公开(公告)号：US20230229537A1

公开(公告)日：2023-07-20

申请号：US17577329

申请日：2022-01-17

Applicant: VMware, Inc.

Inventor： Ashot Nshan Harutyunyan ,  Nelli Aghajanyan ,  Lilit Harutyunyan ,  Arnak Poghosyan ,  Tigran Bunarjyan

IPC: G06F11/07 ,  G06N5/02

CPC classification number: G06F11/0757 ,  G06N5/022 ,  G06F11/0766 ,  G06F11/0709

Abstract: The current document is directed to methods and systems that automatically generate training data for machine-learning-based components used by a metric-data processing-and-analysis component of a distributed computer system, a subsystem within a distributed computer system, or a standalone metric-data processing-and-analysis system. The training data sets are labeled using categorical KPI values. The machine-learning-based components are applied to metric data both for predicting anomalous operational behaviors and problems within the distributed computer system and for determination of potential causes of anomalous operational behaviors and problems within the distributed computer system. Training of machine-learning-based components is carried out concurrently and asynchronously with respect to other metric-data collection, aggregation, processing, storage, and analysis tasks.

公开(公告)号：US20230252109A1

公开(公告)日：2023-08-10

申请号：US17577286

申请日：2022-01-17

Applicant: VMware, Inc

Inventor： Ashot Nshan Harutyunyan ,  Tigran Bunarjyan ,  Arnak Poghosyan ,  Karine Aleksanyan

IPC: G06K9/62 ,  G06F11/30

CPC classification number: G06K9/6278 ,  G06F11/3075 ,  G06F11/3082 ,  G06K9/6284 ,  G06K9/6297

Abstract: The current document is directed to improved methods and systems that collect, generate, and store multidimensional metric data used for monitoring, management, and administration of computer systems and that continuously optimize sampling rates for metric data. Multiple different metric-data streams are sampled for each of multiple different distributed-computer-system objects, and are hierarchically organized into a number of different individual and multidimensional metric-data streams. The sampling rates for the different individual and multidimensional metric-data streams are correspondingly hierarchically optimized in order to avoid oversampling the metric data while preserving the relevant information content of the sampled metric data for downstream data analysis.

公开(公告)号：US20240028444A1

公开(公告)日：2024-01-25

申请号：US18096752

申请日：2023-01-13

Applicant: VMWare, Inc.

Inventor： Ashot Nshan Harutyunyan ,  Arnak Poghosyan ,  Lilit Harutyunyan ,  Nelli Aghajanyan ,  Tigran Bunarjyan ,  Marine Harutyunyan ,  Sam Israelyan

IPC: G06F11/07

CPC classification number: G06F11/079 ,  G06F11/0721 ,  G06F11/0769 ,  G06N20/20

Abstract: Automated computer-implemented methods and systems for resolving performance problems with objects executing in a data center are described. The automated methods use machine learning to obtain rules defining relationships between probabilities of event types of in log messages and performance problems identified by a key performance indictor (“KPI”) of the object. When a KPI violates a corresponding threshold, the rules are used to evaluate run time log messages that describe the probable root cause of the performance problem. An alert identifying the KPI threshold violation, and the log messages are displayed in a graphical user interface of an electronic display device.

公开(公告)号：US20230229675A1

公开(公告)日：2023-07-20

申请号：US17577318

申请日：2022-01-17

Applicant: VMware, Inc.

Inventor： Ashot Hautyunyan ,  Arnak Poghosyan ,  Tigran Bunarjyan ,  Naira Movses Grigoryan

IPC: G06F16/28

CPC classification number: G06F16/285

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, two or more metric-data sets are combined to generate a multidimensional metric-data set. The multidimensional metric-data set is compressed for efficient storage by clustering the multidimensional data points within the multidimensional metric-data set to produce a covering subset of multidimensional data points and by then representing the multidimensional-data-point members of each cluster by a cluster identifier rather than by a set of floating-point values, integer values, or other types of data representations. The covering set is constructed to ensure that the compression does not result in greater than a specified level of distortion of the original data.

公开(公告)号：US20250130871A1

公开(公告)日：2025-04-24

申请号：US18381520

申请日：2023-10-18

Applicant: VMware, Inc.

