可靠性分析
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摘要
在经济生活日益依赖于通信网的今天,网络故障不仅会带来巨大的数据损失,甚至会导致灾难性后果。利用网络可靠性分析,工程人员可以增强网络可靠性,减少故障时损失。尽管网络可靠性分析已经取得了大量成果,但如下方面还需要开展进一步研究:
第一,网络结点不可靠时的可靠性分析。传统的网络可靠性分析通常假设结点可靠,这在一些新的网络研究领域是不成立的,譬如自组织网络、灾难环境网络。而且,当网络结点众多时,可靠性分析中的计算量将变得非常庞大。
第二,网络部件故障非统计独立时的可靠性分析。传统的网络可靠性分析通常假设部件故障统计独立,这会给某些网络的可靠度评估带来很大的偏差,譬如极端环境下的网络。另外,当非统计独立故障较多且网络规模较大时,可靠性分析将变得非常困难。
第三,网络部件重要性分析。传统的网络可靠性分析主要讨论可靠度计算,不能从部件故障角度全面了解网络可靠性,缺乏高效的计算方法。
第四,网络运行时可靠性分析。传统的网络可靠性分析主要研究可靠性设计指标,其成果不能很好地反映运行时的网络可靠性。
通信网有着庞大的规模和动态复杂性,经典的故障分布函数很难描述网络的故障特性。为降低复杂性,解决一般性问题,本文采用了主流的网络可靠性模型——时间独立的离散概率模型。另外,本文只讨论了二端网络的可靠性问题,但其成果可以推广到K端网络和全端网络。
针对网络可靠性分析中存在的上述问题,本文从四个方面开展了研究工作:
(1)研究了结点不可靠网络的可靠性分析,提出了基于标记因子划分的网络可靠度评估算法——MFP(Marked Factor Partition)和基于特征识别的street网络可靠度评估算法——SNR(Street Network Reliability),解决了部分规模较大而结点不可靠的网络可靠度评估问题。最后,针对无线传感网的众多不可靠结点,提出了能有效评估可靠度的算法——EF(EnhancedFactoring)。
1)MFP改进了基于有序二叉判定图(OBDD,Ordered Binary DecisionDiagram)的可靠性分析方法。在创建OBDD过程中,利用边替代(ER,EdgeReplacement)运算处理网络中的不可靠结点。同时,从两个方面减少了计算量:分解网络时,根据子网的标记因子划分来识别同构子网,避免分解同构子网带来的重复计算;利用OBDD来存储网络结点和边的状态,减少了冗余状态。
2)SNR是专门针对street网络的方法,其基本原理与MFP相同。SNR利用源端位置信息作为子网特征,识别street网络分解过程中出现的同构子网,从而减少重复计算。
3)增强因子分解算法在网络分解过程中采用hash表来存储同构子网的可靠度,提高了计算效率。
(2)研究了一类常见的非统计独立故障——共因失效,提出了分析共因失效网络可靠性的算法——CNR(Common-cause-failure NetworkReliability)和无线传感网可靠性的算法——WR(Wireless-sensor-networksReliability)。解决了部分共因事件较多且规模较大网络的可靠度评估问题,讨论了有共因失效的无线传感网可靠度评估问题。
1)CNR改进了基于OBDD的共因失效可靠性分析方法。为降低分析复杂性,CNR算法未考虑结点不可靠的情况。由于CNR方法只需要递归创建一个OBDD结构,在共因事件较多、网络规模较大时,这种方法可节省大量计算开销。关于如何创建共因失效网络的OBDD,本文又提出了两种方法:基于布尔运算的方法和基于共因变量集的方法。第一种方式比较灵活,第二种方式有较高的存储效率。
2)WR算法充分考虑了无线传感网中的大量共因事件和不可靠结点,借用了CNR创建共因失效网络OBDD的思路,利用结点扩张法来处理无线传感网中的不可靠结点。
(3)研究了网络部件重要性分析。讨论利用Birnbaum测度、关键重要度和风险增长区间等部件重要性指标来描述网络可靠性,提出了基于Birnbaum测度的链路重要性评估算法——BIL(Birnbaum Importance ofLink)和基于风险增长区间的网络可靠性评估算法——RI(Risk Increment)。BIL充分利用OBDD结构来高效计算Birnbaum测度、关键重要度和风险增长,发现网络薄弱环节,确定引发故障的最可能部件。而RI能够对部件故障时的网络稳定度进行分析,为网路抗毁性提供了又一个参考指标。另外,针对无线传感网中不可靠结点,提出了基于风险增长的结点重要性评估算法——WNI(Wireless Node Importance)。
(4)针对网络运行时可靠性分析,提出了两个可靠性测度:基于故障检测的指标——容错MPLS网络的可靠性测度;基于可用带宽测量的指标——自愈IP网络可靠性测度。最后,为分析容错MPLS网络运行时可靠性,设计并实现了一个开销较低速度较快的故障检测方法——自适应的LSP故障检测机制。
As the economy society depends on communication networks more and more, the network failures will bring about a great data loss or even disaster. With the network reliability analysis, the engineers can enhance the network reliability and reduce the loss when the failures take place. Although the network reliability analysis has been discussed widely, some works need to be furthered:
Firstly, the reliability analysis of network with imperfect nodes. The traditional assumption of perfect nodes is inappropriate for new fields such as ad hoc networks and some networks in disaster environment. Furthermore, the number of reliability computations will largely expand when there are many imperfect nodes.
