TOPOLOGY-BASED FEATURE SELECTION FOR ANOMALY DETECTION

摘要

Instead of attempting to scan all metric measurements of a distributed application, an anomaly detector intelligently selects instances of metrics from the universe of metric instances available for the distributed application to detect anomalies. Intelligent feature selection allows the anomaly detector to efficiently and reliably detect anomalies for a distributed application. The intelligent selection is guided by execution paths of transactions of the distributed application, and the execution paths are determined from a topology of the distributed application. The anomaly detector scans the incoming time-series data of the selected metric instances by transaction type and determines whether the scanned measurements across the selected metric instances form a pattern correlated with anomalous behavior.