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

IPC: G06F12/02 ,  G06F3/06

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。

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

IPC: G06F17/30 ,  G06F12/02 ,  G06F3/06

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.