公开(公告)号：US20220291982A1

公开(公告)日：2022-09-15

申请号：US17374682

申请日：2021-07-13

Applicant: VMware, Inc.

Inventor： Arnak Poghosyan ,  Ashot Nshan Harutyunyan ,  Naira Movses Grigoryan ,  Clement Pang ,  George Oganesyan ,  Karen Avagyan

IPC: G06F11/07

Abstract: Computer-implemented methods and systems described herein perform intelligent sampling of application traces generated by an application. Computer-implemented methods and systems determine different sampling rates based on frequency of occurrence of normal traces and erroneous traces of the application. The sampling rates for low frequency normal and erroneous traces are larger than the sampling rates for high frequency normal and erroneous traces. The relatively larger sampling rates for low frequency trace ensures that low frequency traces are sampled in sufficient numbers and are not passed over during sampling of the application traces. The sampled normal and erroneous traces are stored in a data storage device.

公开(公告)号：US20210216860A1

公开(公告)日：2021-07-15

申请号：US16742594

申请日：2020-01-14

Applicant: VMware, Inc.

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

IPC: G06N3/08 ,  G06F16/2458 ,  G06N3/04

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：US20220058073A1

公开(公告)日：2022-02-24

申请号：US17492099

申请日：2021-10-01

Applicant: VMware, Inc.

Inventor： Amak Poghosyan ,  Ashot Nshan Harutyunyan ,  Naira Movses Grigoryan ,  Clement Pang ,  George Oganesyan ,  Davit Baghdasaryan

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

Abstract: The current document is directed to methods and systems that employ call traces collected by one or more call-trace services to generate call-trace-classification rules to facilitate root-cause analysis of distributed-application operational problems and failures. In a described implementation, a set of automatically labeled call traces is partitioned by the generated call-trace-classification rules. Call-trace-classification-rule generation is constrained to produce relatively simple rules with greater-than-threshold confidences and coverages. The call-trace-classification rules may point to particular services and service failures, which provides useful information to distributed-application and distributed-computer-system managers and administrators attempting to diagnose operational problems and failures that arise during execution of distributed applications within distributed computer systems. A first dataset is collected during normal distributed-application operation and a second dataset is collected during problem-associated or failure-associated operation of the distributed application. The first and second datasets are used to generate noise-subtracted call-trace-classification rules and/or diagnostic suggestions.

公开(公告)号：US20220283924A1

公开(公告)日：2022-09-08

申请号：US17367490

申请日：2021-07-05

Applicant: VMware, Inc.

Inventor： Arnak Poghosyan ,  Ashot Nshan Harutyunyan ,  Naira Movses Grigoryan ,  Clement Pang ,  George Oganesyan ,  Karen Avagyan

IPC: G06F11/34 ,  G06F11/07

Abstract: Computer-implemented methods and systems described herein perform intelligent sampling of application traces generated by an application. Computer-implemented methods and systems determine different sampling rates based on frequency of occurrence of trace types and/or frequency of occurrence of durations of the traces. Each sampling rate corresponds to a different trace type and/or different duration. The sampling rates for low frequency trace types and durations are larger than the sampling rates for high frequency trace types and durations. The relatively larger sampling rates for low frequency trace types and low frequency durations ensures that low frequency trace types and low frequency durations are sampled in sufficient numbers and are not passed over during sampling of the application traces. The set of sampled traces are stored in a data storage device.

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.