基于离群点检测的链路流量细粒度监测

Subtle Aberration Monitoring of Link Traffic Based on Outlier Detection

【目的】科研网络链路局部流量尖峰和细粒度传输异常对精密联合科学观测的影响较大,科研网络当前采用的固定阈值链路流量监测方法仅能有效监测链路资源消耗,无法实时监测链路汇聚流量中的细粒度异常。【方法】因此,本文基于改进的局部离群点检测方法面向科研网络需求设计了一种新型链路流量细粒度监测预警模型,通过在滑动时间窗口内对观测流量与动态基线进行快速计算实现链路流量细粒度异常的快速监测预警。【结论】在中国科技网生产环境中的实网验证表明,预警触发点与实际生产工单数据中的异常记录点全部吻合,具备工程应用的可行性。

[Objective]The subtle aberration of network traffic has a harmful influence on scientific research precision joint observation.The fixed threshold network traffic monitoring currently used can only effectively warn the link resource consumption,but cannot monitor the subtle aberration of branch line traffic.[Methods]Therefore,we propose a new link traffic warning model which can monitor subtle aberration and trigger alarm based on the improved outlier detection method in this study to support the network management of CSTNet.The model can implement fast monitoring and early warning of subtle aberration of the link traffic by fast calculation of deviation between the observed traffic value and dynamic baseline in sliding time window.[Conclusions]Experiments in the real operation of CSTNet demonstrate that the warning trigger points are all consistent with the abnormal record points in the network operation and diagnostic record,which has the feasibility of engineering application.