Application Dependency Discovery Baseline Modeling
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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 status of monitored applications and their dependencies, using existing monitoring interfaces to build a logical dependency tree, and employs machine learning techniques to predict potential issues and generate corrective actions, while also simulating API problems to test system resilience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual troubleshooting processes are used with multiple systems and monitors, then system administrators can diagnose issues, but the incident recovery time increases and productivity decreases
Solution Approach 1:
The patent combines multiple monitoring systems, dependency graphs, and alerting mechanisms into a unified automated troubleshooting system. The system integrates data from various sources including application performance monitors, infrastructure monitors, and dependency relationship databases to automatically diagnose and resolve issues without requiring manual intervention across multiple separate tools.
Solution Approach 2:
The system performs preliminary actions by pre-building dependency graphs and establishing baseline performance metrics before issues occur. When problems arise, the system has already prepared the contextual information and relationship mappings needed for rapid diagnosis, eliminating the need for administrators to manually trace dependencies during incident response.
2Measurement precision
If system administrators manually contact dependency owners to troubleshoot issues, then root causes can be identified, but the troubleshooting process becomes slower and more complex
Solution Approach 1:
The system implements self-service troubleshooting by automatically analyzing dependency relationships and identifying root causes without requiring human administrators to manually contact other teams. The automated system uses pre-established dependency graphs to trace issues through the system architecture and automatically determine root causes, eliminating the need for time-consuming cross-team communication.
Solution Approach 2:
The system establishes continuous feedback loops where monitoring data automatically triggers analysis and resolution actions. When performance deviations are detected, the system feeds this information into the dependency analysis engine, which automatically correlates the issue with relevant dependencies and provides feedback on the root cause identification, creating a closed-loop automated troubleshooting process.
3Reliability
If comprehensive monitoring of all application dependencies is implemented, then system health can be better understood, but the device complexity and implementation difficulty increase
Solution Approach 1:
The patent segments the monitoring system into distinct modular components: dependency graph builders, performance metric collectors, anomaly detection engines, and root cause analysis modules. Each component handles a specific aspect of monitoring independently, allowing the system to scale complexity only where needed while keeping individual components simple and maintainable.
Solution Approach 2:
The system implements universal monitoring capabilities that can track multiple types of dependencies (application services, infrastructure components, third-party APIs) through a single unified framework. The dependency graph structure and analysis engine are designed to handle diverse dependency types uniformly, reducing the need for separate monitoring systems for different technology stacks or dependency categories.
4Ease of operation
If existing monitoring interfaces are leveraged without modification, then ease of operation improves, but the ability to correlate dependencies and provide automated analysis is limited
Solution Approach 1:
The system introduces an intermediary layer that sits between existing monitoring interfaces and the analysis engine. This intermediary automatically collects data from standard monitoring interfaces without requiring modifications to those interfaces, then enriches the data with dependency context from pre-built graphs, enabling automated correlation and analysis while maintaining compatibility with existing monitoring infrastructure.
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.