Inventor： Ashot Nshan Harutyunyan ,  Arnak Poghosyan ,  Tigran Bunarjyan ,  Andranik Haroyan ,  Marine Harutyunyan ,  Litit Harutyunyan ,  Ashot Baghdasaryan

IPC: G06F9/54 ,  G06F9/451 ,  G06F17/18

Abstract: This disclosure is directed to automated computer-implemented methods for application discovery from log messages generated by event sources of applications executing in a cloud infrastructure. The methods are executed by an operations manager that constructs a data frame of probability distributions of event types of the log messages generated by the event sources in a time period. The operations manager executes clustering techniques that are used to form clusters of the probability distributions in the data frame, where each of the clusters corresponds to one of the applications. The operations manager displays the clusters of the probability distributions in a two-dimensional map of applications in a graphical user interface that enables a user to select one of the clusters in the map of applications that corresponds to one of the applications and launch clustering of probability distributions of the user-selected cluster to discover two or more instances of the application.

公开(公告)号：US20240028955A1

公开(公告)日：2024-01-25

申请号：US18100159

申请日：2023-01-23

Applicant: VMware, Inc.

Inventor： Ashot Nshan Harutyunyan ,  Arnak Poghosyan ,  Lilit Harutyunyan ,  Nelli Aghajanyan ,  Tigran Bunarjyan ,  Marine Harutyunyan ,  Sam Israelyan

IPC: G06N20/00 ,  G06N5/04

CPC classification number: G06N20/00 ,  G06N5/04

Abstract: Automated, computer-implemented methods and systems describe herein resolve performance problems with objects executing in a data center. The operations manager uses machine learning to train an inference model that relates probability distributions of event types of log messages of the object to a key performance indicator (“KPI”) of the object. The operations manager monitors the KPI for run-time KPI values that violates a KPI threshold. When the KPI violates the threshold, the operations manager determines probabilities of event types of log messages recorded in a run-time interval and uses the inference model to determine event types of the probabilities of event types of log messages in the run-time interval to determine a root cause of the performance problem. The inference models can be used to identify log messages of event types that correspond to potential performance problems with data center objects and execute appropriate remedial measures to avoid the problems.

公开(公告)号：US20240028442A1

公开(公告)日：2024-01-25

申请号：US17871080

申请日：2022-07-22

Applicant: VMware, Inc.

Inventor： Ashot Nshan Harutyunyan ,  Arnak Poghosyan ,  Lilit Harutyunyan ,  Nelli Aghajanyan ,  Tigran Bunarjyan ,  Marine Harutyunyan ,  Sam Israelyan

IPC: G06F11/07

CPC classification number: G06F11/079 ,  G06F11/0769

Abstract: Automated, computer-implemented methods and systems for resolving performance problems with objects executing in a data center are described. The automated methods use machine learning to train a model that comprises rules defining relationships between probabilities of event types of in log messages and values of a key performance indictor (“KPI”) of the object over a historical time period. When a KPI violates a corresponding threshold, the rules are used to evaluate run time log messages that describe the probable root cause of the performance problem. An alert identifying the KPI threshold violation, and the log messages are displayed in a graphical user interface of an electronic display device.

公开(公告)号：US20250053496A1

公开(公告)日：2025-02-13

申请号：US18232743

申请日：2023-08-10

Applicant: VMware, Inc.

Inventor： Ashot Baghdasaryan ,  Tigran Bunarjyan ,  Arnak Poghosyan ,  Ashot Nshan Harutyunyan ,  Jad El-Zein

IPC: G06F11/36 ,  G06N5/022

Abstract: This disclosure is directed to automated computer-implemented methods and systems for detecting and correcting a trending problem with an application executing in a data center. The methods receive a new support request entered via a graphical user interface. The methods perform trend discovery of the new support request over recent time windows using a pre-trained and fine-tuned model bidirectional encoder representation from transformer. In response to detecting a trending problem described in the new support request, the method discovers recommended remedial measures for the new support request based on similar support requests previously recorded in a support request data store or on similar knowledge base articles previously recorded in a knowledge base data store. The recommended remedial measures for correcting the trending problem are executed using an operations manager of the data center.

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.