Netflix Inc.

Neeraj Joshi

Philip Simon Tuffs

Michael Lloyd Cohen

Daniel Isaac Jacobson

Yong Yuan

Cody Mitchell Rioux

Puneet Oberai

Arthur Gonigberg

Various embodiments of the disclosure disclosed herein provide techniques for detecting anomalies across one or more components within a distributed computing system, according to various embodiments of the present disclosure. An anomaly detection system retrieves event data associated with a real-time stream of events generated by one or more components within a distributed computing system. The anomaly detection system computes a failure metric based on the event data. The anomaly detection system determines that the failure metric exceeds a dynamically adjustable trigger condition. The anomaly detection system generates an alert associated with the failure metric.

Scalable and real-time anomaly detection

Techniques for predictively scaling a distributed application are described. Embodiments could monitor performance of an application within a cloud computing environment over a first window of time to collect historical performance data. Here, the application comprises a plurality of application instances. A workload of the application could be monitored over a second window of time to collect historical workload data. Embodiments could analyze both the historical performance data and the historical workload data to determine one or more scaling patterns for the application. Upon determining a present state of the application matches one of the one or more scaling patterns, a plan for predictively scaling the application could be determined. Embodiments could then predictively scale the plurality of application instances, based on the determined plan.

Predictive auto scaling engine

Growing community of inventors

San Jose, CA, United States of America

Neeraj Joshi