Patents by Inventor Naira Movses Grigoryan
Naira Movses Grigoryan has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20220027257Abstract: 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.Type: ApplicationFiled: October 18, 2020Publication date: January 27, 2022Applicant: VMware, Inc.Inventors: Ashot Nshan Harutyunyan, Arnak Poghosyan, Sunny Dua, Naira Movses Grigoryan, Karen Aghajanyan
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Publication number: 20220027249Abstract: 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.Type: ApplicationFiled: July 23, 2020Publication date: January 27, 2022Applicant: VMware, Inc.Inventors: Sunny Dua, Bonnie Zhang, Karen Aghajanyan, Hovhannes Antonyan, Ashot Nshan Harutyunyan, Arnak Poghosyan, Naira Movses Grigoryan
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Patent number: 11182267Abstract: Automated methods and systems to determine a baseline event-type distribution of an event source and use the baseline event type distribution to detect changes in the behavior of the event source are described. In one implementation, blocks of event messages generated by the event source are collected and an event-type distribution is computed for each of block of event messages. Candidate baseline event-type distributions are determined from the event-type distributions. The candidate baseline event-type distribution has the largest entropy of the event-type distributions. A normal discrepancy radius of the event-type distributions is computed from the baseline event-type distribution and the event-type distributions. A block of run-time event messages generated by the event source is collected. A run-time event-type distribution is computed from the block of run-time event messages.Type: GrantFiled: October 17, 2019Date of Patent: November 23, 2021Assignee: VMware, Inc.Inventors: Ashot Nshan Harutyunyan, Arnak Poghosyan, Nicholas Kushmerick, Naira Movses Grigoryan
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Patent number: 11184219Abstract: Methods and systems described herein are directed to troubleshooting anomalous behavior in a data center. Anomalous behavior in an object of a data center, such as a computational resource, an application, or a virtual machine (“VM”), may be related to the behavior of other objects at different hierarchies of the data center. Methods and systems provide a graphical user interface that enables a user to select a selected metric associated with an object of the data center experiencing a performance problem. Unexpected metrics of an object topology of the data center that correspond to the performance problem are identified. A recommendation for executing remedial measures to correct the performance problem is generated based on the unexpected metrics.Type: GrantFiled: January 14, 2020Date of Patent: November 23, 2021Assignee: VMware, Inc.Inventors: Ashot Nshan Harutyunyan, Naira Movses Grigoryan, Arnak Poghosyan, Hovhannes Antonyan, Vardan Hovhannisyan
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Publication number: 20210303438Abstract: 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.Type: ApplicationFiled: March 27, 2020Publication date: September 30, 2021Applicant: VMware, Inc.Inventors: Dev Nag, Naira Movses Grigoryan, Arnak Poghosyan, Ashot Nshan Harutyunyan
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Publication number: 20210303431Abstract: 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.Type: ApplicationFiled: December 11, 2020Publication date: September 30, 2021Applicant: VMware, Inc.Inventors: Naira Movses Grigoryan, Arnak Poghosyan, Ashot Nshan Harutyunyan, Clement Pang, Dev Nag
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Patent number: 11113174Abstract: 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.Type: GrantFiled: March 27, 2020Date of Patent: September 7, 2021Assignee: VMware, Inc.Inventors: Dev Nag, Naira Movses Grigoryan, Arnak Poghosyan, Ashot Nshan Harutyunyan
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Publication number: 20210216849Abstract: 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.Type: ApplicationFiled: January 18, 2021Publication date: July 15, 2021Applicant: VMware, Inc.Inventors: Arnak Poghosyan, Narek Hovhannisyan, Sirak Ghazaryan, George Oganesyan, Clement Pang, Ashot Nshan Harutyunyan, Naira Movses Grigoryan
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Publication number: 20210216559Abstract: 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.Type: ApplicationFiled: January 14, 2020Publication date: July 15, 2021Applicant: VMware, Inc.Inventors: Karen Aghajanyan, Hovhannes Antonyan, Naira Movses Grigoryan, Arnak Poghosyan, Ashot Nshan Harutyunyan, Sunny Dua, Bonnie Zhang
