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1. 发明申请

US20150379110A1 AUTOMATED METHODS AND SYSTEMS FOR CALCULATING HARD THRESHOLDS 有权
标题翻译：自动计算硬度阈值的方法和系统
公开(公告)号：US20150379110A1
公开(公告)日：2015-12-31
申请号：US14314490
申请日：2014-06-25
申请人： VMware, Inc.
发明人： Mazda A. Marvasti , Ashot Nshan Harutyunyan , Naira Movses Grigoryan , Arnak Poghosyan
IPC分类号： G06F17/30
CPC分类号： G06F11/3452 , G05B23/0227 , G06F11/0706 , G06F11/0754 , G06F11/3409 , G06F2201/81
摘要： 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.
摘要翻译：本公开涉及用于计算用于监视由数据生成实体生成的时间序列数据的硬阈值的自动化方法和系统。这些方法基于确定表征数据生成实体产生的时间序列数据的数据值违反硬阈值的概率的累积分布。基于用户定义的风险可信度，硬阈值被计算为累积分布的倒数。随后由数据生成实体生成的时间序列数据违反硬阈值，硬阈值可用于产生警报。

2. 发明授权

US11055382B2 Methods and systems that estimate a degree of abnormality of a complex system 有权
公开(公告)号：US11055382B2
公开(公告)日：2021-07-06
申请号：US14701217
申请日：2015-04-30
申请人： VMware, Inc.
发明人： Mazda A. Marvasti , Ashot Nshan Harutyunyan , Naira Movses Grigoryan , Arnak Poghosyan
IPC分类号： G06F17/18 , G06K9/00
摘要： 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.

3. 发明授权

US09948528B2 Methods and systems to manage big data in cloud-computing infrastructures 有权
公开(公告)号：US09948528B2
公开(公告)日：2018-04-17
申请号：US14701066
申请日：2015-04-30
申请人： VMware, Inc.
发明人： Mazda A. Marvasti , Ashot Nshan Harutyunyan , Naira Movses Grigoryan , Arnak Poghosyan
IPC分类号： H04L12/26 , H04L29/08 , G06F11/34
CPC分类号： H04L43/04 , G06F11/34 , H04L43/02 , H04L43/08 , H04L67/1097
摘要： 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.

4. 发明申请

US20160321553A1 METHODS AND SYSTEMS THAT ESTIMATE A DEGREE OF ABNORMALITY OF A COMPLEX SYSTEM 审中-公开
标题翻译：估计复杂系统异常程度的方法和系统
公开(公告)号：US20160321553A1
公开(公告)日：2016-11-03
申请号：US14701217
申请日：2015-04-30
申请人： VMware, Inc.
发明人： Mazda A. Marvasti , Ashot Nshan Harutyunyan , Naira Movses Grigoryan , Arnak Poghosyan
IPC分类号： G06N5/04 , G06N7/00
CPC分类号： G06F17/18 , G06K9/0053 , G06K9/00543
摘要： 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.
摘要翻译：基于代表复杂系统过去行为的历史时间序列数据和使用历史异常程度来估计复杂系统的异常程度的方法和系统，以确定从当前时间序列数据确定的异常程度代表同样复杂系统的当前行为值得关注。时间序列数据可以是度量数据，其表示由于随着时间或时间间隔而进行的复杂系统的连续测量，复杂系统的行为。异常程度表示时间序列数据违反阈值的量。当前时间序列数据的异常程度越大，从历史异常程度来看，阈值越大，当前时间序列数据的违规概率越大。

5. 发明申请

US20140053025A1 METHODS AND SYSTEMS FOR ABNORMALITY ANALYSIS OF STREAMED LOG DATA 有权
标题翻译：流域日志数据异常分析的方法与系统
公开(公告)号：US20140053025A1
公开(公告)日：2014-02-20
申请号：US13960611
申请日：2013-08-06
申请人： VMware, Inc.
发明人： Mazda A. Marvasti , Arnak Poghosyan , Ashot Harutyunyan , Naira Grigoryan
IPC分类号： G06F11/07
CPC分类号： G06F11/079 , G06F11/0706 , G06F11/0754 , G06F2201/86
摘要： 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.
摘要翻译：本公开提供了使用与日志数据相关联的元数据的统计结构来运行时分析用于异常的日志数据流的系统和方法。系统和方法将日志数据流转换为元数据并执行统计分析，以显示元数据中的统计统计模式。元数据被表示为具有表示每个不同事件类型的节点的图，该事件类型与流中与事件相关联的事件源一起检测。系统和方法使用实时分析来比较在操作窗口中收集的当前日志数据流的一部分与由图表表示的历史收集的元数据，以便确定当前日志数据流的异常程度操作窗口。

6. 发明授权

US10467119B2 Data-agnostic adjustment of hard thresholds based on user feedback 有权
公开(公告)号：US10467119B2
公开(公告)日：2019-11-05
申请号：US15479182
申请日：2017-04-04
申请人： VMware, Inc.
发明人： Naira Movses Grigoryan , Mazda A. Marvasti , Arnak Poghosyan , Ashot Nshan Harutyunyan , Yanislav Yankov
IPC分类号： G06F11/34 , G06F11/07 , G06F11/32
摘要： 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.

