Amazon Technologies, Inc.

Patrick Bloebaum

David Kernert

Lenon Alexander Minorics

Rajendra Kumar Vippagunta

Devesh Ratho

Syed Furqhan Ulla

Sunayana Vempati

Namita Das

Yekesa Srinivasa Kosuru

Shashwat Srijan

Jill Blue Lin

Mohammed Talal Yassar Azam

Ketan Vijayvargiya

Ankur Mehrotra

Sadanand Murthy Sachidananda

Ali Caner Turkmen

Laurent Callot

Mohammed Saad Ather

Peter Goetz

Syed Ahsan Ishtiaque

Shashank Srivastava

Dominik Janzing

A system generates a recommendation that includes at least one action to address at least one root cause anomaly that causes other anomalies occurred within a distributed system. The at least one root cause anomaly is determined by at least using a graph that represents the distributed system and metrics that are associated with the distributed system.

Addressing root cause anomaly

A system obtains a graph representing a set of resources of a distributed system. At least one node of the graph represents a resource-metric pair. The system further obtains time series data that indicates anomalies from the system. Then, the system determines a root cause anomaly that caused other anomalies based at least in part on the graph and the time series data.

Systems and methods to analyze root cause anomaly

Techniques for anomaly and causality detection are described. An example includes receiving time series data; performing anomaly detection on the received time series data to detect at least one anomaly using an anomaly detection model; detecting a causal relationship between measures, wherein a set of measures are related when a first of the set of measures has a causal influence on a second of the set of measures, wherein a single time series is a metric and a measure is a numerical or categorical quantity a metric describes; and outputting a result of the anomaly and causality relationship detections.

Anomaly and causality detection

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Mountain View, CA, United States of America

Patrick Bloebaum