Monitoring Application for Dependency Correlation and Root Cause Analysis
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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 the 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, thereby reducing the need for manual tracing and administrative effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tracing and monitoring of application dependencies is performed using existing systems, then system administrators can identify performance issues, but the process is time-consuming and requires significant administrative effort
Solution Approach 1:
The system performs preliminary actions by automatically discovering, mapping, and storing application dependency relationships before incidents occur. A dependency graph is constructed and maintained in advance, enabling rapid incident analysis without manual tracing when problems arise.
Solution Approach 2:
A new monitoring application serves as an intermediary layer between existing monitoring systems and system administrators. This intermediary automatically collects data from multiple existing monitoring interfaces, correlates dependencies, and presents unified insights, eliminating the need for administrators to manually aggregate information from multiple sources.
2Quantity of substance
If multiple existing monitoring systems are used to track application dependencies, then comprehensive monitoring data can be collected, but the systems cannot effectively correlate dependencies across different sources
Solution Approach 1:
The system merges data from multiple existing monitoring interfaces by collecting metrics, logs, and events from different monitoring sources and correlating them within a unified dependency graph framework. This integration preserves information from all sources while adding the crucial layer of dependency relationships that connect them.
Solution Approach 2:
The monitoring application provides universal functionality by serving multiple purposes: it discovers dependencies, builds dependency graphs, collects data from various monitoring sources, correlates information across sources, and enables incident analysis. This multi-functional approach replaces the need for multiple separate manual processes.
3Measurement precision
If system administrators manually differentiate root cause from impact chain in complex dependency systems, then accurate problem diagnosis can be achieved, but the process requires extensive effort and specialized knowledge
Solution Approach 1:
The system implements feedback by continuously monitoring application and dependency states, comparing current performance against the established dependency graph, and automatically identifying when deviations occur. This feedback loop enables automatic root cause identification by tracing impacts back through the dependency relationships without requiring manual analysis.
Solution Approach 2:
The monitoring system performs self-service by automatically discovering dependencies, building the dependency graph, collecting monitoring data, correlating information, and identifying root causes without requiring system administrator intervention. The system serves itself by maintaining its own dependency knowledge base and using it to autonomously diagnose issues.
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.