公开(公告)号：US11929869B2

公开(公告)日：2024-03-12

申请号：US16410867

申请日：2019-05-13

Applicant: NETFLIX, INC.

Inventor： Arthur Gonigberg ,  Michael Lloyd Cohen ,  Neeraj Joshi ,  Cody Mitchell Rioux

IPC: H04L41/0681 ,  H04L41/0604 ,  H04L41/0631 ,  H04L43/04 ,  H04L43/0817 ,  H04L43/0823 ,  H04L43/16

CPC classification number: H04L41/0681 ,  H04L41/0609 ,  H04L41/0631 ,  H04L43/04 ,  H04L43/0817 ,  H04L43/0823 ,  H04L43/16

Abstract: 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.

公开(公告)号：US10552745B2

公开(公告)日：2020-02-04

申请号：US14057898

申请日：2013-10-18

Applicant: Netflix, Inc.

Inventor： Daniel Isaac Jacobson ,  Neeraj Joshi ,  Puneet Oberai ,  Yong Yuan ,  Philip Simon Tuffs

IPC: H04L12/26 ,  G06N5/04 ,  H04L12/911

Abstract: 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.

公开(公告)号：US20150113120A1

公开(公告)日：2015-04-23

申请号：US14057898

申请日：2013-10-18

Applicant: Netflix, Inc.

Inventor： Daniel Isaac Jacobson ,  Neeraj Joshi ,  Puneet Oberai ,  Yong Yuan ,  Philip Simon Tuffs

IPC: G06N5/04 ,  H04L12/911

Abstract: 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.

Abstract translation: 描述了用于预测性地缩放分布式应用的技术。 实施例可以在第一时间窗口内监视云计算环境内的应用程序的性能，以收集历史性能数据。 这里，应用程序包括多个应用程序实例。 可以在第二个时间窗口监视应用程序的工作负载，以收集历史工作负载数据。 实施例可以分析历史性能数据和历史工作负载数据，以确定应用的一个或多个缩放模式。 在确定应用程序的当前状态与一个或多个缩放模式中的一个匹配时，可以确定用于预测缩放应用程序的计划。 然后，实施例可以基于所确定的计划来预测性地缩放多个应用程序实例。