公开(公告)号：US20160274966A1

公开(公告)日：2016-09-22

申请号：US14659065

申请日：2015-03-16

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Dustin R. Garvey ,  Kenny C. Gross ,  Kalyanaraman Vaidyanathan ,  Debabrata Sarkar

IPC: G06F11/07

CPC classification number: G06F11/079 ,  G06F11/0706 ,  G06F11/0757 ,  G06F11/3058

Abstract: The disclosed embodiments provide a system that analyzes telemetry data from a computer system. During operation, the system obtains the telemetry data, which includes first information containing telemetric signals gathered using sensors in the computer system and second information that indicates one or more transaction latencies of software running on the computer system. Upon detecting an upward trend in the one or more transaction latencies, the system analyzes the telemetry data for a correlation between the one or more transaction latencies and one or more environmental factors represented by a subset of the telemetric signals. Upon identifying the correlation between the one or more transaction latencies and an environmental factor, the system stores an indication that the environmental factor may be contributing to the upward trend in the one or more transaction latencies.

Abstract translation: 所公开的实施例提供了一种从计算机系统分析遥测数据的系统。 在操作期间，系统获得遥测数据，其包括包含在计算机系统中使用传感器收集的遥测信号的第一信息和指示计算机系统上运行的软件的一个或多个事务延迟的第二信息。 在检测到一个或多个事务延迟中的向上趋势时，系统分析遥测数据以获得一个或多个事务延迟与由遥测信号的子集表示的一个或多个环境因素之间的相关性。 在识别一个或多个事务延迟与环境因素之间的相关性时，系统存储环境因素可能有助于一个或多个事务延迟中的上升趋势的指示。

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：US09645875B2

公开(公告)日：2017-05-09

申请号：US14659065

申请日：2015-03-16

Applicant: Oracle International Corporation

Inventor： Dustin R. Garvey ,  Kenny C. Gross ,  Kalyanaraman Vaidyanathan ,  Debabrata Sarkar

IPC: G06F11/00 ,  G06F11/07 ,  G06F11/30

CPC classification number: G06F11/079 ,  G06F11/0706 ,  G06F11/0757 ,  G06F11/3058

Abstract: The disclosed embodiments provide a system that analyzes telemetry data from a computer system. During operation, the system obtains the telemetry data, which includes first information containing telemetric signals gathered using sensors in the computer system and second information that indicates one or more transaction latencies of software running on the computer system. Upon detecting an upward trend in the one or more transaction latencies, the system analyzes the telemetry data for a correlation between the one or more transaction latencies and one or more environmental factors represented by a subset of the telemetric signals. Upon identifying the correlation between the one or more transaction latencies and an environmental factor, the system stores an indication that the environmental factor may be contributing to the upward trend in the one or more transaction latencies.

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。

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.

公开(公告)号：US09929922B2

公开(公告)日：2018-03-27

申请号：US15180812

申请日：2016-06-13

Applicant: Oracle International Corporation

Inventor： Kenny C. Gross ,  Kalyanaraman Vaidyanathan ,  Dustin R. Garvey ,  Lik Wong

IPC: G06F11/30 ,  H04L12/26 ,  G06F11/07

CPC classification number: G06F11/3476 ,  G06F11/008 ,  G06F11/263 ,  G06F11/3058 ,  G06F17/40

Abstract: The disclosed embodiments relate to a system that gathers telemetry data while testing a computer system. During operation, the system obtains a test script that generates a load profile to exercise the computer system, wherein a running time of the test script is designed to be relatively prime in comparison to a sampling interval for telemetry data in the computer system. Next, the system gathers telemetry data during multiple successive executions of the test script on the computer system. The system merges the telemetry data gathered during the multiple successive executions of the test script, wherein the relatively prime relationship between the running time of the test script and the sampling interval for the telemetry data causes a sampling point for the telemetry data to precess through different points in the test script during the multiple successive executions of the test script, thereby densifying sampled telemetry data points gathered for the test script. Finally, the system outputs the densified telemetry data.

