A resource allocating neural network based approach for detecting end-to-end network performance anomaly

Authors:
Wenwei Li;Dafang Zhang;Jinmin Yang;Gaogang Xie;Lei Wang
Affiliations:
College of Computer and Communication, Hunan University, Changsha, China;School of Software, Hunan University, Changsha, China;School of Software, Hunan University, Changsha, China;Institute of Computing Technology, Chinese Academy of Science, Beijing, China;School of Software, Hunan University, Changsha, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

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Abstract

Automatic detection of end-to-end network performance anomalies is important to efficient network management and optimization. We present an end-to-end network performance anomalies detection method, based on characterizing of the dynamic statistical properties of RTT normality. The experiment on real Internet end-to-end path RTT data shows that, the proposed method is accurate in detecting performance anomalies, it can successfully detect about 96.25% anomalies in the experiment.