基于动态模糊关联规则推理的光网络故障管理(英文)

当代光网络趋于复杂化,一个故障将引发多个告警事件。同时,同一告警可能由不同的故障导致。本文研究了基于数据挖掘的光网络告警相关性分析,我们从动态网络资源与设备中挖掘关联规则,充分利用和维护原有规则知识,使网络结构和规则库都能快速更新,并提出了新型的动态模糊关联规则挖掘算法IDFARM。同时运用模糊逻辑将数值型告警属性转化为逻辑语言变量,当网络中有新的未知告警发生时,我们对模糊关联规则运用模糊推理来进行故障诊断,这将缩短网络恢复时间,有利于提高光网络故障管理性能。仿真实验验证了文章算法的正确性和有效性。

Optical Network Fault Management Based on Dynamic Fuzzy Association Rule Reasoning

Nowadays, optical network turns to be more complex. Once there occurs a failure, there will result in multi-alarm-events. On the other hand, an alarm might result from different faults. This paper describes the alarm correlation in optical networks based on data mining. We mine association rules for dynamic network resource and service by fully utilizing knowledge base formed before, which enables the framework can be easily updated and new discovery methods can be readily incorporated within it. Fuzzy logic is also used to convert numerical alarm attributes into linguistic terms. Once there are new unknown alarms occur in network, we use fuzzy reasoning based on fuzzy association rules (FARs) for fault diagnosis. It will shorten the recovery time and improve the performance of optical network fault management. Experiments are carried out to validate the accuracy &efficiency of our research.