Patents by Inventor Arnak Poghosyan
Arnak Poghosyan 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: 20170364581Abstract: Methods and systems to evaluate importance of metrics generated in a data center and ranking metric in order of relevance to data center performance are described. Methods collect sets of metric data generated in a data center over a period of time and categorize each set of metric data as being of high importance, medium importance, or low importance. Methods also calculate a rank ordering of each set of high importance and medium importance metric data. By determining importance of data center metrics, an optimal usage and distribution of computational and storage resources of the data center may be determined.Type: ApplicationFiled: June 16, 2016Publication date: December 21, 2017Applicant: VMware, Inc.Inventors: Ashot Nshan Harutyunyan, Arnak Poghosyan, Naira Movses Grigoryan, Hovhannes Antonyan
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Publication number: 20170364391Abstract: Methods determine a capacity-forecast model based on historical capacity metric data and historical business metric data. The capacity-forecast model may be to estimate capacity requirements with respect to changes in demand for the data center customer's application program. The capacity-forecast model provides an analytical “what-if” approach to reallocating data center resources in order to satisfy projected business level expectations of a data center customer and calculate estimated capacities for different business scenarios.Type: ApplicationFiled: June 16, 2016Publication date: December 21, 2017Applicant: VMware, Inc.Inventors: Arnak Poghosyan, Ashot Nshan Harutyunyan, Naira Movses Grigoryan, Khachatur Nazaryan, Ruzan Hovhannisyan
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Publication number: 20170353362Abstract: Methods recommend to data center customers those attributes of a data center infrastructure and application program that are associated with service-level objective (“SLO”) metric degradation and may be recorded in problem definitions. In other words, a data center customer is offered to “codify” problems primarily with atomic abnormality conditions on indicated attributes that decrease the SLO by some degree that the data center customer would like to be aware. As a result, the data center customer is warned of potentially significant SLO decline in order to prevent unwanted loss and take any necessary actions to prevent active anomalies. Methods also generate patterns of attributes that constitute core structures highly associated with degradation of the SLO metric.Type: ApplicationFiled: June 6, 2016Publication date: December 7, 2017Applicant: VMware, Inc.Inventors: Ashot Nshan Harutyunyan, Naira Movses Grigoryan, Arnak Poghosyan
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Publication number: 20170353345Abstract: A problem in a cloud infrastructure may be identified when a server computer deviates from a normal level of operation based on anomaly scores, which generates an alert and an alert time that indicates when the alert is generated. Methods then determine which virtual machine (“VM”) and other IT objects/resources or their pools contribute to the problem within a time window surrounding the estimated problem start time and calculate which objects show similar, related anomalous behavior. Method also generate ranked remediation recommendations on an object level and server computer-to-object level. The methods generate results that enable a system administrator to identify the start time of the problem and identify the objects that are responsible for the problem.Type: ApplicationFiled: June 3, 2016Publication date: December 7, 2017Applicant: VMware, Inc.Inventors: Naira Movses Grigoryan, Arnak Poghosyan, Ashot Nshan Harutyunyan, Mazda A. Marvasti
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Publication number: 20170255537Abstract: This disclosure is directed to data-agnostic computational methods and systems for adjusting hard thresholds based on user feedback. Hard thresholds are used to monitor time-series data generated by a data-generating entity. The time-series data may be metric data that represents usage of the data-generating entity over time. The data is compared with a hard threshold associated with usage of the resource or process and when the data violates the threshold, an alert is typically generated and presented to a user. Methods and systems collect user feedback after a number of alerts to determine the quality and significance of the alerts. Based on the user feedback, methods and systems automatically adjust the hard thresholds to better represent how the user perceives the alerts.Type: ApplicationFiled: April 4, 2017Publication date: September 7, 2017Applicant: VMware, Inc.Inventors: Naira Movses Grigoryan, Mazda A. Marvasti, Arnak Poghosyan, Ashot Nshan Harutyunyan, Yanislav Yankov
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Patent number: 9742435Abstract: The current document is directed to a multi-stage metric-data compression method and subsystem for compressing metric data collected and stored within distributed computing systems to facilitate computer-system management and administration. In a described implementation, metric data is partitioned into constant metric data, low-variability metric data, and high-variability metric data. High-variability metric data is compressed by identifying a set of basis metrics, or independent metrics, with respect to which a remaining set of dependent metrics can be expressed using coefficient multipliers. The high-variability metric data can then be stored as a set of independent metrics and set of coefficients, along with a small amount of additional data.Type: GrantFiled: June 21, 2016Date of Patent: August 22, 2017Assignee: VMware, Inc.Inventors: Arnak Poghosyan, Ashot Nshan Harutyunyan, Naira Movses Grigoryan, Vahe Khachikyan, Meruzhan Kerobyan
