公开(公告)号：US10853161B2

公开(公告)日：2020-12-01

申请号：US16050429

申请日：2018-07-31

Applicant: Oracle International Corporation

Inventor： Rafiul Ahad

IPC: G06F11/00 ,  G06F11/07 ,  H04L12/24 ,  H04L29/08 ,  G06F11/34 ,  G06F11/30

Abstract: An anomaly detection and resolution system (ADRS) is disclosed for automatically detecting and resolving anomalies in computing environments. The ADRS may be implemented using an anomaly classification system defining different types of anomalies (e.g., a defined anomaly and an undefined anomaly). A defined anomaly may be based on bounds (fixed or seasonal) on any metric to be monitored. An anomaly detection and resolution component (ADRC) may be implemented in each component defining a service in a computing system. An ADRC may be configured to detect and attempt to resolve an anomaly locally. If the anomaly event for an anomaly can be resolved in the component, the ADRC may communicate the anomaly event to an ADRC of a parent component, if one exists. Each ADRC in a component may be configured to locally handle specific types of anomalies to reduce communication time and resource usage for resolving anomalies.

公开(公告)号：US20190042353A1

公开(公告)日：2019-02-07

申请号：US16050429

申请日：2018-07-31

Applicant: Oracle International Corporation

Inventor： Rafiul Ahad

IPC: G06F11/07 ,  H04L29/08 ,  H04L12/24 ,  G06F11/34 ,  G06F11/30

Abstract: An anomaly detection and resolution system (ADRS) is disclosed for automatically detecting and resolving anomalies in computing environments. The ADRS may be implemented using an anomaly classification system defining different types of anomalies (e.g., a defined anomaly and an undefined anomaly). A defined anomaly may be based on bounds (fixed or seasonal) on any metric to be monitored. An anomaly detection and resolution component (ADRC) may be implemented in each component defining a service in a computing system. An ADRC may be configured to detect and attempt to resolve an anomaly locally. If the anomaly event for an anomaly can be resolved in the component, the ADRC may communicate the anomaly event to an ADRC of a parent component, if one exists. Each ADRC in a component may be configured to locally handle specific types of anomalies to reduce communication time and resource usage for resolving anomalies.

公开(公告)号：US10042697B2

公开(公告)日：2018-08-07

申请号：US15165298

申请日：2016-05-26

Applicant: Oracle International Corporation

Inventor： Rafiul Ahad

IPC: G06F11/00 ,  G06F11/07 ,  H04L12/24 ,  H04L29/08 ,  G06F11/34 ,  G06F11/30

Abstract: An anomaly detection and resolution system (ADRS) is disclosed for automatically detecting and resolving anomalies in computing environments. The ADRS may be implemented using an anomaly classification system defining different types of anomalies (e.g., a defined anomaly and an undefined anomaly). A defined anomaly may be based on bounds (fixed or seasonal) on any metric to be monitored. An anomaly detection and resolution component (ADRC) may be implemented in each component defining a service in a computing system. An ADRC may be configured to detect and attempt to resolve an anomaly locally. If the anomaly event for an anomaly can be resolved in the component, the ADRC may communicate the anomaly event to an ADRC of a parent component, if one exists. Each ADRC in a component may be configured to locally handle specific types of anomalies to reduce communication time and resource usage for resolving anomalies.

公开(公告)号：US10942791B2

公开(公告)日：2021-03-09

申请号：US16132706

申请日：2018-09-17

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Rafiul Ahad

IPC: G06F15/16 ,  G06F9/50 ,  H04L29/08 ,  G06F9/54

Abstract: Systems, methods, and other embodiments that manage load in request processing environments are described. In one embodiment, a method includes receiving, at a backend of a request processing environment, requests transmitted by frontends. The backend is controlled to process the requests to create responses that are transmitted back to the frontends. Load of the backend processing the requests is monitored. In response to the load exceeding a threshold, a retry interval is calculated as a function of the load. In response to receiving a subsequent request from a frontend, a command is transmitted to the frontend. The command modifies operation of the frontend to wait the retry interval before re-transmitting the subsequent request as a retry request and to avoid generating an error message.

公开(公告)号：US20170012834A1

公开(公告)日：2017-01-12

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: H04L12/24 ,  G06F17/30 ,  G06F9/48

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.

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。

公开(公告)号：US20150234869A1

公开(公告)日：2015-08-20

申请号：US14705304

申请日：2015-05-06

Applicant: Oracle International Corporation

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

IPC: G06F17/30 ,  G06F9/455

CPC classification number: H04L41/5009 ,  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 ,  G06F16/1824 ,  G06F16/21 ,  G06F16/22 ,  G06F16/285 ,  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.

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。

公开(公告)号：US10333798B2

公开(公告)日：2019-06-25

申请号：US14705304

申请日：2015-05-06

Applicant: Oracle International Corporation

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

IPC: G06F11/00 ,  H04L12/24 ,  G06F16/21 ,  G06F16/22 ,  G06F16/28 ,  G06F16/182 ,  G06F9/48 ,  G06F11/07 ,  G06F11/34 ,  G06F11/30 ,  G06F9/455

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：US09495395B2

公开(公告)日：2016-11-15

申请号：US14109578

申请日：2013-12-17

Applicant: Oracle International Corporation

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

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

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.

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。

公开(公告)号：US20140310714A1

公开(公告)日：2014-10-16

申请号：US14109578

申请日：2013-12-17

Applicant: Oracle International Corporation

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

IPC: G06F9/48

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.

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。

公开(公告)号：US20160350173A1

公开(公告)日：2016-12-01

申请号：US15165298

申请日：2016-05-26

Applicant: Oracle International Corporation

Inventor： Rafiul Ahad

IPC: G06F11/07 ,  H04L29/08 ,  H04L12/24

CPC classification number: G06F11/0793 ,  G06F11/0709 ,  G06F11/0751 ,  G06F11/079 ,  G06F11/301 ,  G06F11/3048 ,  G06F11/3072 ,  G06F11/3409 ,  G06F11/3495 ,  H04L41/5003 ,  H04L41/5038 ,  H04L67/02

Abstract: An anomaly detection and resolution system (ADRS) is disclosed for automatically detecting and resolving anomalies in computing environments. The ADRS may be implemented using an anomaly classification system defining different types of anomalies (e.g., a defined anomaly and an undefined anomaly). A defined anomaly may be based on bounds (fixed or seasonal) on any metric to be monitored. An anomaly detection and resolution component (ADRC) may be implemented in each component defining a service in a computing system. An ADRC may be configured to detect and attempt to resolve an anomaly locally. If the anomaly event for an anomaly can be resolved in the component, the ADRC may communicate the anomaly event to an ADRC of a parent component, if one exists. Each ADRC in a component may be configured to locally handle specific types of anomalies to reduce communication time and resource usage for resolving anomalies.

Abstract translation: 公开了一种异常检测和解决系统（ADRS），用于自动检测和解决计算环境中的异常。 可以使用定义不同类型的异常（例如，定义的异常和未定义的异常）的异常分类系统来实现ADRS。 定义的异常可以基于要监视的任何度量的边界（固定或季节性）。 可以在定义计算系统中的服务的每个组件中实现异常检测和解决部件（ADRC）。 ADRC可能被配置为检测并尝试在本地解决异常。 如果异常异常事件可以在组件中解决，则ADRC可以将异常事件传达给父组件的ADRC（如果存在）。 组件中的每个ADRC可以被配置为本地处理特定类型的异常，以减少通信时间和资源使用以解决异常。