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.

公开(公告)号：US20230281070A1

公开(公告)日：2023-09-07

申请号：US17683601

申请日：2022-03-01

Applicant: VMware, Inc.

Inventor： Ashot Nshan Harutyunyan ,  Arnak Poghosyan ,  Naira Movses Grigoryan

IPC: G06F11/07 ,  G06F11/30 ,  G06N20/00 ,  G06K9/62

CPC classification number: G06F11/079 ,  G06F11/3006 ,  G06N20/00 ,  G06K9/6256

Abstract: Automated methods and systems for identifying and resolving performance problems of objects of a data center are described. The automated methods and systems construct a model for identifying objects of the datacenter that are experiencing performance problems based on baseline distributions of events of the objects in a historical time period and event distributions of events of the objects in a time window located outside the historical time period. A root causes and recommendations database is constructed for resolving performance problems based on remedial measures previously performed for resolving performance problems. The model is used to monitor the objects of data center for runtime performance problems. When a performance problem with an object is detected, the root causes and recommendations database is used to identify a root cause of the performance problem and generate a recommendation for resolving the performance problem in near real time.

公开(公告)号：US20220027257A1

公开(公告)日：2022-01-27

申请号：US17073381

申请日：2020-10-18

Applicant: VMware, Inc.

Inventor： Ashot Nshan Harutyunyan ,  Arnak Poghosyan ,  Sunny Dua ,  Naira Movses Grigoryan ,  Karen Aghajanyan

IPC: G06F11/36 ,  G06F16/2457 ,  G06N20/00

Abstract: Methods and systems described herein automate troubleshooting a problem in execution of an application in a distributed computing. Methods and systems learn interesting patterns in problem instances over time. The problem instances are displayed in a graphical user interface (“GUI”) that enables a user to assign a problem type label to each historical problem instance. A machine learning model is trained to predict problem types in executing the application based on the historical problem instances and associated problem types. In response to detecting a run-time problem instance in the execution of the application. the machine learning model is used to determine one or more problem types associated with the run-time problem instance. The one or more problem types are rank-ordered and a recommendation may be generated to correct the run-time problem instance based on the highest ranked problem type.

公开(公告)号：US20210303431A1

公开(公告)日：2021-09-30

申请号：US17119462

申请日：2020-12-11

Applicant: VMware, Inc.

Inventor： Naira Movses Grigoryan ,  Arnak Poghosyan ,  Ashot Nshan Harutyunyan ,  Clement Pang ,  Dev Nag

IPC: G06F11/30 ,  G06F11/32 ,  G06F11/34

Abstract: The current document is directed to methods and systems that employ distributed-computer-system metrics collected by one or more distributed-computer-system metrics-collection services, call traces collected by one or more call-trace services, and attribute values for distributed-computer-system components to identify attribute dimensions related to anomalous behavior of distributed-computer-system components. In a described implementation, nodes correspond to particular types of system components and node instances are individual components of the component type corresponding to a node. Node instances are associated with attribute values and node are associated with attribute-value spaces defined by attribute dimensions. A set of call traces is partitioned, by clustering. Using attribute values and call traces, attribute dimensions that are likely related to particular anomalous behaviors of distributed-computer-system components are determined by decision-tree-related analyses for each partition and are reported to one or more computational entities to facilitate resolution of the anomalous behaviors.

公开(公告)号：US11113174B1

公开(公告)日：2021-09-07

申请号：US16833102

申请日：2020-03-27

Applicant: VMware, Inc.

Inventor： Dev Nag ,  Naira Movses Grigoryan ,  Arnak Poghosyan ,  Ashot Nshan Harutyunyan

IPC: G06F11/34 ,  G06F11/30

Abstract: The current document is directed to methods and systems that employ distributed-computer-system metrics collected by one or more distributed-computer-system metrics-collection services, call traces collected by one or more call-trace services, and attribute values for distributed-computer-system components to identify attribute dimensions related to anomalous behavior of distributed-computer-system components. In a described implementation, nodes correspond to particular types of system components and node instances are individual components of the component type corresponding to a node. Node instances are associated with attribute values and node are associated with attribute-value spaces defined by attribute dimensions. Using attribute values and call traces, attribute dimensions that are likely related to particular anomalous behaviors of distributed-computer-system components are determined by decision-tree-related analyses and are reported to one or more computational entities to facilitate resolution of the anomalous behaviors.

公开(公告)号：US20210216849A1

公开(公告)日：2021-07-15

申请号：US17151610

申请日：2021-01-18

Applicant: VMware, Inc.

