公开(公告)号：US20170371878A1

公开(公告)日：2017-12-28

申请号：US15190678

申请日：2016-06-23

Applicant: VMware, Inc.

Inventor： Naira Movses Grigoryan ,  Vahan Tadevosyan ,  Nina Karapetyan ,  Ashot Nshan Harutyunyan ,  Arnak Poghosyan

IPC: G06F17/30

CPC classification number: G06F9/5072

Abstract: Methods and systems to evaluate data center performance and prioritize data center objects and anomalies for remedial actions are described. Methods rank data center objects and determine object performance trends. Methods calculate an object rank of each object of the data center over a period of time and calculate an object trend of each object of the data center based on relative frequencies of alerts at different times. The objects may be prioritized for remedial actions based on the object ranks and object trends.

公开(公告)号：US20240419530A1

公开(公告)日：2024-12-19

申请号：US18336799

申请日：2023-06-16

Applicant: VMware, Inc.

Inventor： Ashot Nshan Harutyunyan ,  Arnak Poghosyan ,  Artur Grigoryan ,  Vahan Tadevosyan ,  Vahe Mikayelyan

IPC: G06F11/07

Abstract: Automated computer-implemented methods and systems for discovering incidents occurring with objects running in a data center and executing remedial measures that correct the incidents are described herein. The methods and systems discover clusters of alerts in a stream of alerts triggered by a stream of events occurring with objects in the data center. User feedback is used to identify alerts with related event types in each cluster of alerts that corresponds to separate incidents occurring in the data center. The methods and system compare a set of runtime alerts to each incident to determine one or more similar incidents to the set of runtime alerts. The one or more similar incidents and corresponding remedial measures are displayed in a GUI with each remedial measure selectable to launch an operation that corrects one of the problems represented by the one or more similar incidents.

公开(公告)号：US10394612B2

公开(公告)日：2019-08-27

申请号：US15190678

申请日：2016-06-23

Applicant: VMware, Inc.

Inventor： Naira Movses Grigoryan ,  Vahan Tadevosyan ,  Nina Karapetyan ,  Ashot Nshan Harutyunyan ,  Arnak Poghosyan

IPC: G06F9/50

Abstract: Methods and systems to evaluate data center performance and prioritize data center objects and anomalies for remedial actions are described. Methods rank data center objects and determine object performance trends. Methods calculate an object rank of each object of the data center over a period of time and calculate an object trend of each object of the data center based on relative frequencies of alerts at different times. The objects may be prioritized for remedial actions based on the object ranks and object trends.

公开(公告)号：US20240022466A1

公开(公告)日：2024-01-18

申请号：US17867353

申请日：2022-07-18

Applicant: VMware, Inc.

Inventor： Ashot Nshan Harutyunyan ,  Arnak Poghosyan ,  Naira Movses Grigoryan ,  Artur Grigoryan ,  Tigran Bunarjyan ,  Karen Aghajanyan ,  Vahan Tadevosyan ,  Tigran Avagimyants

IPC: H04L41/0604 ,  G06F9/451 ,  G06F7/08 ,  G06F16/28 ,  H04L41/22 ,  H04L41/0631

CPC classification number: H04L41/0609 ,  G06F9/451 ,  G06F7/08 ,  G06F16/285 ,  H04L41/22 ,  H04L41/065

Abstract: Automated computer-implemented methods and systems for discovering clusters of alerts triggered by abnormal events occurring with objects in a data center are described. In one aspect, alerts with start times in a sliding run-time window are retrieved from an alerts database. Each alert corresponds to a run-time event occurring with an object of the data center. Clusters of alerts in the sliding run-time window are detected based on the start times of the alerts and topological proximity of the objects. High priority alerts in the clusters of alerts are determined based on alert types. The events associated with discovered clusters of alerts and high priority alerts are displayed in a graphical user interface (“GUI”). Time evolution clustering of alerts and coverage evolution of alerts are over time based on the start times of the alerts and topological proximity of objects exhibiting abnormal behavior in the data center.