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Patent number: 9632905Abstract: This disclosure is directed to data-agnostic computational methods and systems for adjusting hard thresholds based on user feedback. Hard thresholds are used to monitor time-series data generated by a data-generating entity. The time-series data may be metric data that represents usage of the data-generating entity over time. The data is compared with a hard threshold associated with usage of the resource or process and when the data violates the threshold, an alert is typically generated and presented to a user. Methods and systems collect user feedback after a number of alerts to determine the quality and significance of the alerts. Based on the user feedback, methods and systems automatically adjust the hard thresholds to better represent how the user perceives the alerts.Type: GrantFiled: June 24, 2014Date of Patent: April 25, 2017Assignee: VMware, Inc.Inventors: Naira Movses Grigoryan, Mazda A. Marvasti, Arnak Poghosyan, Ashot Nshan Harutyunyan, Yanislav Yankov
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Patent number: 9547710Abstract: Cycles and other patterns within time-series data are determined. Time-series data are transformed into discretized sets of clustered data that are organized by time period. Comparison is made of the organized data to determine similar time periods and multiclusters of the similar time periods are formed. From the multicluster data, cycles are identified from which thresholds and other useful data may be derived, or the data used for other useful purposes.Type: GrantFiled: August 5, 2008Date of Patent: January 17, 2017Assignee: VMware, Inc.Inventors: Mazda A. Marvasti, Astghik Grigoryan, Arnak Poghosyan, Naira Grigoryan, Ashot Harutyunyan
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Publication number: 20160321553Abstract: 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: ApplicationFiled: April 30, 2015Publication date: November 3, 2016Applicant: VMware, Inc.Inventors: Mazda A. Marvasti, Ashot Nshan Harutyunyan, Naira Movses Grigoryan, Arnak Poghosyan
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Publication number: 20160323157Abstract: Methods and systems that manage large volumes of metric data generation by cloud-computing infrastructures are described. The cloud-computing infrastructure generates sets of metric data, each set of metric data may represent usage or performance of an application or application module run by the cloud-computing infrastructure or may represent use or performance of cloud-computing resources used by the applications. The metric data management methods and systems are composed of separate modules that perform sequential application of metric data reduction techniques on different levels of data abstraction in order to reduce volume of metric data collected. In particular, the modules determine normalcy bounds, delete highly correlated metric data, and delete metric data with highly correlated normalcy bound violations.Type: ApplicationFiled: April 30, 2015Publication date: November 3, 2016Applicant: VMware, Inc.,Inventors: Mazda A. Marvasti, Ashot Nshan Harutyunyan, Naira Movses Grigoryan, Arnak Poghosyan
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Patent number: 9466031Abstract: This disclosure is directed to computational, closed-loop user feedback systems and methods for ranking or updating beliefs for a user based on user feedback. The systems and methods are based on a data-agnostic user feedback formulation that uses user feedback to automatically rank beliefs for a user or update the beliefs. The methods and systems are based on a general statistical inference model, which, in turn, is based on an assumption of convergence in user opinion. The closed-loop user feedback methods and systems may be used to rank or update beliefs prior to inputting the beliefs to a recommender engine. As a result, the recommender engine is expected to be more responsive to customer environments and efficient at deployment and reducing the level of unnecessary user recommendations.Type: GrantFiled: December 12, 2013Date of Patent: October 11, 2016Assignee: VMware, Inc.Inventors: Ashot Nshan Harutyunyan, Naira Movses Grigoryan, Mazda A. Marvasti, Arnak Poghosyan, Yanislav Yankov
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Patent number: 9298538Abstract: This disclosure presents systems and methods for run-time analysis of streams of log data for abnormalities using a statistical structure of meta-data associated with the log data. The systems and methods convert a log data stream into meta-data and perform statistical analysis in order to reveal a dominant statistical pattern within the meta-data. The meta-data is represented as a graph with nodes that represent each of the different event types, which are detected in the stream along with event sources associated with the events. The systems and methods use real-time analysis to compare a portion of a current log data stream collected in an operational window with historically collected meta-data represented by a graph in order to determine the degree of abnormality of the current log data stream collected in the operational window.Type: GrantFiled: August 6, 2013Date of Patent: March 29, 2016Assignee: VMware, Inc.Inventors: Mazda A. Marvasti, Arnak Poghosyan, Ashot Harutyunyan, Naira Grigoryan
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Patent number: 9245000Abstract: Cycles and other patterns within time-series data are determined. Time-series data are transformed into discretized sets of clustered data that are organized by time period. Comparison is made of the organized data to determine similar time periods and multiclusters of the similar time periods are formed. From the multicluster data, cycles are identified from which thresholds and other useful data may be derived, or the data used for other useful purposes.Type: GrantFiled: August 5, 2008Date of Patent: January 26, 2016Assignee: VMware, Inc.Inventors: Mazda A. Marvasti, Astghik Grigoryan, Arnak Poghosyan, Naira Grigoryan, Ashot Harutyunyan