Inventor： Arnak Poghosyan ,  Narek Hovhannisyan ,  Sirak Ghazaryan ,  George Oganesyan ,  Clement Pang ,  Ashot Nshan Harutyunyan ,  Naira Movses Grigoryan

IPC: G06N3/04 ,  G06N3/08

Abstract: The current document is directed to methods and systems that generate forecasts based on input time-series data using a forecasting neural network or other machine-learning-based forecasting subsystem. In various implementations, an input time series is first classified and then transformed, based on the classification, to a corresponding stationary time series. The corresponding stationary time series is then submitted to a neural network or other machine-learning-based forecasting subsystem to generate an initial forecast for future time points. The initial forecast is then inverse transformed, based on the input-time-series classification, to generate a final, output forecast.

公开(公告)号：US20210216848A1

公开(公告)日：2021-07-15

申请号：US17128089

申请日：2020-12-19

Applicant: VMware, Inc.

Inventor： Arnak Poghosyan ,  Ashot Nshan Harutyunyan ,  Naira Movses Grigoryan ,  Clement Pang ,  George Oganesyan ,  Sirak Ghazaryan ,  Narek Hovhannisyan

IPC: G06N3/04 ,  G06N3/08

Abstract: The current document is directed to improved system monitoring and management tools and methods based on generation an anomaly signal from time-series data collected from components of a computer system, providing improved system monitoring and management. The time series data comprises a time-ordered sequence of metric datapoints that is received over a period of time. At each of a set of discrete, successive time points within the period of time, a datapoint for the anomaly signal is generated from a forecast generated from a preceding set of time-series datapoints, referred to as a “history window,” and a short segment of the time series, referred to as the “observation window,” extending forward in time from the most recently datapoint in the history window. The anomaly signal predicts incipient anomalous conditions in the computer system.

公开(公告)号：US10901869B2

公开(公告)日：2021-01-26

申请号：US15805424

申请日：2017-11-07

Applicant: VMware, Inc.

Inventor： Arnak Poghosyan ,  Ashot Nshan Harutyunyan ,  Naira Movses Grigoryan ,  Vaghinak Saghatelyan ,  Vahe Khachikyan

IPC: G06F11/30 ,  G06F11/34

Abstract: The current document is directed to methods and systems that collect metric data within computing facilities, including large data centers and cloud-computing facilities. In a described implementation, lower and higher metric-data-value thresholds are used to partition collected metric data into outlying metric data and inlying metric data. The inlying metric data is quantized to compress the inlying metric data and adjacent data points having the same quantized metric-data values are eliminated, to further compress the inlying metric data. The resulting compressed data includes original metric-data representations for outlier data points and compressed metric-data representations for inlier data points, providing accurate restored metric-data values for significant data points when compressed metric data is decompressed.

公开(公告)号：US20190138419A1

公开(公告)日：2019-05-09

申请号：US15805424

申请日：2017-11-07

Applicant: VMware, Inc.

Inventor： Arnak Poghosyan ,  Ashot Nshan Harutyunyan ,  Naira Movses Grigoryan ,  Vaghinak Saghatelyan ,  Vahe Khachikyan

IPC: G06F11/30

Abstract: The current document is directed to methods and systems that collect metric data within computing facilities, including large data centers and cloud-computing facilities. In a described implementation, lower and higher metric-data-value thresholds are used to partition collected metric data into outlying metric data and inlying metric data. The inlying metric data is quantized to compress the inlying metric data and adjacent data points having the same quantized metric-data values are eliminated, to further compress the inlying metric data. The resulting compressed data includes original metric-data representations for outlier data points and compressed metric-data representations for inlier data points, providing accurate restored metric-data values for significant data points when compressed metric data is decompressed.

公开(公告)号：US20190026459A1

公开(公告)日：2019-01-24

申请号：US15653269

申请日：2017-07-18

Applicant: VMware, Inc.

Inventor： Ashot Nshan Harutyunyan ,  Arnak Poghosyan ,  Nara Movses Grigoryan ,  Vardan Movsisyan

IPC: G06F21/50 ,  H04L12/26 ,  H04L29/06

Abstract: Methods and systems are directed to automatically analyzing the behavior of event sources, detecting anomalies in the behavior of event sources, and generating recommendations to correct the detected anomalies. An event source can be an application program, an operating system, a virtual machine, a container, or any other source of event messages in a computer system. Method quantify the event messages generated over time to form property time series data, which is metadata regarding the event messages generated by the event source. Methods compute a threshold from the property time series data. Methods detect abnormal states of the event source when property data points of the property time series data violate the threshold. A systems administrator may be notified by a property digression alert displayed on a system console. Methods also generate a recommendation to correct the anomalous behavior and optimize performance of the event source.