Intent-Driven Root Cause Analysis for Computer Networks
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Solution Overview
Problem
Complex computer networks pose challenges in root cause analysis due to dynamic symptoms, diverse topologies, and interconnected elements, making it difficult to identify and fix underlying issues efficiently.
Innovation Solution
A method involving the generation of fault and symptom representations using graph models and behavior specifications, which are then analyzed to determine root causes, allowing for automated configuration and management of network elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual fault analysis methods are used in complex computer networks, then analysis process is simple to implement, but analysis efficiency is low and root cause identification is difficult
Solution Approach 1:
The patent segments the complex network analysis problem into distinct components: symptom data collection from multiple sources, fault model representation with structured relationships, and root cause analysis engine that processes symptoms against known fault patterns. This segmentation allows each component to be optimized independently, improving overall analysis efficiency despite network complexity.
Solution Approach 2:
The patent introduces a fault model representation as an intermediary layer between raw symptom data and root cause determination. This fault model acts as a mediator that structures and contextualizes symptoms, enabling more efficient analysis by translating complex network states into analyzable fault patterns without requiring direct complex network analysis.
2Measurement precision
If comprehensive symptom data is collected from all network elements, then root cause identification accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent extracts only the relevant symptom data needed for fault analysis from the comprehensive network data. The symptom data collection is targeted toward elements that indicate faults, and the fault model representation extracts key relationships and patterns from the data, filtering out unnecessary information to maintain accuracy while reducing processing complexity.
Solution Approach 2:
The patent applies different processing approaches to different parts of the data based on their relevance. Critical symptom data from key network elements receives more detailed analysis through the fault model, while less critical data is processed more efficiently. This local quality approach ensures high accuracy for root cause identification while optimizing overall data processing complexity.
3Reliability
If dynamic network changes are monitored continuously, then fault detection capability is improved, but system resource consumption increases
Solution Approach 1:
The patent implements periodic monitoring and analysis cycles rather than continuous real-time processing. The system collects symptom data, updates the fault model representation, and performs root cause analysis at structured intervals. This periodic approach maintains reliable fault detection capability while significantly reducing system resource consumption compared to continuous monitoring.
Solution Approach 2:
The fault model representation is designed to be self-updating based on incoming symptom data. Once established, the fault model can autonomously process new symptoms and identify root causes without requiring intensive external processing resources. This self-service capability maintains high fault detection reliability while minimizing ongoing system resource consumption.
Data Source
AI summary
A fault model representation of a computer network is generated, wherein the computer network includes a set of connected computer network elements that was at least in part configured based on a specified declarative intent in forming the computer network. A symptom representation for the computer network is determined based on telemetry data of one or more elements of the set of connected computer network elements and a behavior specification repository identifying symptoms and their associated root causes. The fault model representation and the symptom representation are provided to a root cause analysis to determine one or more root causes of one or more detected symptoms of the computer network.