公开(公告)号：US09448928B2

公开(公告)日：2016-09-20

申请号：US14145720

申请日：2013-12-31

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Rafiul Ahad ,  Adriano Santos

IPC: G06F12/02 ,  G06F9/455

CPC classification number: G06F12/0253 ,  G06F9/45558 ,  G06F12/0276 ,  G06F2009/45583 ,  G06F2212/1016 ,  G06F2212/1044

Abstract: In accordance with an embodiment, described herein is a system and method for two-tier adaptive heap management (AHM) in a virtual machine environment, such as a Java virtual machine (JVM). In accordance with an embodiment, a two-tier AHM approach recognizes that more virtual machines can be run on a particular host, or the same number of virtual machines can support higher load while minimizing out-of-memory occurrences, swapping, and long old garbage collection pauses, if the heap is divided into tiers, so that a garbage collection policy that minimizes pause time can be used in a first (normal) tier, and a garbage collection policy that favors heap compaction and release of free memory to the host can be used in another (high-heap) tier.

Abstract translation: 根据本文所描述的实施例，在诸如Java虚拟机（JVM）的虚拟机环境中的用于两层自适应堆管理（AHM）的系统和方法。 根据实施例，两层AHM方法认识到可以在特定主机上运行更多的虚拟机，或者相同数量的虚拟机可以支持更高的负载，同时最小化内存不足的情况，交换和长老 垃圾收集暂停，如果堆分为层，以便可以在第一（正常）层中使用最小化暂停时间的垃圾收集策略，以及有利于堆压缩和释放可用内存到主机的垃圾收集策略 可以在另一个（高堆）层中使用。

公开(公告)号：US10205640B2

公开(公告)日：2019-02-12

申请号：US14109546

申请日：2013-12-17

Applicant: Oracle International Corporation

Inventor： Eric S. Chan ,  Rafiul Ahad ,  Adel Ghoneimy ,  Adriano Covello Santos

IPC: G06F17/30 ,  H04L12/24 ,  G06F9/48 ,  G06F11/00 ,  G06F11/07 ,  G06F11/34 ,  G06F11/30

Abstract: Data can be categorized into facts, information, hypothesis, and directives. Activities that generate certain categories of data based on other categories of data through the application of knowledge which can be categorized into classifications, assessments, resolutions, and enactments. Activities can be driven by a Classification-Assessment-Resolution-Enactment (CARE) control engine. The CARE control and these categorizations can be used to enhance a multitude of systems, for example diagnostic system, such as through historical record keeping, machine learning, and automation. Such a diagnostic system can include a system that forecasts computing system failures based on the application of knowledge to system vital signs such as thread or stack segment intensity and memory heap usage. These vital signs are facts that can be classified to produce information such as memory leaks, convoy effects, or other problems. Classification can involve the automatic generation of classes, states, observations, predictions, norms, objectives, and the processing of sample intervals having irregular durations.

公开(公告)号：US09692662B2

公开(公告)日：2017-06-27

申请号：US15275035

申请日：2016-09-23

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Eric S. Chan ,  Rafiul Ahad ,  Adel Ghoneimy ,  Adriano Covello Santos

IPC: G06F9/46 ,  H04L12/24 ,  G06F17/30 ,  G06F9/48 ,  G06F11/00 ,  G06F11/07 ,  G06F11/34 ,  G06F11/30

CPC classification number: H04L41/5009 ,  G06F9/4881 ,  G06F11/008 ,  G06F11/0715 ,  G06F11/0718 ,  G06F11/0751 ,  G06F11/0778 ,  G06F11/079 ,  G06F11/3003 ,  G06F11/3037 ,  G06F11/34 ,  G06F11/3409 ,  G06F11/3419 ,  G06F11/3452 ,  G06F11/3471 ,  G06F17/30289 ,  G06F17/30598 ,  G06F2201/865 ,  G06F2201/88

Abstract: Data can be categorized into facts, information, hypothesis, and directives. Activities that generate certain categories of data based on other categories of data through the application of knowledge which can be categorized into classifications, assessments, resolutions, and enactments. Activities can be driven by a Classification-Assessment-Resolution-Enactment (CARE) control engine. The CARE control and these categorizations can be used to enhance a multitude of systems, for example diagnostic system, such as through historical record keeping, machine learning, and automation. Such a diagnostic system can include a system that forecasts computing system failures based on the application of knowledge to system vital signs such as thread or stack segment intensity and memory heap usage. These vital signs are facts that can be classified to produce information such as memory leaks, convoy effects, or other problems. Classification can involve the automatic generation of classes, states, observations, predictions, norms, objectives, and the processing of sample intervals having irregular durations.

