Application of a Self-Healing Video-Streaming Architecture to RTSP Servers



Streaming media is now one of the killer applications on the Internet. Availability in streaming services is a critical concern, as consumer expectations are drawn around decades of traditional TV experience. Server performance has particular importance in streaming, as its sensitiveness to delays makes it vulnerable to performance anomalies. Current work on server- level performance analysis fails to cope with performance failures not explained by the workload. We propose a self-healing architecture for streaming servers sustained by a biological metaphor of heart that explores proactive server recovery by anticipating performance failures through detection of arrhythmias (transmission delays of streaming content) and session probing. We evaluated the approach in RTSP streaming through experimental work in several resource exhaustion scenarios. Results have shown that our approach is able to predict and localize service failures several seconds before their occurrence for most failure scenarios.


Failure Prediction, Failure Diagnosis, Video-Streaming


Failure Prediction and Diagnosis in Streaming Servers


IEEE International Symposium on Network Computing and Applications (NCA), August 2011

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