Amazon Technologies, Inc.

Laurent Callot

Alexander Zimin

Nam Khanh Tran

Paul M Vazquez

Jasmeet Chhabra

Parminder Bhatia

Tim Januschowski

Qijia Chen

Lifan Chen

Andrey Kan

Luyang Kong

Joel Dan Castellon Arevalo

Mostafa Rahmani

Ming Chen

Baris Kurt

Meredith Paige Kiessling

Pramuditha Perera

A determination is made that anomaly analysis is to be performed with respect to an application. An anomaly score of the application is generated with respect to observed values of a plurality of metrics of the application. Generation of the anomaly score comprises computing an anomaly score contribution associated with an analysis of a correlation between values of a pair of metrics of the application. In response to a detection that the anomaly score exceeds a threshold, an anomaly response operation is initiated.

Multi-factor anomaly detection for application execution environments

Techniques for performing anomaly detection are described. An exemplary method includes receiving a request to detect potential anomalies using an anomaly detection system having at least one anomaly scoring model; processing the received data using the anomaly detection system to score the data to determine when the data is potentially anomalous based on one or more thresholds; requesting feedback of at least one determined potential anomaly; receiving feedback on the least one determined potential anomaly; and adjusting at least one of one or more of thresholds used to determine potential anomalies and what is considered an anomaly without adjusting the at least one anomaly scoring model.

Anomaly detection using feedback

Methods, systems, and computer-readable media for automatically detecting root causes of anomalies occurring in information technology (IT) systems are disclosed. In some embodiments, data of a service graph depicting dependencies between nodes or services of the IT infrastructure is traversed to determine propagation patterns of anomaly symptoms/alarms through the IT infrastructure. Also, a causal inference model is used to determine probabilities that an observed propagation pattern corresponds to a stored propagation pattern, wherein a close correspondence indicates that the current anomaly is likely caused by a similar root cause as a past anomaly that caused the stored propagation pattern.

Root cause detection and corrective action diagnosis system

Growing community of inventors

Berlin, Germany

Laurent Callot