Towards real-time anomalies monitoring for QoE indicators

Anomaly detection and characterization is a main topic for network managers. Although quality-of-service (QoS) indicators can help to infer problem occurrence, they do not provide immediate insight on the user’s perceived quality. Evolved service-level agreements (SLA) will likely be established in terms of quality of experience (QoE) indicators. QoS metrics composing the QoE indicators need to be monitored on a real-time basis by the SLA management tools in order to detect anomalies driving to contract violations. Monitoring SLA contracts may involve the surveillance of individual application sessions for several users. In this work, we address the problem of anomaly detection with impact on a relatively large number of users, either on one or on several types of applications simultaneously. We propose a method to characterize the state of the network, representing QoE indicators as time series and reducing the dimension of the data set. The singular spectrum analysis (SSA) method, using a combination of geometric and statistical methods, is proposed as an analysis tool in order to detect anomalies on QoE indicator evolution.