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Publication number: 20210218619Abstract: Methods and systems described herein are directed to troubleshooting anomalous behavior in a data center. Anomalous behavior in an object of a data center, such as a computational resource, an application, or a virtual machine (“VM”), may be related to the behavior of other objects at different hierarchies of the data center. Methods and systems provide a graphical user interface that enables a user to select a selected metric associated with an object of the data center experiencing a performance problem. Unexpected metrics of an object topology of the data center that correspond to the performance problem are identified. A recommendation for executing remedial measures to correct the performance problem is generated based on the unexpected metrics.Type: ApplicationFiled: January 14, 2020Publication date: July 15, 2021Applicant: VMware, Inc.Inventors: Ashot Nshan Harutyunyan, Naira Movses Grigoryan, Arnak Poghosyan, Hovhannes Antonyan, Vardan Hovhannisyan
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Publication number: 20210216848Abstract: 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.Type: ApplicationFiled: December 19, 2020Publication date: July 15, 2021Applicant: VMware, Inc.Inventors: Arnak Poghosyan, Ashot Nshan Harutyunyan, Naira Movses Grigoryan, Clement Pang, George Oganesyan, Sirak Ghazaryan, Narek Hovhannisyan
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Patent number: 11061796Abstract: Computational processes and systems are directed to detecting abnormally behaving objects of a distributed computing system. An object can be a physical or a virtual object, such as a server computer, application, VM, virtual network device, or container. Processes and systems identify a set of metrics associated with an object and compute an indicator metric from the set of metrics. The indicator metric is used to label time stamps that correspond to outlier metric values of the set of metrics. The metrics and outlier time stamps are used to compute rules by machine learning. Each rule corresponds to a subset or combination of metrics and represents specific threshold conditions for metric values. The rules are applied to run-time metric data of the metrics to detect run-time abnormal behavior of the object.Type: GrantFiled: February 19, 2019Date of Patent: July 13, 2021Assignee: VMware, Inc.Inventors: Ashot Nshan Harutyunyan, Naira Movses Grigoryan, Arnak Poghosyan, Nicholas Kushmerick
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Patent number: 11055382Abstract: Methods and systems that estimate a degree of abnormality of a complex system based on historical time-series data representative of the complex system's past behavior and using the historical degree of abnormality to determine whether or not a degree of abnormality determined from current time-series data representative of the same complex system's current behavior is worthy of attention. The time-series data may be metric data that represents behavior of a complex system as a result of successive measurements of the complex system made over time or in a time interval. A degree of abnormality represents the amount by which the time-series data violates a threshold. The larger the degree of abnormality of the current time-series data is from the historical degree of abnormality, the larger the violation of the thresholds and the greater the probability the violation in the current time-series data is worthy of attention.Type: GrantFiled: April 30, 2015Date of Patent: July 6, 2021Assignee: VMware, Inc.Inventors: Mazda A. Marvasti, Ashot Nshan Harutyunyan, Naira Movses Grigoryan, Arnak Poghosyan
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Patent number: 11050624Abstract: The current document is directed to methods and subsystems within computing systems, including distributed computing systems, that collect, store, process, and analyze population metrics for types and classes of system components, including components of distributed applications executing within containers, virtual machines, and other execution environments. In a described implementation, a graph-like representation of the configuration and state of a computer system included aggregation nodes that collect metric data for a set of multiple object nodes and that collect metric data that represents the members of the set over a monitoring time interval. Population metrics are monitored, in certain implementations, to detect outlier members of an aggregation.Type: GrantFiled: June 28, 2016Date of Patent: June 29, 2021Assignee: VMware, Inc.Inventors: Avetik Hovhannisyan, Ashot Nshan Harutyunyan, Naira Movses Grigoryan, Arnak Poghosyan
