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1.发明公开
公开(公告)号：EP3991030A1
公开(公告)日：2022-05-04
申请号：EP20832144.8
申请日：2020-06-26
IPC分类号： G06F9/38 , G06F9/445 , G06F9/54 , G06F16/903 , G06N20/00
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2.发明公开
公开(公告)号：EP4242849A3
公开(公告)日：2023-11-29
申请号：EP23184633.8
申请日：2020-06-26
摘要： Techniques for monitoring operating statuses of an application and its dependencies are provided. A monitoring application may collect and report the operating status of the monitored application and each dependency. Through use of existing monitoring interfaces, the monitoring application can collect operating status without requiring modification of the underlying monitored application or dependencies. The monitoring application may determine a problem service that is a root cause of an unhealthy state of the monitored application. Dependency analyzer and discovery crawler techniques may automatically configure and update the monitoring application. Machine learning techniques may be used to determine patterns of performance based on system state information associated with performance events and provide health reports relative to a baseline status of the monitored application. Also provided are techniques for testing a response of the monitored application through modifications to API calls. Such tests may be used to train the machine learning model.
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3.发明公开
公开(公告)号：EP4242850A3
公开(公告)日：2024-04-17
申请号：EP23184645.2
申请日：2020-06-26
CPC分类号： G06N20/00 , G06F16/903 , G06F16/9027 , G06F16/2246 , G06F11/3409 , G06F11/3055 , G06F11/3006 , G06F11/079 , G06F11/0793 , G06F11/3684 , G06F11/008 , G06F11/3688
摘要： Techniques for monitoring operating statuses of an application and its dependencies are provided. A monitoring application may collect and report the operating status of the monitored application and each dependency. Through use of existing monitoring interfaces, the monitoring application can collect operating status without requiring modification of the underlying monitored application or dependencies. The monitoring application may determine a problem service that is a root cause of an unhealthy state of the monitored application. Dependency analyzer and discovery crawler techniques may automatically configure and update the monitoring application. Machine learning techniques may be used to determine patterns of performance based on system state information associated with performance events and provide health reports relative to a baseline status of the monitored application. Also provided are techniques for testing a response of the monitored application through modifications to API calls. Such tests may be used to train the machine learning model.
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4.发明公开
公开(公告)号：EP4242850A2
公开(公告)日：2023-09-13
申请号：EP23184645.2
申请日：2020-06-26
IPC分类号： G06F11/30
摘要： Techniques for monitoring operating statuses of an application and its dependencies are provided. A monitoring application may collect and report the operating status of the monitored application and each dependency. Through use of existing monitoring interfaces, the monitoring application can collect operating status without requiring modification of the underlying monitored application or dependencies. The monitoring application may determine a problem service that is a root cause of an unhealthy state of the monitored application. Dependency analyzer and discovery crawler techniques may automatically configure and update the monitoring application. Machine learning techniques may be used to determine patterns of performance based on system state information associated with performance events and provide health reports relative to a baseline status of the monitored application. Also provided are techniques for testing a response of the monitored application through modifications to API calls. Such tests may be used to train the machine learning model.
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5.发明公开
公开(公告)号：EP4242849A2
公开(公告)日：2023-09-13
申请号：EP23184633.8
申请日：2020-06-26
IPC分类号： G06F11/30
摘要： Techniques for monitoring operating statuses of an application and its dependencies are provided. A monitoring application may collect and report the operating status of the monitored application and each dependency. Through use of existing monitoring interfaces, the monitoring application can collect operating status without requiring modification of the underlying monitored application or dependencies. The monitoring application may determine a problem service that is a root cause of an unhealthy state of the monitored application. Dependency analyzer and discovery crawler techniques may automatically configure and update the monitoring application. Machine learning techniques may be used to determine patterns of performance based on system state information associated with performance events and provide health reports relative to a baseline status of the monitored application. Also provided are techniques for testing a response of the monitored application through modifications to API calls. Such tests may be used to train the machine learning model.
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