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Publication number: 20150379110Abstract: This disclosure is directed to automated methods and systems for calculating hard thresholds used to monitor time-series data generated by data-generating entity. The methods are based on determining a cumulative distribution that characterizes the probability that data values of time-series data generated by the data-generating entity violate a hard threshold. The hard threshold is calculated as an inverse of the cumulative distribution based on a user defined risk confidence level. The hard threshold may then be used to generate alerts when time-series data generated later by the data-generating entity violate the hard threshold.Type: ApplicationFiled: June 25, 2014Publication date: December 31, 2015Applicant: VMware, Inc.Inventors: Mazda A. Marvasti, Ashot Nshan Harutyunyan, Naira Movses Grigoryan, Arnak Poghosyan
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Publication number: 20150370682Abstract: This disclosure is directed to data-agnostic computational methods and systems for adjusting hard thresholds based on user feedback. Hard thresholds are used to monitor time-series data generated by a data-generating entity. The time-series data may be metric data that represents usage of the data-generating entity over time. The data is compared with a hard threshold associated with usage of the resource or process and when the data violates the threshold, an alert is typically generated and presented to a user. Methods and systems collect user feedback after a number of alerts to determine the quality and significance of the alerts. Based on the user feedback, methods and systems automatically adjust the hard thresholds to better represent how the user perceives the alerts.Type: ApplicationFiled: June 24, 2014Publication date: December 24, 2015Applicant: VMware, Inc.Inventors: Naira Movses Grigoryan, Mazda A. Marvasti, Arnak Poghosyan, Ashot Nshan Harutyunyan, Yanislav Yankov
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Publication number: 20140298098Abstract: This disclosure presents computational systems and methods for detecting anomalies in data output from any type of monitoring tool. The data is aggregated and sent to an alerting system for abnormality detection via comparison with normalcy bounds. The anomaly detection methods are performed by construction of normalcy bounds of the data based on the past behavior of the data output from the monitoring tool. The methods use data quality assurance and data categorization processes that allow choosing a correct procedure for determination of the normalcy bounds. The methods are completely data agnostic, and as a result, can also be used to detect abnormalities in time series data associated with any complex system.Type: ApplicationFiled: March 29, 2013Publication date: October 2, 2014Inventors: Arnak Poghosyan, Ashot Nshan Harutyunyan, Naira Movses Grigoryan, Mazda A. Marvasti
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Patent number: 8751867Abstract: An approach to root cause determination in a complex systems based on monitoring and event data is disclosed. It includes a historical analysis of events with their probabilistic correlations. Applying information measures between the random variables which embody those events one can detect origins of problems and generate real-time recommendations for their locations in a hierarchical system. Estimation of system bottlenecks, as well as the risk of “black swan”-type events are also computed. The processes are based on a statistical processing of a virtual directed graph produced from historical events.Type: GrantFiled: October 12, 2011Date of Patent: June 10, 2014Assignee: VMware, Inc.Inventors: Mazda A. Marvasti, Arnak Poghosyan, Ashot Harutyunyan, Naira Grigoryan
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Publication number: 20140053025Abstract: This disclosure presents systems and methods for run-time analysis of streams of log data for abnormalities using a statistical structure of meta-data associated with the log data. The systems and methods convert a log data stream into meta-data and perform statistical analysis in order to reveal a dominant statistical pattern within the meta-data. The meta-data is represented as a graph with nodes that represent each of the different event types, which are detected in the stream along with event sources associated with the events. The systems and methods use real-time analysis to compare a portion of a current log data stream collected in an operational window with historically collected meta-data represented by a graph in order to determine the degree of abnormality of the current log data stream collected in the operational window.Type: ApplicationFiled: August 6, 2013Publication date: February 20, 2014Inventors: Mazda A. Marvasti, Arnak Poghosyan, Ashot Harutyunyan, Naira Grigoryan
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Publication number: 20130097463Abstract: An approach to root cause determination in a complex systems based on monitoring and event data is disclosed. It includes a historical analysis of events with their probabilistic correlations. Applying information measures between the random variables which embody those events one can detect origins of problems and generate real-time recommendations for their locations in a hierarchical system. Estimation of system bottlenecks, as well as the risk of “black swan”-type events are also computed. The processes are based on a statistical processing of a virtual directed graph produced from historical events.Type: ApplicationFiled: October 12, 2011Publication date: April 18, 2013Applicant: VMWARE, INC.Inventors: Mazda A. MARVASTI, Arnak POGHOSYAN, Ashot HARUTYUNYAN, Naira GRIGORYAN
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Publication number: 20100036643Abstract: Cycles and other patterns within time-series data are determined. Time-series data are transformed into discretized sets of clustered data that are organized by time period. Comparison is made of the organized data to determine similar time periods and multiclusters of the similar time periods are formed. From the multicluster data, cycles are identified from which thresholds and other useful data may be derived, or the data used for other useful purposes.Type: ApplicationFiled: August 5, 2008Publication date: February 11, 2010Inventors: Mazda A. Marvasti, Astghik Grigoryan, Arnak Poghosyan, Naira Grigoryan, Ashot Harutyunyan