Network Anomaly Root Cause Detection Through Hierarchical Topology
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Solution Overview
Problem
Existing network infrastructure in data centers is prone to faulty components that can cause network anomalies and disruptions due to misconfiguration, damage, or erroneous states, which are often undetected and lead to non-reachable services or overloaded redundant pathways, affecting user experience.
Innovation Solution
A computer system and method that utilizes historical network data and hierarchical network topology to identify root causes of network anomalies by selecting root cause candidates and removing those with common upstream entities, identifying the most upstream entity as the root cause, and generating alerts for rectification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If redundant connections are implemented to secure network elements, then network reliability is improved, but network complexity increases and redundant pathways may become overloaded
Solution Approach 1:
The patent segments the network into hierarchical levels (core, distribution, access) with clear upstream-downstream relationships. This segmentation allows the system to identify root causes by traversing upstream from affected downstream entities, reducing the complexity of analyzing redundant connections while maintaining reliability.
Solution Approach 2:
The patent introduces an intermediary analysis system that uses network topology data and anomaly detection to identify root cause entities. This intermediary layer mediates between redundant network connections and fault detection, automatically determining which upstream entity is the actual root cause without requiring manual analysis of complex redundant pathways.
2Measurement precision
If manual monitoring of network components is performed, then detection accuracy can be maintained, but time consumption increases and faults remain undetected for longer periods
Solution Approach 1:
The patent performs preliminary actions by continuously collecting and storing network topology data, performance metrics, and configuration information before anomalies occur. When an anomaly is detected, the pre-prepared data enables immediate root cause analysis by traversing upstream entities, eliminating the time delay associated with manual data gathering while maintaining accurate detection through pre-established baseline comparisons.
Solution Approach 2:
The patent implements feedback mechanisms where anomaly detection results trigger automated upstream traversal and root cause identification. The system continuously monitors network entities, detects anomalies, determines root causes, and provides feedback for corrective actions. This closed-loop feedback system maintains high detection accuracy while reducing response time compared to manual monitoring.
3Measurement precision
If comprehensive network topology tracking is implemented, then root cause identification accuracy is improved, but computational complexity and data processing requirements increase
Solution Approach 1:
The patent applies local quality by focusing computational resources on the specific upstream path from affected downstream entities to potential root causes, rather than analyzing the entire network topology. The system determines upstream entities only for the subset of affected network elements, reducing computational complexity while maintaining accurate root cause identification for the specific anomaly being investigated.
Solution Approach 2:
The patent segments the root cause identification process into distinct stages: anomaly detection, upstream entity determination, root cause candidate identification, and verification. This segmentation allows the system to process network topology data in manageable portions rather than analyzing all possible relationships simultaneously, reducing computational complexity while maintaining identification accuracy.
Data Source
AI summary
Root causes of network anomalies can be identified as follows. A subset of network entities that have experienced network anomalies during a time period are determined based on historical network data. A set of root cause candidates are selected among the plurality of network entities by iterating through the network topology, each root cause candidate being directly upstream of two or more network entities in the subset of network entities that have experienced network anomalies according to the network topology. Network entities that are root causes of the network anomalies are identified by removing root cause candidates that have a common upstream network entity that is also a root cause candidate from the set of root cause candidates leaving a set of remaining root cause candidates that are the root causes.