Network Behavior Model for Root Cause Analysis

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Solution Overview

Problem

Current network monitoring and alerting systems struggle to effectively correlate alerts and determine root causes of network problems due to the complexity and distributive nature of networks, leading to manual and time-consuming root cause analysis.

Innovation Solution

A network behavior model that learns and updates with network states and events to correlate network problems and determine root causes, enabling automated alerting and correction processes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If alerts are correlated based on prior static knowledge, then the correlation process is simple, but the accuracy of root cause determination deteriorates due to network complexity and dynamic behavior

Engineering Contradiction:

Improveease of alert correlationVSAvoidaccuracy of root cause determination

Core Design Contradiction:

Ease of manufactureVSMeasurement precision

Solution Approach 1:

The patent transforms static alert correlation rules into a dynamic state machine model that continuously learns and adapts to network behavior. The system transitions between states based on observed network events, enabling accurate root cause determination in complex dynamic networks while maintaining automated processing.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The state machine model automatically learns network behavior patterns from observed events without requiring manual configuration or expert intervention. The system self-updates its understanding of network dynamics, enabling accurate root cause analysis while reducing operational complexity.

Inventive Principle:

Principle #25Self-service

2Reliability

If manual root cause analysis is performed, then thorough investigation is possible, but time and effort consumption increases significantly

Engineering Contradiction:

Improvethoroughness of analysisVSAvoidtime for root cause analysis

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical analysis processes with an automated state machine system that processes network events algorithmically. The system maintains thorough analysis by tracking state transitions and identifying root causes through systematic evaluation of event sequences, while eliminating time-consuming manual intervention.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system continuously monitors network events and uses feedback from observed state transitions to automatically update its understanding of network behavior. This enables the automated system to perform thorough root cause analysis by learning from past events and adapting its analysis approach based on current network conditions.

Inventive Principle:

Principle #23Feedback

3Loss of information

If comprehensive network monitoring is implemented, then complete visibility of network events is achieved, but computing resource consumption increases

Engineering Contradiction:

Improvecompleteness of network visibilityVSAvoidcomputing resource consumption

Core Design Contradiction:

Loss of informationVSUse of energy by moving object

Solution Approach 1:

The patent extracts only the essential state transitions and relevant events needed for root cause analysis from the complete set of network events. The state machine model focuses on capturing critical behavior changes rather than processing all raw network data, maintaining complete visibility of important network conditions while reducing computing resource consumption.

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS12244452B1Network monitoring and healing based on a behavior model

Publication Date: 2025.03.04 GOOGLE LLC

AI summary

Aspects of the disclosure are directed to monitoring, alerting, and/or root causing network problems based on current network behavior and network events at any instant in time using a network behavior model. The network behavior model can learn and be updated with network states and events to correlate network problems and determine root causes of the network problems for alerting and/or automatic correction.