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Patent number: 11023353Abstract: Computational processes and systems are directed to forecasting time series data and detection of anomalous behaving resources of a distributed computing system data. Processes and systems comprise off-line and on-line modes that accelerate the forecasting process and identification of anomalous behaving resources. In the off-line mode, recurrent neural network (“RNN”) is continuously trained using time series data associated with various resources of the distributed computing system. In the on-line mode, the latest RNN is used to forecast time series data for resources in a forecast time window and confidence bounds are computed over the forecast time window. The forecast time series data characterizes expected resource usage over the forecast time window so that usage of the resource may be adjusted. The confidence bounds may be used to detect anomalous behaving resources. Remedial measures may then be executed to correct problems indicated by the anomalous behavior.Type: GrantFiled: January 17, 2019Date of Patent: June 1, 2021Assignee: VMware, Inc.Inventors: Arnak Poghosyan, Clement Pang, Ashot Nshan Harutyunyan, Naira Movses Grigoryan
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Patent number: 10997009Abstract: The current document is directed to methods and systems for detecting the occurrences of abnormal events and operational behaviors within the distributed computer system. The currently described methods and systems continuously collect metric data from various metric-data sources, generate a sequence of metric-data observations, each metric-data observation comprising a set of temporally aligned metric data, and employ principle-component analysis to transform the metric-data observations to facilitate reduction of the dimensionality of the metric-data observations.Type: GrantFiled: December 10, 2018Date of Patent: May 4, 2021Assignee: VMware, Inc.Inventors: Arnak Poghosyan, Ashot Nshan Harutyunyan, Naira Movses Grigoryan, Nicholas Kushmerick
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Publication number: 20210124665Abstract: 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.Type: ApplicationFiled: January 2, 2021Publication date: April 29, 2021Applicant: VMware, Inc.Inventors: Arnak Poghosyan, Ashot Nshan Harutyunyan, Naira Movses Grigoryan, Vaghinak Saghatelyan, Vahe Khachikyan
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Patent number: 10977151Abstract: Processes and systems described herein are directed to determining efficient sampling rates for metrics generated by various different metric sources of a distributed computing system. In one aspect, processes and systems retrieve the metrics from metric data storage and determine non-constant metrics of the metrics generated by the various metric sources. Processes and systems separately determine an efficient sampling rate for each non-constant metric by constructing a plurality of corresponding reduced metrics, each reduced metric comprising a different subsequence of the corresponding metric. Information loss is computed for each reduced metric. An efficient sampling rate is determined for each metric based on the information losses created by constructing the reduced metrics. The efficient sampling rates are applied to corresponding streams of run-time metric values and may also be used to resample the corresponding metric already stored in metric data storage, reducing storage space for the metrics.Type: GrantFiled: May 9, 2019Date of Patent: April 13, 2021Assignee: VMware, Inc.Inventors: Ashot Nshan Harutyunyan, Arnak Poghosyan, Naira Movses Grigoryan
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Patent number: 10901869Abstract: 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.Type: GrantFiled: November 7, 2017Date of Patent: January 26, 2021Assignee: VMware, Inc.Inventors: Arnak Poghosyan, Ashot Nshan Harutyunyan, Naira Movses Grigoryan, Vaghinak Saghatelyan, Vahe Khachikyan
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Publication number: 20200356459Abstract: Processes and systems described herein are directed to determining efficient sampling rates for metrics generated by various different metric sources of a distributed computing system. In one aspect, processes and systems retrieve the metrics from metric data storage and determine non-constant metrics of the metrics generated by the various metric sources. Processes and systems separately determine an efficient sampling rate for each non-constant metric by constructing a plurality of corresponding reduced metrics, each reduced metric comprising a different subsequence of the corresponding metric. Information loss is computed for each reduced metric. An efficient sampling rate is determined for each metric based on the information losses created by constructing the reduced metrics. The efficient sampling rates are applied to corresponding streams of run-time metric values and may also be used to resample the corresponding metric already stored in metric data storage, reducing storage space for the metrics.Type: ApplicationFiled: May 9, 2019Publication date: November 12, 2020Applicant: VMware, Inc.Inventors: Ashot Nshan Harutyunyan, Arnak Poghosyan, Naira Movses Grigoryan