公开(公告)号：US09720823B2

公开(公告)日：2017-08-01

申请号：US14743805

申请日：2015-06-18

Applicant: Oracle International Corporation

Inventor： Aleksey M. Urmanov ,  Dustin R. Garvey ,  Lik Wong

IPC: G06F11/00 ,  G06F12/02 ,  G06F9/455 ,  G06F11/30 ,  G06F11/34 ,  G06F11/14

CPC classification number: G06F12/0253 ,  G06F9/45558 ,  G06F11/1484 ,  G06F11/301 ,  G06F11/3423 ,  G06F2009/45583 ,  G06F2212/702

Abstract: The disclosed embodiments provide a system that detects anomalous events in a virtual machine. During operation, the system obtains time-series virtual machine (VM) data including garbage-collection (GC) data collected during execution of a virtual machine in a computer system. Next, the system computes, by a service processor, a time window for analyzing the time-series VM data based at least in part on a working time scale of high-activity patterns in the time-series GC data. The system then uses a trend-estimation technique to analyze the time-series VM data within the time window to determine an out-of-memory (OOM) risk in the virtual machine. Finally, the system stores an indication of the OOM risk for the virtual machine based at least in part on determining the OOM risk in the virtual machine.

公开(公告)号：US20170359234A1

公开(公告)日：2017-12-14

申请号：US15180812

申请日：2016-06-13

Applicant: Oracle International Corporation

Inventor： Kenny C. Gross ,  Kalyanaraman Vaidyanathan ,  Dustin R. Garvey ,  Lik Wong

IPC: H04L12/26 ,  G06F11/07

CPC classification number: G06F11/3476 ,  G06F11/008 ,  G06F11/263 ,  G06F11/3058 ,  G06F17/40

Abstract: The disclosed embodiments relate to a system that gathers telemetry data while testing a computer system. During operation, the system obtains a test script that generates a load profile to exercise the computer system, wherein a running time of the test script is designed to be relatively prime in comparison to a sampling interval for telemetry data in the computer system. Next, the system gathers telemetry data during multiple successive executions of the test script on the computer system. The system merges the telemetry data gathered during the multiple successive executions of the test script, wherein the relatively prime relationship between the running time of the test script and the sampling interval for the telemetry data causes a sampling point for the telemetry data to precess through different points in the test script during the multiple successive executions of the test script, thereby densifying sampled telemetry data points gathered for the test script. Finally, the system outputs the densified telemetry data.

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

IPC: G06F11/00 ,  G06F11/36 ,  G06F11/34

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.

公开(公告)号：US20160371180A1

公开(公告)日：2016-12-22

申请号：US14743805

申请日：2015-06-18

Applicant: Oracle International Corporation

Inventor： Aleksey M. Urmanov ,  Dustin R. Garvey ,  Lik Wong

IPC: G06F12/02 ,  G06F11/14 ,  G06F11/34 ,  G06F9/455 ,  G06F11/30

CPC classification number: G06F12/0253 ,  G06F9/45558 ,  G06F11/1484 ,  G06F11/301 ,  G06F11/3423 ,  G06F2009/45583 ,  G06F2212/702

Abstract: The disclosed embodiments provide a system that detects anomalous events in a virtual machine. During operation, the system obtains time-series virtual machine (VM) data including garbage-collection (GC) data collected during execution of a virtual machine in a computer system. Next, the system computes, by a service processor, a time window for analyzing the time-series VM data based at least in part on a working time scale of high-activity patterns in the time-series GC data. The system then uses a trend-estimation technique to analyze the time-series VM data within the time window to determine an out-of-memory (OOM) risk in the virtual machine. Finally, the system stores an indication of the OOM risk for the virtual machine based at least in part on determining the OOM risk in 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，系统获得包括计算机系统中虚拟机执行期间收集的垃圾收集（GC）数据的时间序列虚拟机（VM）数据。 接下来，该系统由服务处理器至少部分地基于时间序列GC数据中的高活动模式的工作时间尺度来计算用于分析时间序列VM数据的时间窗口。 然后，系统使用趋势估计技术来分析时间窗口内的时间序列VM数据，以确定虚拟机中的内存不足（OOM）风险。 最后，系统至少部分地基于确定虚拟机中的OOM风险来存储针对虚拟机的OOM风险的指示。