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1. 发明申请

US20220391279A1 MACHINE LEARNING METHODS AND SYSTEMS FOR DISCOVERING PROBLEM INCIDENTS IN A DISTRIBUTED COMPUTER SYSTEM 有权
公开(公告)号：US20220391279A1
公开(公告)日：2022-12-08
申请号：US17342423
申请日：2021-06-08
申请人： VMware, Inc
发明人： Naira Movses Grigoryan , Ashot Nshan Harutyunyan , Amak Poghosyan , Nicholas Kushmerick , Janislav Jankov
IPC分类号： G06F11/07 , G06N20/00 , G06N5/04 , G06F16/28
摘要： Methods and systems are directed to discovering problem incidents in a distributed computing system. Events corresponding to historical problems incidents for the distributed computing system are retrieved from a data base. Sets of representative events of the various historical problem incidents for the distributed computing system are determined. A runtime problem incident in the distributed computing system is characterized by runtime events. The runtime problem incident is classified as corresponding to a historical problem incident of the historical problem incidents based on the runtime events and the sets of representative events. Remedial measures used to correct the historical problem incident may be used to correct the runtime problem.

2. 发明申请

US20220283924A1 METHODS AND SYSTEMS FOR INTELLIGENT SAMPLING OF APPLICATION TRACES 有权
公开(公告)号：US20220283924A1
公开(公告)日：2022-09-08
申请号：US17367490
申请日：2021-07-05
申请人： VMware, Inc.
发明人： Arnak Poghosyan , Ashot Nshan Harutyunyan , Naira Movses Grigoryan , Clement Pang , George Oganesyan , Karen Avagyan
IPC分类号： G06F11/34 , G06F11/07
摘要： 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.

3. 发明申请

US20220027249A1 AUTOMATED METHODS AND SYSTEMS FOR TROUBLESHOOTING PROBLEMS IN A DISTRIBUTED COMPUTING SYSTEM 有权
公开(公告)号：US20220027249A1
公开(公告)日：2022-01-27
申请号：US16936565
申请日：2020-07-23
申请人： VMware, Inc.
发明人： Sunny Dua , Bonnie Zhang , Karen Aghajanyan , Hovhannes Antonyan , Ashot Nshan Harutyunyan , Arnak Poghosyan , Naira Movses Grigoryan
IPC分类号： G06F11/34 , G06F11/30 , G06F11/32 , G06F11/07 , G06K9/62
摘要： 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.

4. 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.

5. 发明申请

US20200341832A1 PROCESSES THAT DETERMINE STATES OF SYSTEMS OF A DISTRIBUTED COMPUTING SYSTEM 审中-公开
公开(公告)号：US20200341832A1
公开(公告)日：2020-10-29
申请号：US16391702
申请日：2019-04-23
申请人： VMware, Inc.
发明人： Arnak Poghosyan , Ashot Nshan Harutyunyan , Naira Movses Grigoryan
IPC分类号： G06F11/07 , G06F11/32 , G06F17/18 , G06K9/62
摘要： Automated processes and systems that determine a state of a complex computational system of a distributed computing system are described. The processes and systems determine outlier and normal metric values of metrics associated with a complex computational system. A total outlier metric is constructed based on the outlier and normal metric values of the metrics. Time stamps of outlier and normal total outlier metric values of the total outlier metric are labeled. Each time-stamp label identifies a normal or abnormal state of the complex computation system. One or more rules for classifying normal and abnormal states of the complex computational system are computed based on the time-stamp labels. The rules are applied to run-time metric values to determine a state of the complex computational system and generate an alert when the state is abnormal. The type of alert and corresponding abnormal state may be used to execute remedial measures.

6. 发明授权

US10394612B2 Methods and systems to evaluate data center performance and prioritize data center objects and anomalies for remedial actions 有权
公开(公告)号：US10394612B2
公开(公告)日：2019-08-27
申请号：US15190678
申请日：2016-06-23
申请人： VMware, Inc.
发明人： Naira Movses Grigoryan , Vahan Tadevosyan , Nina Karapetyan , Ashot Nshan Harutyunyan , Arnak Poghosyan
IPC分类号： G06F9/50
摘要： Methods and systems to evaluate data center performance and prioritize data center objects and anomalies for remedial actions are described. Methods rank data center objects and determine object performance trends. Methods calculate an object rank of each object of the data center over a period of time and calculate an object trend of each object of the data center based on relative frequencies of alerts at different times. The objects may be prioritized for remedial actions based on the object ranks and object trends.

7. 发明授权

US10275284B2 Datacenter resource allocation based on estimated capacity metric 有权
公开(公告)号：US10275284B2
公开(公告)日：2019-04-30
申请号：US15184742
申请日：2016-06-16
申请人： VMware, Inc.
发明人： Arnak Poghosyan , Ashot Nshan Harutyunyan , Naira Movses Grigoryan , Khachatur Nazaryan , Ruzan Hovhannisyan
IPC分类号： G06F9/50
摘要： 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.

8. 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.

9. 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。

10. 发明申请

US20220027257A1 Automated Methods and Systems for Managing Problem Instances of Applications in a Distributed Computing Facility 有权
公开(公告)号：US20220027257A1
公开(公告)日：2022-01-27
申请号：US17073381
申请日：2020-10-18
申请人： VMware, Inc.
发明人： Ashot Nshan Harutyunyan , Arnak Poghosyan , Sunny Dua , Naira Movses Grigoryan , Karen Aghajanyan
IPC分类号： G06F11/36 , G06F16/2457 , G06N20/00
摘要： 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.

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