7. 发明授权

US09882798B2 Method and system that analyzes operational characteristics of multi-tier applications 有权
公开(公告)号：US09882798B2
公开(公告)日：2018-01-30
申请号：US14711648
申请日：2015-05-13
申请人： VMware, Inc.
发明人： Pradeep Padala , Neelima Mukiri , Mazda A. Marvasti
IPC分类号： G06F15/173 , H04L12/26 , H04L29/08 , G06N7/00 , G06F9/455 , H04L29/14
CPC分类号： H04L43/10 , G06F9/45558 , G06F2009/45562 , G06F2009/45595 , G06N7/005 , H04L43/0817 , H04L67/10 , H04L67/20 , H04L67/2804 , H04L69/40
摘要： The current document is directed to an analysis subsystem within a large distributed computing system, such as a virtual data center or cloud-computing facility, that monitors the operational states associated with a multi-tiered application and provides useful information for determining one or more causes of various types of failures and undesirable operational states that may arise during operation of the multi-tiered application. In one implementation, the analysis subsystem collects metrics provided by various different types of metrics sources within the computational system and employs principal feature analysis to select a generally small subset of the collected metrics particularly relevant to monitoring a multi-tiered application and diagnosing underlying causes of operational states of the multi-tiered application. The analysis subsystem develops one or more conditional probability distributions with respect to the subset of metrics. These one or more conditional probability distributions, in turn, allow the analysis subsystem to provide useful information for analysis of the causes of failures and undesirable system states associated with the multi-tiered application.

8. 发明申请

US20170353345A1 METHODS AND SYSTEMS TO DIAGNOSE ANOMALIES IN CLOUD INFRASTRUCTURES 审中-公开
公开(公告)号：US20170353345A1
公开(公告)日：2017-12-07
申请号：US15172616
申请日：2016-06-03
申请人： VMware, Inc.
发明人： Naira Movses Grigoryan , Arnak Poghosyan , Ashot Nshan Harutyunyan , Mazda A. Marvasti
IPC分类号： H04L12/24 , G06F9/455 , H04L29/08
CPC分类号： H04L41/064 , H04L41/0677 , H04L41/142 , H04L67/10
摘要： 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.

9. 发明授权

US09466031B1 Data-agnostic methods and systems for ranking and updating beliefs 有权
标题翻译：数据无关的方法和系统，用于排名和更新信念
公开(公告)号：US09466031B1
公开(公告)日：2016-10-11
申请号：US14104351
申请日：2013-12-12
申请人： VMware, Inc.
发明人： Ashot Nshan Harutyunyan , Naira Movses Grigoryan , Mazda A. Marvasti , Arnak Poghosyan , Yanislav Yankov
IPC分类号： G06N99/00 , G06N5/02
CPC分类号： G06N99/005 , G06N5/02 , G06N7/005 , G06Q30/00
摘要： 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.
摘要翻译：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。

10. 发明授权

US09298538B2 Methods and systems for abnormality analysis of streamed log data 有权
标题翻译：流式日志数据异常分析方法与系统
公开(公告)号：US09298538B2
公开(公告)日：2016-03-29
申请号：US13960611
申请日：2013-08-06
申请人： VMware, Inc.
发明人： Mazda A. Marvasti , Arnak Poghosyan , Ashot Harutyunyan , Naira Grigoryan
IPC分类号： G06F11/07
CPC分类号： G06F11/079 , G06F11/0706 , G06F11/0754 , G06F2201/86
摘要： 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.
摘要翻译：本公开提供了使用与日志数据相关联的元数据的统计结构来运行时分析用于异常的日志数据流的系统和方法。系统和方法将日志数据流转换为元数据并执行统计分析，以显示元数据中的统计统计模式。元数据被表示为具有表示每个不同事件类型的节点的图，该事件类型与流中与事件相关联的事件源一起检测。系统和方法使用实时分析来比较在操作窗口中收集的当前日志数据流的一部分与由图表表示的历史收集的元数据，以便确定当前日志数据流的异常程度操作窗口。

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