公开(公告)号：US20140324924A1

公开(公告)日：2014-10-30

申请号：US14145720

申请日：2013-12-31

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Rafiul Ahad ,  Adriano Santos

IPC: G06F12/02 ,  G06F9/455

CPC classification number: G06F12/0253 ,  G06F9/45558 ,  G06F12/0276 ,  G06F2009/45583 ,  G06F2212/1016 ,  G06F2212/1044

Abstract: In accordance with an embodiment, described herein is a system and method for two-tier adaptive heap management (AHM) in a virtual machine environment, such as a Java virtual machine (JVM). In accordance with an embodiment, a two-tier AHM approach recognizes that more virtual machines can be run on a particular host, or the same number of virtual machines can support higher load while minimizing out-of-memory occurrences, swapping, and long old garbage collection pauses, if the heap is divided into tiers, so that a garbage collection policy that minimizes pause time can be used in a first (normal) tier, and a garbage collection policy that favors heap compaction and release of free memory to the host can be used in another (high-heap) tier.

Abstract translation: 根据本文所描述的实施例，在诸如Java虚拟机（JVM）的虚拟机环境中的用于两层自适应堆管理（AHM）的系统和方法。 根据实施例，两层AHM方法认识到可以在特定主机上运行更多的虚拟机，或者相同数量的虚拟机可以支持更高的负载，同时最小化内存不足的情况，交换和长老 垃圾收集暂停，如果堆分为层，以便可以在第一（正常）层中使用最小化暂停时间的垃圾收集策略，以及有利于堆压缩和释放可用内存到主机的垃圾收集策略 可以在另一个（高堆）层中使用。

公开(公告)号：US20140310235A1

公开(公告)日：2014-10-16

申请号：US14109546

申请日：2013-12-17

Applicant: Oracle International Corporation

Inventor： Eric S. Chan ,  Rafiul Ahad ,  Adel Ghoneimy ,  Adriano Covello Santos

IPC: G06F17/30

CPC classification number: G06F17/30312 ,  G06F9/45533 ,  G06F9/4881 ,  G06F11/008 ,  G06F11/0715 ,  G06F11/0718 ,  G06F11/0751 ,  G06F11/0778 ,  G06F11/079 ,  G06F11/3003 ,  G06F11/3037 ,  G06F11/34 ,  G06F11/3409 ,  G06F11/3419 ,  G06F11/3452 ,  G06F11/3471 ,  G06F17/30197 ,  G06F17/30289 ,  G06F17/30598 ,  G06F2201/865 ,  G06F2201/88 ,  H04L41/5009

Abstract: Data can be categorized into facts, information, hypothesis, and directives. Activities that generate certain categories of data based on other categories of data through the application of knowledge which can be categorized into classifications, assessments, resolutions, and enactments. Activities can be driven by a Classification-Assessment-Resolution-Enactment (CARE) control engine. The CARE control and these categorizations can be used to enhance a multitude of systems, for example diagnostic system, such as through historical record keeping, machine learning, and automation. Such a diagnostic system can include a system that forecasts computing system failures based on the application of knowledge to system vital signs such as thread or stack segment intensity and memory heap usage. These vital signs are facts that can be classified to produce information such as memory leaks, convoy effects, or other problems. Classification can involve the automatic generation of classes, states, observations, predictions, norms, objectives, and the processing of sample intervals having irregular durations.

Abstract translation: 数据可以分为事实，信息，假设和指令。 通过应用可分类到分类，评估，决议和制定的知识，基于其他类别的数据生成某些类别的数据的活动。 活动可以通过分类评估 - 分配制度（CARE）控制引擎来驱动。 CARE控制和这些分类可用于增强大量系统，例如诊断系统，例如通过历史记录保存，机器学习和自动化。 这样的诊断系统可以包括基于将知识应用于诸如线程或堆栈段强度和内存堆使用的系统生命体征来预测计算系统故障的系统。 这些生命体征是可以分类以产生诸如记忆泄漏，车队效应或其他问题的信息的事实。 分类可以涉及自动生成类，状态，观察，预测，规范，目标以及具有不规则持续时间的采样间隔的处理。