Secondly, the reliability analysis of network with dependent componets failures. Because the componets failures are usually assumed to be s-independent (statisticly-independent), the traditianl methods overestimate the network reliability. Furthermore, this assumption is inappropriate for networks in extreme environment. When the network scale and number of dependent failures are large, the network reliability analysis will become difficult.
Thirdly, the importance analysis of network components. The tranditional methods mainly focus on the reliability values, and little reliability information can be known from the point of components failures. There needs some effective methods for the importance analysis of network components.
Fourthly, the runtime reliability analysis about networks. The tranditional methods mainly discuss the reliability metrics for design, which can not indicate the reliability when the networks are operating.
Because of the large scale and dynamic complexity, it is difficult to describe the network failures with those classic failure distribution functions. To simplify the complexity and resolve the general problem, this thesis makes use of current prevalent method--discrete probability models with time independence. Although olny the two terminals networks are discussed, the methods can be applied into the researches of K-terminal network and all terminal network.
This thesis focuses on the network reliability analysis and carries out the following researches:
(1) The reliability analysis about networks with imperfect nodes. Two enhanced OBDD (Ordered Binary Decision Diagram) method are presented, with the names of MFP (Marked Factor Partition) and SNR (Street Network Reliability). The MFP evaluates the network reliability based on the marked factor partition, and the SNR evaluates the network reliability based on character indentification. These methods can evaluate the reliabilities of some large networks with imperfect nodes. Otherwise, with consideration of many imperfect nodes, the EF (Enhanced Factoring) is presented to evaluate the reliability of WSN (wireless sensor networks).
1) MFP enhances the reliability analysis method based on OBDD. During the OBDD construction, the ER (Edge Replacement) operations are executed to represent the imperfect nodes with OBDD nodes. Two points improve the computation efficiency for the large scale networks: during the decomposition, the marked factor partitions are used to identify the ismophic sub-networks and the repeated computations are decreased from these networks; the states of nodes and edges are stored in OBDD and the redundant states are decreased.
2) The computation course of SNR is similar with MFP, but is is especial for the street networks. The difference is the identification of ismophic sub-networks, SNR makes use of the source node position to characterize each sub-networks and indentify those sub-networks with same structures.
3) EF enhances the factoring and improves efficiency with hash table, which stores the reliabilities of ismophic sub-networks.
(2) The reliability analysis about networks with CCF (Common Cause Failure). Based on the model of common cause failure, the CNR (Common-cause-failure Network Reliability) and WR (Wireless-sensor-networks Reliability) are presented. The CNR is for a general networks with CCF, and it can analyze the large networks with many common cause events (CCE).
1) The CNR is an enhanced OBDD algorithm, and the nodes are assumed to be perfect for simplification. During the reliability computations, only one recursive OBDD construction is executed. When the network scale and CCE number are large, this kind of OBDD construction greately decreases the running time. About the OBDD constructions for CCF networks, two methods are proposed: the first one is based on Boolean operation, and it is more flexible; the second one is based on common cause variable sets, and it is more storage efficient.
2) With the considerations of large number of CCEs and imperfect nodes, WR can evaluate the reliability of WSN. The basic principle of WR is samiliar with CNR, but it effectively resolves the imperfect nodes problem with node expansion.
(3) The importance analysis of network components. Some importance metrics are dicussed: Birnbaum measurement, Critical importance, Risk increment interval, etc. Two importance ananlysis methods for general networks are presented: BIL (Birnbaum Importance of Link) and RI (Risk Increment). The BIO computes the Birnbaum importance of links, and indicates the weakness and the fault component with the most probability. As an evaluation method for the risk of component failure, the RIO can also analyze the network stability, which can be used as invulnerability metric. Otherwise, based on the risk increment, the WNI (Wireless Node Importance) is presented to evaluate the node importance of WSN.
(4) The reliability analysis based on network measurement. The reliability metrics are presented for fault-tolerant MPLS network and resilient IP network. The one for MPLS network is based on the number of failure, and the one for IP network is based on available bandwidth. For the runtime reliability analysis of MPLS network, an adaptive loopback mechanism for LSP failure detection is presented. It is a fast and low costs method for MPLS failure detection.
