18612224. DISCOVERY CRAWLER FOR APPLICATION DEPENDENCY DISCOVERY, REPORTING, AND MANAGEMENT TOOL simplified abstract (Capital One Services, LLC)
Contents
DISCOVERY CRAWLER FOR APPLICATION DEPENDENCY DISCOVERY, REPORTING, AND MANAGEMENT TOOL
Organization Name
Inventor(s)
Muralidharan Balasubramanian of Gaithersburg MD (US)
Eric K. Barnum of Midlothian VA (US)
Julie Dallen of Vienna VA (US)
David Watson of Alexandria VA (US)
DISCOVERY CRAWLER FOR APPLICATION DEPENDENCY DISCOVERY, REPORTING, AND MANAGEMENT TOOL - A simplified explanation of the abstract
This abstract first appeared for US patent application 18612224 titled 'DISCOVERY CRAWLER FOR APPLICATION DEPENDENCY DISCOVERY, REPORTING, AND MANAGEMENT TOOL
Abstract: 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.
- Simplified Explanation:
The patent application describes techniques for monitoring the operating statuses of an application and its dependencies using a monitoring application that collects and reports on their status without requiring modifications to the underlying applications.
- Key Features and Innovation:
- Monitoring application collects and reports operating status of monitored application and dependencies - No need for modification of underlying applications - Identification of root causes of unhealthy states - Automatic configuration and updating of monitoring application - Machine learning for performance pattern detection and health reports - Testing of application response through API call modifications
- Potential Applications:
- IT infrastructure monitoring - Application performance optimization - Root cause analysis in system failures - Automated configuration management
- Problems Solved:
- Difficulty in monitoring application and dependency statuses - Identifying root causes of unhealthy application states - Manual configuration and updating of monitoring systems - Lack of performance pattern detection
- Benefits:
- Improved application performance monitoring - Automated root cause analysis - Efficient configuration management - Enhanced system health reporting
- Commercial Applications:
Monitoring and optimization software for IT companies, cloud service providers, and application developers.
- Prior Art:
Prior art related to this technology may include existing monitoring tools and techniques in the field of IT infrastructure management and performance optimization.
- Frequently Updated Research:
Research on machine learning algorithms for performance pattern detection and automated configuration management in monitoring systems.
Questions about Monitoring Application Techniques:
1. How do these techniques improve the efficiency of monitoring applications? These techniques enhance the efficiency by automating the collection of operating statuses and identifying root causes of issues without requiring modifications to the underlying applications.
2. What role does machine learning play in determining patterns of performance in the monitored application? Machine learning is used to analyze system state information associated with performance events and provide health reports based on a baseline status, improving the accuracy of performance pattern detection.
Original Abstract Submitted
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.
