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TRACK-Plus: Optimizing Artificial Neural Networks for Hybrid Anomaly Detection in Data Streaming Systems
2020
IEEE Access
Software applications can feature intrinsic variability in their execution time due to interference from other applications or software contention from other users, which may lead to unexpectedly long running times and anomalous performance. There is thus a need for effective automated performance anomaly detection methods that can be used within production environments to avoid any late detection of unexpected degradations of service level. To address this challenge, we introduce TRACK-Plus a
doi:10.1109/access.2020.3015346
fatcat:eozee43rdbdd3fokerxz7abw6i