面向业务的基于模糊关联规则挖掘的网络故障诊断
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摘要
现代社会中，网络已经成为信息交流和通信的基础载体。网络即使发生微小故障，也会给人类的社会活动、经济活动带来巨大影响。因此，智能化的网络故障诊断是下一代网络高可信度的重要保障。当故障发生时，要求网络管理员在最短时间内识别故障类型和故障级别，正确判断出故障根源所在位置，并及时对网络进行修复。相对传统网络以网络设备为管理对象而言，用户更关心的是网络业务的正常性。然而设备的正常运行并不意味着业务的正常运行，这使得业务故障管理缺乏面向用户的根本性。另一方面，网管系统经历了基础平台建设和系统整合两个阶段，基于设备进行管理的模式越来越不适应网络发展的要求，支持面向业务的网络管理是必然趋势。
本论文将网络业务作为主要被管理对象，以包含业务信息的网络告警为载体，研究了面向业务的QoS参数与面向资源的网络性能参数之间的映射。根据网元和业务的映射关系，得到设备告警与业务告警之间的对应关系。同时，将模糊理论、模糊推理与数据挖掘结合起来，对多域多层的网络故障信息进行动态模糊关联规则的挖掘。
论文综合讨论了层间模糊关联规则挖掘、分布式模糊关联规则挖掘以及多支持度动态模糊关联规则挖掘算法，全方位地进行了网络故障相关性分析。最终由面向业务的网络告警，生成模糊关联规则知识库，并建立模糊推理系统。旨在解决告警信息不确定的情况下，快速准确地进行网络故障诊断、定位与恢复，从而提高通信网络的效率和性能。本课题研究的创新性主要体现在以下几个方面：
第一，运用模糊逻辑定义的隶属度函数进行网络告警的预处理和模糊化，使之更接近于实际应用环境，有效降低多维告警的粒度和复杂度，提高了告警关联规则挖掘效率。模糊化后的网络告警，其物理意义表达了与根源告警的接近程度，从而能有效反映该告警在网络中的相对重要程度和影响范围，使在此基础上的告警相关性分析、模糊推理和网络故障诊断更加科学。
第二，针对网络层次化划分的管理特点，考虑以业务层故障作为主要被管对象，在网络告警中融入业务层、网络层直至网元层的分层信息属性，记为“告警类型”。使得当网络业务受到影响发生故障时，将网络性能故障映射到与之相关的业务应用QoS故障层面，面向终端用户，解决网络故障诊断问题，为新一代智能化网络管理奠定基础。
第三，针对多厂商设备环境以及多地域子网的管理特点，提出了分布式并行关联规则挖掘思想：区别于传统分布式挖掘算法，横向分割大型数据库，采用多处理器并行挖掘子数据库的以资源代价换取时间效率的策略；而将分布式网络中的各局域网告警数据视为不同子数据库进行同步挖掘，即设立全局站点与局部站点在时间维度上同时采集数据，全局站点负责局间关联规则挖掘，而局部站点负责局内关联规则挖掘。并由此提出多层多域模糊关联规则挖掘算法——MMDFARM，由层次和地域两个方面考虑了挖掘算法的适应性问题。
第四，通信网络的设备信息、拓扑结构乃至业务需求都是动态变化的，从而导致网络告警数据库也呈现渐进式的变化。当出现新增的告警数据时，需要对整个数据库进行重新挖掘，以找出与新告警关联的规则信息。这不仅造成大量资源的浪费，也完全忽略了历史告警的作用。根据告警数据的时间关联性，提出多支持度动态关联规则挖掘算法——IDFARM，由时间维度方面考虑了挖掘算法的适应性问题。
第五，深入研究了基于模糊关联规则的模糊推理算法，定义了推理过程中各模糊算子的数学形式，比较和验证了不同模糊组合下的推理结果，从中优选出适合通信网告警相关性分析的最佳方法，建立起特征化的网络故障诊断模型。该模型拥有良好的人机接口界面，通过模糊推理模块进行诊断，能快速准确地定位出引发当前告警的根源故障所在，从而有效恢复网络通信，提高网络的稳健性。
总之，实现面向业务的、基于模糊关联规则挖掘、快速准确的网络故障诊断和智能化的网络管理，是本课题的特色所在。
In the modern information age, even a minor fault in communication networks willbring strong affects on social and economic activities of human beings. Therefore, theintelligent network fault diagnosis is a vital guarantee of next-generation networks.Once alarms occur, it is important for the network operator to identify the alarm typesand levels a.s.a.p, and accurately locate the root alarms to recover networkcommunications promptly. Despite the fact that traditional networks take networkdevices as managed objects, users care more about the normality of network services.However, the normal operation of devices does not reflect the normal operation ofcorresponding services, which means fault managements of services lack user-orientedfeatures. On the other hand, network management experiences two stages as:fundamental platform construction and system integration. It makes device-based faultmanagement no longer suitable for the developing requirements of the communicationnetwork, and there is an inevitable trend of service-oriented network management.
