Abstract
Continuity is a critical aspect of video-streaming services due to large user abandonment costs resulting from interruptions in the middle of video playback. Server performance problems in particular are in the origin of soft failures manifested as degradation of video quality, which can have the same impact on viewer experience as hard failures (e.g., abrupt connection termination). Predicting both failure types is essential to avoid their propagation to end-users by means of proactive repairs. This paper addresses the problem of predicting and diagnosing performance problems in streaming servers in terms of its main cause (client-workload overloading and performance anomaly) for proactive repair, server capacity planning and fault repair. Evaluation of the approach using several machine learning algorithms showed that (1) Bayesian Networks predicted 93% of failures, (2) prediction performance is worse for performance anomalies than for client-workload overloading, (3) diagnosis correctness for predicted failures is 100% for client-workload overloading failures and approximately 98% for performance anomalies.
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