公开(公告)号：US20210216559A1

公开(公告)日：2021-07-15

申请号：US16742120

申请日：2020-01-14

Applicant: VMware, Inc.

Inventor： Karen Aghajanyan ,  Hovhannes Antonyan ,  Naira Movses Grigoryan ,  Arnak Poghosyan ,  Ashot Nshan Harutyunyan ,  Sunny Dua ,  Bonnie Zhang

IPC: G06F16/2457 ,  G06F16/25 ,  G06F3/0484

Abstract: Methods and systems are directed to finding various types of evidence of performance problems with objects in a data center, troubleshooting the performance problems, and generating recommendations for correcting the performance problems. A performance problem with an object of a data center, such as a server computer, an application, or a virtual machine (“VM”), may result from performance problems associated with other objects of the data center. The methods and systems detect origins of performance problems with objects for which no alerts and parameters for detecting the performance problems have been defined or detect performance problems related to alerts that fail to point to a root cause of the performance problem.

公开(公告)号：US20220027249A1

公开(公告)日：2022-01-27

申请号：US16936565

申请日：2020-07-23

Applicant: VMware, Inc.

Inventor： Sunny Dua ,  Bonnie Zhang ,  Karen Aghajanyan ,  Hovhannes Antonyan ,  Ashot Nshan Harutyunyan ,  Arnak Poghosyan ,  Naira Movses Grigoryan

IPC: G06F11/34 ,  G06F11/30 ,  G06F11/32 ,  G06F11/07 ,  G06K9/62

Abstract: Methods and systems described herein automate various aspects of troubleshooting a problem in a distributed computing system for various forms of object information regarding objects of the distributed computing system. In one aspect, the object information includes metrics, log messages, properties, network flows, events, and application traces. Methods and systems learn interesting patterns contained in the object information. The interesting patterns include change points in metrics and network flows, changes in the types of log messages, broken correlations between events, anomalous event transactions, atypical histogram distributions of metrics, and atypical histogram distributions of span durations in application traces. The interesting patterns are displayed in a graphical user interface (“GUI”) that enables a user to assign a label identifying a problem associated with the interesting patterns.