This project takes network services as main managed objects, and considersnetwork alarms that contain service information as carriers to study the mappingrelationship between service-oriented QoS parameters and device-oriented networkperformance parameters to get the association of device alarms and service alarmsaccording to the above relationship. Meanwhile, fuzzy theory, fuzzy reasoning and datamining are combined together to realize dynamic fuzzy association rule mining ofmulti-layer multi-domain network fault information.
The author discusses multi-layer fuzzy association rule mining, distributed fuzzyassociation rule mining and dynamic fuzzy association rule mining, comprehensivelystudies network fault correlation analysis, at last develops fuzzy association rule basedfuzzy reasoning system. The system can deal with situations when alarm information isuncertain, quickly carry out network fault diagnosis, and accurately locate as well asrecover faults to improve the efficiency and performance of communication networks.The innovation of this study is mainly reflected in the following aspects:
(1) Apply fuzzy logic and fuzzy membership function to pretreatment and fuzzification of network alarms, which effectively reduce granularity and complexity ofmulti-dimensional network alarms and improve the mining efficiency. Also the actualmeaning of fuzzy membership degree is the relationship between current alarm and rootalarm, which reflects relative importance and influence areas of network alarms.Performing alarm correlation analysis, fuzzy reasoning and network fault diagnosisbased on fuzzy alarms will be more reasonable.
(2) According to hierarchical features of network management, integrate layerinformation when generating alarms. Once the services are affected by network faults,QoS faults of applications are mapped into associated performance faults of networks.
(3) Propose the idea of parallel mining distributed fuzzy association rules.Differing from traditional distributed mining algorithms, which horizontally partitionhuge database and use multiple processors for parallel mining rules in sub-database, thisis a trade off between resource and efficiency. This study vertically partition database,and collect alarm information according to time dimension in both global site andlocal site, where the former is in charge of mining inter-network association rules whilethe latter is in charge of mining intra-network association rules. Then develop multi-level multi-dimensional distributed fuzzy association rule mining algorithm–MMDFARM, which considers different sides of algorithm adaptability for networkmanagement in complex environment.
(4) In practical environments, the device information, the topology of network andthe service requirements change dynamically, which result in changes of the alarmdatabase. When there is a new alarm, one of the solutions is to remine the wholedatabase to find association rules with the new alarm information. This not only causesa lot of waste of resources, but also completely ignores the knowledge of historicalalarms. The author proposes incremental dynamic fuzzy association rules miningalgorithm–IDFARM according to the time correlation of network alarms, whichconsiders time dimension of algorithm adaptability for the updating communicationnetwork.
(5) Deeply study fuzzy association rules based fuzzy reasoning strategy and definemathematic models of fuzzy matching operators, fuzzy implication operators as well asfuzzy composition operators in the process of inference. Experiments are carried out tovalidate the accuracy and efficiency of different combinations of fuzzy operators, and the best one of our synthetic communication network is selected to develop acharacteristic system for network fault diagnosis, which build optimal inference engineby verifying and evaluating various fuzzy reasoning methods and designingfine human-machine interface of network fault management system. Applying fuzzyreasoning to realize automatic and intelligent network management can accuratelydiagnose and well position root alarms, resulting in shortening the recovery time andimproving the performance of communication networks.
