Root Cause Analysis Dashboard for Distributed Network Services
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Solution Overview
Problem
In complex networked environments, identifying the root cause of performance issues in web services is challenging due to the distributed nature of applications and the difficulty in tracking and monitoring across disparate systems, leading to prolonged mean time to repair and inadequate solutions.
Innovation Solution
A system and method for guided exploration and automated root cause analysis that includes a processor, memory, and modules to detect performance issues, receive and analyze various data types (metrics, events, logs, snapshots, configurations), and provide a dashboard interface for user input and correlation analysis to identify candidate root causes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual monitoring and tracking methods are used in distributed network environments, then system complexity is reduced, but the time to identify root causes increases significantly
Solution Approach 1:
The system performs preliminary actions by automatically collecting, storing, and organizing performance data, events, and configuration information from distributed entities before issues occur. This pre-processing of data enables rapid root cause analysis when performance issues arise, reducing mean time to repair without requiring manual monitoring setup.
Solution Approach 2:
The patent introduces an intermediary system comprising data collectors, storage mechanisms, and analysis components that mediate between distributed network entities and users. This intermediary automatically correlates performance data across multiple entities, eliminating the need for manual tracking while managing system complexity through automated intermediate processing layers.
2Measurement precision
If automated data collection and correlation analysis is implemented across distributed entities, then root cause identification accuracy improves, but system complexity increases
Solution Approach 1:
The monitoring system is segmented into distinct functional modules: data collectors deployed on individual entities, centralized storage components, and analysis engines. Each segment handles specific tasks independently, improving root cause identification accuracy through specialized processing while managing complexity through modular architecture.
Solution Approach 2:
The patent implements universal data collection and correlation mechanisms that can analyze multiple data types (performance metrics, events, configurations) across diverse distributed entities using the same framework. This multi-functional approach improves measurement precision by applying consistent analysis methods while reducing overall system complexity through reuse of common components.
3Reliability
If comprehensive performance data from multiple data types is collected and analyzed, then diagnostic accuracy improves, but the complexity of data processing increases
Solution Approach 1:
The system merges multiple data types (performance data, events, configuration information) into a unified analysis framework. By combining these diverse data sources and correlating them through integrated processing, the system improves diagnostic accuracy while managing processing complexity through unified data structures and correlation algorithms.
Solution Approach 2:
The correlation analysis system implements feedback mechanisms where analysis results inform subsequent data collection and processing priorities. This feedback loop improves diagnostic accuracy by focusing processing resources on relevant data while reducing overall processing complexity through adaptive, priority-based data handling.
Data Source
AI summary
In one aspect, a system for identifying a root cause of a performance issue in a monitored entity is disclosed. The system can detect a performance issue with the monitored entity running in a monitored environment of networked entities; receive a plurality of datatypes and associated data for each entity, the plurality of datatypes include metrics, events, logs, snapshots, and configurations; provide a dashboard user interface to display the datatypes and associated data for each entity as user selectable items; receive user input through the dashboard user interface that indicate a selection of two of the datatypes for performing correlation analysis; perform the correlation analysis using the received user selection of the two of the datatypes; identify a candidate root cause of the performance issue based on the correlation analysis; and display the identified candidate root cause through the dashboard user interface.