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公开(公告)号：US10558543B2
公开(公告)日：2020-02-11
申请号：US15806187
申请日：2017-11-07
Applicant: VMware, Inc.
Inventor： Ashot Nshan Harutyunyan , Arnak Poghosyan , Naira Movses Grigoryan , Vahe Khachikyan , Nshan Sharoyan
IPC: G06F15/173 , 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, 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.
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公开(公告)号：US10509712B2
公开(公告)日：2019-12-17
申请号：US15828227
申请日：2017-11-30
Applicant: VMware, Inc.
Abstract: 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. When the run-time event-type distribution is outside the normal discrepancy radius, an alert is generated indicating abnormal behavior of the event source.
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公开(公告)号：US10467119B2
公开(公告)日：2019-11-05
申请号：US15479182
申请日：2017-04-04
Applicant: VMware, Inc.
Inventor： Naira Movses Grigoryan , Mazda A. Marvasti , Arnak Poghosyan , Ashot Nshan Harutyunyan , Yanislav Yankov
Abstract: 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.
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公开(公告)号：US10452665B2
公开(公告)日：2019-10-22
申请号：US15627987
申请日：2017-06-20
Applicant: VMware, Inc.
Inventor： Arnak Poghosyan , Ashot Nshan Harutyunyan , Naira Movses Grigoryan
Abstract: Automated methods and systems to reduce the size of time series data while maintaining outlier data points are described. The time series data may be read from a data-storage device of a physical data center. Clusters of data points of the time series data are determined. A normalcy domain of the time series data and outlier data points of the time series data is determined. The normalcy domain of the time series data comprises ranges of values associated with each clusters of data points. The outlier data points are located outside the ranges. Quantized time series data are computed from the normalcy domain. When the loss of information due to quantization is less than a limit, the quantized time series data is compressed. The time series data in the data-storage device is replaced with the compressed time series data and outlier data points.
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25.发明申请公开(公告)号：US20190163598A1
公开(公告)日：2019-05-30
申请号：US15828227
申请日：2017-11-30
Applicant: VMware, Inc.
Abstract: 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. When the run-time event-type distribution is outside the normal discrepancy radius, an alert is generated indicating abnormal behavior of the event source.
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Patent Agency Ranking
公开(公告)号：US20190138420A1
公开(公告)日：2019-05-09
申请号：US15806187
申请日：2017-11-07
Applicant: VMware, Inc.
Inventor： Ashot Nshan Harutyunyan , Arnak Poghosyan , Naira Movses Grigoryan , Vahe Khachikyan , Nshan Sharoyan
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, 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.
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公开(公告)号：US20180365301A1
公开(公告)日：2018-12-20
申请号：US15627925
申请日：2017-06-20
Applicant: VMware, Inc.
Inventor： Arnak Poghosyan , Ashot Nshan Harutyunyan , Naira Movses Grigoryan
Abstract: Methods and systems quantize and compress time series data generated by a resource of a distributed computing system. The time series data is partitioned according to a set of quantiles. Quantized time series data is generated from the time series data and the quantiles. The quantized time series data is compressed by deleting sequential duplicate quantized data points from the quantized time series data to obtain compress time series data. Quantization and compression are performed for different combinations of quantiles. The user may choose to minimize information loss of information due to quantization while selecting a lower bound for the compression rate. Alternatively, the user may choose to maximize the compression rate while placing an upper limit on the loss of information due to quantization. The compressed time series data that satisfies the user selected optimization conditions may be used to replace the original time series data in the data-storage device.
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公开(公告)号：US20170364391A1
公开(公告)日：2017-12-21
申请号：US15184742
申请日：2016-06-16
Applicant: VMware, Inc.
Inventor： Arnak Poghosyan , Ashot Nshan Harutyunyan , Naira Movses Grigoryan , Khachatur Nazaryan , Ruzan Hovhannisyan
IPC: G06F9/50
CPC classification number: G06F9/5027 , G06F9/505
Abstract: 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.
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公开(公告)号：US20170353345A1
公开(公告)日：2017-12-07
申请号：US15172616
申请日：2016-06-03
Applicant: VMware, Inc.
CPC classification number: H04L41/064 , H04L41/0677 , H04L41/142 , H04L67/10
Abstract: 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.
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公开(公告)号：US09466031B1
公开(公告)日：2016-10-11
申请号：US14104351
申请日：2013-12-12
Applicant: VMware, Inc.
Inventor： Ashot Nshan Harutyunyan , Naira Movses Grigoryan , Mazda A. Marvasti , Arnak Poghosyan , Yanislav Yankov
CPC classification number: G06N99/005 , G06N5/02 , G06N7/005 , G06Q30/00
Abstract: 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.
Abstract translation: 本公开涉及用于基于用户反馈来对用户的信念进行排名或更新的计算，闭环用户反馈系统和方法。 系统和方法基于数据无关的用户反馈公式，其使用用户反馈自动对用户的信念进行排名或更新信念。 方法和系统基于一般的统计推理模型，反过来，它是基于用户意见收敛的假设。 闭环用户反馈方法和系统可以在将信念输入推荐者引擎之前对信念进行排名或更新。 因此，预计推荐者引擎将更能响应客户环境并且在部署时效率高，并降低不必要的用户建议的水平。
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