All in all, realizing quickly and accurately network fault diagnosis based on fuzzyassociation rules of service-oriented alarms and improving intelligent network faultmanagement, are the highlights of this project. In view of the encouraging results, it isthe authors' belief that a fuzzy reasoning will be a valuable tool to assist network faulttroubleshooters in handling the task of diagnosing failure components and for furtherpractical implementation.
本论文将网络业务作为主要被管理对象，以包含业务信息的网络告警为载体，研究了面向业务的QoS参数与面向资源的网络性能参数之间的映射。根据网元和业务的映射关系，得到设备告警与业务告警之间的对应关系。同时，将模糊理论、模糊推理与数据挖掘结合起来，对多域多层的网络故障信息进行动态模糊关联规则的挖掘。
论文综合讨论了层间模糊关联规则挖掘、分布式模糊关联规则挖掘以及多支持度动态模糊关联规则挖掘算法，全方位地进行了网络故障相关性分析。最终由面向业务的网络告警，生成模糊关联规则知识库，并建立模糊推理系统。旨在解决告警信息不确定的情况下，快速准确地进行网络故障诊断、定位与恢复，从而提高通信网络的效率和性能。本课题研究的创新性主要体现在以下几个方面：
第一，运用模糊逻辑定义的隶属度函数进行网络告警的预处理和模糊化，使之更接近于实际应用环境，有效降低多维告警的粒度和复杂度，提高了告警关联规则挖掘效率。模糊化后的网络告警，其物理意义表达了与根源告警的接近程度，从而能有效反映该告警在网络中的相对重要程度和影响范围，使在此基础上的告警相关性分析、模糊推理和网络故障诊断更加科学。
第二，针对网络层次化划分的管理特点，考虑以业务层故障作为主要被管对象，在网络告警中融入业务层、网络层直至网元层的分层信息属性，记为“告警类型”。使得当网络业务受到影响发生故障时，将网络性能故障映射到与之相关的业务应用QoS故障层面，面向终端用户，解决网络故障诊断问题，为新一代智能化网络管理奠定基础。
第三，针对多厂商设备环境以及多地域子网的管理特点，提出了分布式并行关联规则挖掘思想：区别于传统分布式挖掘算法，横向分割大型数据库，采用多处理器并行挖掘子数据库的以资源代价换取时间效率的策略；而将分布式网络中的各局域网告警数据视为不同子数据库进行同步挖掘，即设立全局站点与局部站点在时间维度上同时采集数据，全局站点负责局间关联规则挖掘，而局部站点负责局内关联规则挖掘。并由此提出多层多域模糊关联规则挖掘算法——MMDFARM，由层次和地域两个方面考虑了挖掘算法的适应性问题。
第四，通信网络的设备信息、拓扑结构乃至业务需求都是动态变化的，从而导致网络告警数据库也呈现渐进式的变化。当出现新增的告警数据时，需要对整个数据库进行重新挖掘，以找出与新告警关联的规则信息。这不仅造成大量资源的浪费，也完全忽略了历史告警的作用。根据告警数据的时间关联性，提出多支持度动态关联规则挖掘算法——IDFARM，由时间维度方面考虑了挖掘算法的适应性问题。
第五，深入研究了基于模糊关联规则的模糊推理算法，定义了推理过程中各模糊算子的数学形式，比较和验证了不同模糊组合下的推理结果，从中优选出适合通信网告警相关性分析的最佳方法，建立起特征化的网络故障诊断模型。该模型拥有良好的人机接口界面，通过模糊推理模块进行诊断，能快速准确地定位出引发当前告警的根源故障所在，从而有效恢复网络通信，提高网络的稳健性。
总之，实现面向业务的、基于模糊关联规则挖掘、快速准确的网络故障诊断和智能化的网络管理，是本课题的特色所在。
In the modern information age, even a minor fault in communication networks willbring strong affects on social and economic activities of human beings. Therefore, theintelligent network fault diagnosis is a vital guarantee of next-generation networks.Once alarms occur, it is important for the network operator to identify the alarm typesand levels a.s.a.p, and accurately locate the root alarms to recover networkcommunications promptly. Despite the fact that traditional networks take networkdevices as managed objects, users care more about the normality of network services.However, the normal operation of devices does not reflect the normal operation ofcorresponding services, which means fault managements of services lack user-orientedfeatures. On the other hand, network management experiences two stages as:fundamental platform construction and system integration. It makes device-based faultmanagement no longer suitable for the developing requirements of the communicationnetwork, and there is an inevitable trend of service-oriented network management.
