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 black-box training
methodology for performance anomaly detection. The method uses an artificial neural networks-driven
methodology and Bayesian Optimization to identify anomalous performance and are validated on Apache
Spark Streaming. TRACK-Plus has been extensively validated using a real Apache Spark Streaming system
and achieve a high F-score while simultaneously reducing training time by 80% compared to efficiently
detect anomalies.