Amazon Technologies, Inc.

Devesh Ratho

Syed Furqhan Ulla

Rajendra Kumar Vippagunta

David Kernert

Lenon Alexander Minorics

Patrick Bloebaum

Ankita Verma

Shashwat Srijan

Sunayana Vempati

Yekesa Srinivasa Kosuru

Namita Das

Jill Blue Lin

Mohammed Talal Yassar Azam

Ketan Vijayvargiya

Ahmed Gamal Hamed

Mohammad Adnan

Mohammed Saad Ather

Fnu Syed Furqhan Ulla

Ankur Mehrotra

Aditya Bahuguna

Sadanand Murthy Sachidananda

Gaurav Rajendra Kataria

Alfred Bruno Herbst

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

Execution status of managed time series processing tasks may be tracked. Status of a time series processing task that operations on different portions of a time series may be respectively captured. A request for the status of one of the portions of the time series with respect to the time series processing task may be received. The status may be identified and returned. For failed tasks, a failure reason may be generated by the time series processing system and included in a response with a failure status.

Tracking status of managed time series processing tasks

A service to provide anomaly detection receives a request to back-test the service. The request includes information for accessing a dataset of historical data. The service executes workflows to ingest the data, train a plurality of machine learning models to perform anomaly detection, and detect anomalies in the dataset. A representation of the detect anomalies is generated and presented to a user. The service receives an indication to activate the service to provide ongoing anomaly detection services.

Anomaly back-testing

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Seattle, WA, United States of America

Devesh Ratho