第一,网络结点不可靠时的可靠性分析。传统的网络可靠性分析通常假设结点可靠,这在一些新的网络研究领域是不成立的,譬如自组织网络、灾难环境网络。而且,当网络结点众多时,可靠性分析中的计算量将变得非常庞大。
第二,网络部件故障非统计独立时的可靠性分析。传统的网络可靠性分析通常假设部件故障统计独立,这会给某些网络的可靠度评估带来很大的偏差,譬如极端环境下的网络。另外,当非统计独立故障较多且网络规模较大时,可靠性分析将变得非常困难。
第三,网络部件重要性分析。传统的网络可靠性分析主要讨论可靠度计算,不能从部件故障角度全面了解网络可靠性,缺乏高效的计算方法。
第四,网络运行时可靠性分析。传统的网络可靠性分析主要研究可靠性设计指标,其成果不能很好地反映运行时的网络可靠性。
通信网有着庞大的规模和动态复杂性,经典的故障分布函数很难描述网络的故障特性。为降低复杂性,解决一般性问题,本文采用了主流的网络可靠性模型——时间独立的离散概率模型。另外,本文只讨论了二端网络的可靠性问题,但其成果可以推广到K端网络和全端网络。
针对网络可靠性分析中存在的上述问题,本文从四个方面开展了研究工作:
(1)研究了结点不可靠网络的可靠性分析,提出了基于标记因子划分的网络可靠度评估算法——MFP(Marked Factor Partition)和基于特征识别的street网络可靠度评估算法——SNR(Street Network Reliability),解决了部分规模较大而结点不可靠的网络可靠度评估问题。最后,针对无线传感网的众多不可靠结点,提出了能有效评估可靠度的算法——EF(EnhancedFactoring)。
1)MFP改进了基于有序二叉判定图(OBDD,Ordered Binary DecisionDiagram)的可靠性分析方法。在创建OBDD过程中,利用边替代(ER,EdgeReplacement)运算处理网络中的不可靠结点。同时,从两个方面减少了计算量:分解网络时,根据子网的标记因子划分来识别同构子网,避免分解同构子网带来的重复计算;利用OBDD来存储网络结点和边的状态,减少了冗余状态。
2)SNR是专门针对street网络的方法,其基本原理与MFP相同。SNR利用源端位置信息作为子网特征,识别street网络分解过程中出现的同构子网,从而减少重复计算。
3)增强因子分解算法在网络分解过程中采用hash表来存储同构子网的可靠度,提高了计算效率。
(2)研究了一类常见的非统计独立故障——共因失效,提出了分析共因失效网络可靠性的算法——CNR(Common-cause-failure NetworkReliability)和无线传感网可靠性的算法——WR(Wireless-sensor-networksReliability)。解决了部分共因事件较多且规模较大网络的可靠度评估问题,讨论了有共因失效的无线传感网可靠度评估问题。
1)CNR改进了基于OBDD的共因失效可靠性分析方法。为降低分析复杂性,CNR算法未考虑结点不可靠的情况。由于CNR方法只需要递归创建一个OBDD结构,在共因事件较多、网络规模较大时,这种方法可节省大量计算开销。关于如何创建共因失效网络的OBDD,本文又提出了两种方法:基于布尔运算的方法和基于共因变量集的方法。第一种方式比较灵活,第二种方式有较高的存储效率。
2)WR算法充分考虑了无线传感网中的大量共因事件和不可靠结点,借用了CNR创建共因失效网络OBDD的思路,利用结点扩张法来处理无线传感网中的不可靠结点。
(3)研究了网络部件重要性分析。讨论利用Birnbaum测度、关键重要度和风险增长区间等部件重要性指标来描述网络可靠性,提出了基于Birnbaum测度的链路重要性评估算法——BIL(Birnbaum Importance ofLink)和基于风险增长区间的网络可靠性评估算法——RI(Risk Increment)。BIL充分利用OBDD结构来高效计算Birnbaum测度、关键重要度和风险增长,发现网络薄弱环节,确定引发故障的最可能部件。而RI能够对部件故障时的网络稳定度进行分析,为网路抗毁性提供了又一个参考指标。另外,针对无线传感网中不可靠结点,提出了基于风险增长的结点重要性评估算法——WNI(Wireless Node Importance)。
(4)针对网络运行时可靠性分析,提出了两个可靠性测度:基于故障检测的指标——容错MPLS网络的可靠性测度;基于可用带宽测量的指标——自愈IP网络可靠性测度。最后,为分析容错MPLS网络运行时可靠性,设计并实现了一个开销较低速度较快的故障检测方法——自适应的LSP故障检测机制。
As the economy society depends on communication networks more and more, the network failures will bring about a great data loss or even disaster. With the network reliability analysis, the engineers can enhance the network reliability and reduce the loss when the failures take place. Although the network reliability analysis has been discussed widely, some works need to be furthered:
Firstly, the reliability analysis of network with imperfect nodes. The traditional assumption of perfect nodes is inappropriate for new fields such as ad hoc networks and some networks in disaster environment. Furthermore, the number of reliability computations will largely expand when there are many imperfect nodes.
