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公开(公告)号：US20160371181A1
公开(公告)日：2016-12-22
申请号：US14743817
申请日：2015-06-18
Applicant: Oracle International Corporation
Inventor： Dustin R. Garvey , Sampanna S. Salunke , Lik Wong , Xuemei Gao , Yongqiang Zhang , Eric S. Chan , Kenny C. Gross
CPC classification number: G06F12/0253 , G06F3/0619 , G06F3/0653 , G06F3/0671
Abstract: The disclosed embodiments provide a system that detects anomalous events in a virtual machine. During operation, the system obtains time-series garbage-collection (GC) data collected during execution of a virtual machine in a computer system. Next, the system generates one or more seasonal features from the time-series GC data. The system then uses a sequential-analysis technique to analyze the time-series GC data and the one or more seasonal features for an anomaly in the GC activity of the virtual machine. Finally, the system stores an indication of a potential out-of-memory (OOM) event for the virtual machine based at least in part on identifying the anomaly in the GC activity of the virtual machine.
Abstract translation: 所公开的实施例提供了一种检测虚拟机中的异常事件的系统。 在运行期间，系统获取计算机系统中虚拟机执行期间收集的时间序列垃圾收集（GC）数据。 接下来，系统从时间序列GC数据生成一个或多个季节特征。 然后，系统使用顺序分析技术来分析时间序列GC数据以及虚拟机的GC活动中的异常的一个或多个季节特征。 最后，系统至少部分地基于识别虚拟机的GC活动中的异常来存储针对虚拟机的潜在的内存不足（OOM）事件的指示。
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公开(公告)号：US09600394B2
公开(公告)日：2017-03-21
申请号：US14743847
申请日：2015-06-18
Applicant: Oracle International Corporation
Inventor： Sampanna S. Salunke , Dustin R. Garvey , Lik Wong , Kenny C. Gross
CPC classification number: G06F11/3612 , G06F11/0712 , G06F11/0754 , G06F11/3452 , G06F11/3466 , G06F11/3608
Abstract: The disclosed embodiments provide a system that detects anomalous events. During operation, the system obtains machine-generated time-series performance data collected during execution of a software program in a computer system. Next, the system removes a subset of the machine-generated time-series performance data within an interval around one or more known anomalous events of the software program to generate filtered time-series performance data. The system uses the filtered time-series performance data to build a statistical model of normal behavior in the software program and obtains a number of unique patterns learned by the statistical model. When the number of unique patterns satisfies a complexity threshold, the system applies the statistical model to subsequent machine-generated time-series performance data from the software program to identify an anomaly in an activity of the software program and stores an indication of the anomaly for the software program upon identifying the anomaly.
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公开(公告)号：US20160371170A1
公开(公告)日：2016-12-22
申请号：US14743847
申请日：2015-06-18
Applicant: ORACLE INTERNATIONAL CORPORATION
Inventor： Sampanna S. Salunke , Dustin R. Garvey , Lik Wong , Kenny C. Gross
IPC: G06F11/36
CPC classification number: G06F11/3612 , G06F11/0712 , G06F11/0754 , G06F11/3452 , G06F11/3466 , G06F11/3608
Abstract: The disclosed embodiments provide a system that detects anomalous events. During operation, the system obtains machine-generated time-series performance data collected during execution of a software program in a computer system. Next, the system removes a subset of the machine-generated time-series performance data within an interval around one or more known anomalous events of the software program to generate filtered time-series performance data. The system uses the filtered time-series performance data to build a statistical model of normal behavior in the software program and obtains a number of unique patterns learned by the statistical model. When the number of unique patterns satisfies a complexity threshold, the system applies the statistical model to subsequent machine-generated time-series performance data from the software program to identify an anomaly in an activity of the software program and stores an indication of the anomaly for the software program upon identifying the anomaly.
Abstract translation: 所公开的实施例提供了一种检测异常事件的系统。 在运行期间，系统在计算机系统中获取在执行软件程序期间收集的机器生成的时间序列性能数据。 接下来，系统在围绕软件程序的一个或多个已知异常事件的间隔内去除机器生成的时间序列性能数据的子集，以生成经过滤的时间序列性能数据。 系统使用过滤的时间序列性能数据构建软件程序中正常行为的统计模型，并获得由统计模型学习的许多独特模式。 当唯一模式的数量满足复杂度阈值时，系统将统计模型应用于来自软件程序的后续机器生成的时间序列性能数据，以识别软件程序的活动中的异常，并存储针对 软件程序在识别异常时。
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公开(公告)号：US10248561B2
公开(公告)日：2019-04-02
申请号：US14743817
申请日：2015-06-18
Applicant: Oracle International Corporation
Inventor： Dustin R. Garvey , Sampanna S. Salunke , Lik Wong , Xuemei Gao , Yongqiang Zhang , Eric S. Chan , Kenny C. Gross
Abstract: The disclosed embodiments provide a system that detects anomalous events in a virtual machine. During operation, the system obtains time-series garbage-collection (GC) data collected during execution of a virtual machine in a computer system. Next, the system generates one or more seasonal features from the time-series GC data. The system then uses a sequential-analysis technique to analyze the time-series GC data and the one or more seasonal features for an anomaly in the GC activity of the virtual machine. Finally, the system stores an indication of a potential out-of-memory (OOM) event for the virtual machine based at least in part on identifying the anomaly in the GC activity of the virtual machine.
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