Application Dependency Crawler for Root Cause Diagnosis
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Solution Overview
Problem
Current monitoring systems face challenges in quickly diagnosing and addressing root causes of performance issues in complex applications due to their inability to effectively correlate and monitor application dependencies, leading to prolonged incident recovery times and increased system resiliency challenges.
Innovation Solution
A monitoring application that collects and reports operating statuses of monitored applications and their dependencies, using existing monitoring interfaces to build a logical dependency tree, and employs machine learning techniques to predict potential unhealthy states and generate corrective actions, while also simulating API problems to test system resilience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring systems are used to track application dependencies, then system coverage is maintained, but incident diagnosis time increases and recovery speed decreases
Solution Approach 1:
The system performs preliminary actions by proactively monitoring dependency health scores and predicting potential failures before they occur. The dependency graph is built and analyzed in advance, allowing the system to identify at-risk dependencies and take preventive measures, thus reducing incident recovery time when failures actually occur.
Solution Approach 2:
The system implements continuous feedback loops by monitoring dependency performance metrics, updating health scores in real-time, and using machine learning models to predict future states. This feedback mechanism enables the system to adapt and respond quickly to changing conditions, improving both reliability and response speed.
2Loss of information
If comprehensive dependency monitoring is implemented, then system visibility improves, but system complexity and computational overhead increase
Solution Approach 1:
The monitoring system is segmented into modular components: dependency graph builders, health score calculators, prediction models, and alerting systems. Each component handles specific tasks independently, making the overall complex system manageable and maintainable while preserving comprehensive dependency information.
Solution Approach 2:
The patent introduces intermediary elements such as dependency proxies and abstraction layers that simplify the monitoring of complex relationships. These intermediaries mediate between the complex dependency graph and the monitoring interfaces, reducing the perceived complexity while maintaining full visibility into dependency correlations.
3Productivity
If manual troubleshooting processes are used, then system resource consumption is low, but diagnostic accuracy and root cause identification speed decrease
Solution Approach 1:
The system applies partial action by focusing computational resources only on the most critical path of dependencies leading to the affected service. Instead of analyzing the entire dependency graph equally, it identifies and deeply analyzes only the relevant subset, improving diagnostic speed while controlling resource consumption.
Solution Approach 2:
The system dynamically changes monitoring parameters based on service criticality and current system state. For high-priority services, it increases monitoring frequency and detail, while using lighter monitoring for less critical services, thus optimizing the balance between diagnostic capability and resource usage.
Data Source
AI summary
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.