Secondly, the reliability analysis of network with dependent componets failures. Because the componets failures are usually assumed to be s-independent (statisticly-independent), the traditianl methods overestimate the network reliability. Furthermore, this assumption is inappropriate for networks in extreme environment. When the network scale and number of dependent failures are large, the network reliability analysis will become difficult.
Thirdly, the importance analysis of network components. The tranditional methods mainly focus on the reliability values, and little reliability information can be known from the point of components failures. There needs some effective methods for the importance analysis of network components.
Fourthly, the runtime reliability analysis about networks. The tranditional methods mainly discuss the reliability metrics for design, which can not indicate the reliability when the networks are operating.
Because of the large scale and dynamic complexity, it is difficult to describe the network failures with those classic failure distribution functions. To simplify the complexity and resolve the general problem, this thesis makes use of current prevalent method--discrete probability models with time independence. Although olny the two terminals networks are discussed, the methods can be applied into the researches of K-terminal network and all terminal network.
This thesis focuses on the network reliability analysis and carries out the following researches:
(1) The reliability analysis about networks with imperfect nodes. Two enhanced OBDD (Ordered Binary Decision Diagram) method are presented, with the names of MFP (Marked Factor Partition) and SNR (Street Network Reliability). The MFP evaluates the network reliability based on the marked factor partition, and the SNR evaluates the network reliability based on character indentification. These methods can evaluate the reliabilities of some large networks with imperfect nodes. Otherwise, with consideration of many imperfect nodes, the EF (Enhanced Factoring) is presented to evaluate the reliability of WSN (wireless sensor networks).
1) MFP enhances the reliability analysis method based on OBDD. During the OBDD construction, the ER (Edge Replacement) operations are executed to represent the imperfect nodes with OBDD nodes. Two points improve the computation efficiency for the large scale networks: during the decomposition, the marked factor partitions are used to identify the ismophic sub-networks and the repeated computations are decreased from these networks; the states of nodes and edges are stored in OBDD and the redundant states are decreased.
2) The computation course of SNR is similar with MFP, but is is especial for the street networks. The difference is the identification of ismophic sub-networks, SNR makes use of the source node position to characterize each sub-networks and indentify those sub-networks with same structures.
3) EF enhances the factoring and improves efficiency with hash table, which stores the reliabilities of ismophic sub-networks.
(2) The reliability analysis about networks with CCF (Common Cause Failure). Based on the model of common cause failure, the CNR (Common-cause-failure Network Reliability) and WR (Wireless-sensor-networks Reliability) are presented. The CNR is for a general networks with CCF, and it can analyze the large networks with many common cause events (CCE).
1) The CNR is an enhanced OBDD algorithm, and the nodes are assumed to be perfect for simplification. During the reliability computations, only one recursive OBDD construction is executed. When the network scale and CCE number are large, this kind of OBDD construction greately decreases the running time. About the OBDD constructions for CCF networks, two methods are proposed: the first one is based on Boolean operation, and it is more flexible; the second one is based on common cause variable sets, and it is more storage efficient.
2) With the considerations of large number of CCEs and imperfect nodes, WR can evaluate the reliability of WSN. The basic principle of WR is samiliar with CNR, but it effectively resolves the imperfect nodes problem with node expansion.
(3) The importance analysis of network components. Some importance metrics are dicussed: Birnbaum measurement, Critical importance, Risk increment interval, etc. Two importance ananlysis methods for general networks are presented: BIL (Birnbaum Importance of Link) and RI (Risk Increment). The BIO computes the Birnbaum importance of links, and indicates the weakness and the fault component with the most probability. As an evaluation method for the risk of component failure, the RIO can also analyze the network stability, which can be used as invulnerability metric. Otherwise, based on the risk increment, the WNI (Wireless Node Importance) is presented to evaluate the node importance of WSN.
(4) The reliability analysis based on network measurement. The reliability metrics are presented for fault-tolerant MPLS network and resilient IP network. The one for MPLS network is based on the number of failure, and the one for IP network is based on available bandwidth. For the runtime reliability analysis of MPLS network, an adaptive loopback mechanism for LSP failure detection is presented. It is a fast and low costs method for MPLS failure detection.
引文
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