This paper presents a distributed system for real-time anomaly detection in backbone. The backbone traffic is so huge that it is difficult to monitor abnormal traffic by traditional methods. Our system is based on Hadoop, an open source framework, and used to detect the abnormal traffic. Firstly, We establish a precise regression model to describe the network traffic. Secondly, distributed system Hadoop is used to detect the abnormal traffic. Finally, the experimental results prove that our system can detect the abnormal traffic accurately and efficiently in the real-world network environment.
Based on the analysis of current web service applications and protocol, a group of quality of service (QoS) parameters of web traffic is proposed from users' perspective. These parameters focus on delay of web service. A active monitor system is designed and used to monitor web performance on real network of the biggest ISPs in China. These delay parameters are actively measured in this specialized measurement system. Based on measured data, different aspects of user perceived web performance are analyzed in different access methods, target websites and time slots. Some practical valuable conclusions are concluded.
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