This project takes network services as main managed objects, and considersnetwork alarms that contain service information as carriers to study the mappingrelationship between service-oriented QoS parameters and device-oriented networkperformance parameters to get the association of device alarms and service alarmsaccording to the above relationship. Meanwhile, fuzzy theory, fuzzy reasoning and datamining are combined together to realize dynamic fuzzy association rule mining ofmulti-layer multi-domain network fault information.
The author discusses multi-layer fuzzy association rule mining, distributed fuzzyassociation rule mining and dynamic fuzzy association rule mining, comprehensivelystudies network fault correlation analysis, at last develops fuzzy association rule basedfuzzy reasoning system. The system can deal with situations when alarm information isuncertain, quickly carry out network fault diagnosis, and accurately locate as well asrecover faults to improve the efficiency and performance of communication networks.The innovation of this study is mainly reflected in the following aspects:
(1) Apply fuzzy logic and fuzzy membership function to pretreatment and fuzzification of network alarms, which effectively reduce granularity and complexity ofmulti-dimensional network alarms and improve the mining efficiency. Also the actualmeaning of fuzzy membership degree is the relationship between current alarm and rootalarm, which reflects relative importance and influence areas of network alarms.Performing alarm correlation analysis, fuzzy reasoning and network fault diagnosisbased on fuzzy alarms will be more reasonable.
(2) According to hierarchical features of network management, integrate layerinformation when generating alarms. Once the services are affected by network faults,QoS faults of applications are mapped into associated performance faults of networks.
(3) Propose the idea of parallel mining distributed fuzzy association rules.Differing from traditional distributed mining algorithms, which horizontally partitionhuge database and use multiple processors for parallel mining rules in sub-database, thisis a trade off between resource and efficiency. This study vertically partition database,and collect alarm information according to time dimension in both global site andlocal site, where the former is in charge of mining inter-network association rules whilethe latter is in charge of mining intra-network association rules. Then develop multi-level multi-dimensional distributed fuzzy association rule mining algorithm–MMDFARM, which considers different sides of algorithm adaptability for networkmanagement in complex environment.
(4) In practical environments, the device information, the topology of network andthe service requirements change dynamically, which result in changes of the alarmdatabase. When there is a new alarm, one of the solutions is to remine the wholedatabase to find association rules with the new alarm information. This not only causesa lot of waste of resources, but also completely ignores the knowledge of historicalalarms. The author proposes incremental dynamic fuzzy association rules miningalgorithm–IDFARM according to the time correlation of network alarms, whichconsiders time dimension of algorithm adaptability for the updating communicationnetwork.
(5) Deeply study fuzzy association rules based fuzzy reasoning strategy and definemathematic models of fuzzy matching operators, fuzzy implication operators as well asfuzzy composition operators in the process of inference. Experiments are carried out tovalidate the accuracy and efficiency of different combinations of fuzzy operators, and the best one of our synthetic communication network is selected to develop acharacteristic system for network fault diagnosis, which build optimal inference engineby verifying and evaluating various fuzzy reasoning methods and designingfine human-machine interface of network fault management system. Applying fuzzyreasoning to realize automatic and intelligent network management can accuratelydiagnose and well position root alarms, resulting in shortening the recovery time andimproving the performance of communication networks.
All in all, realizing quickly and accurately network fault diagnosis based on fuzzyassociation rules of service-oriented alarms and improving intelligent network faultmanagement, are the highlights of this project. In view of the encouraging results, it isthe authors' belief that a fuzzy reasoning will be a valuable tool to assist network faulttroubleshooters in handling the task of diagnosing failure components and for furtherpractical implementation.
引文
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