Router Burst Detection for Silent Network Failure Root Cause
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Solution Overview
Problem
Existing communication networks face challenges in identifying root causes of silent or blackhole failures, which are difficult to detect and address due to their nature of not triggering physical device failure notifications, and conventional methods struggle with timely and accurate root cause analysis in the presence of substantial data volumes.
Innovation Solution
An AI/ML-based clustering and burst detection methodology is employed to identify root causes of network connectivity failures by analyzing bursts of network connectivity test results between edge router pairs, using algorithms like Kleinberg burst detection to pinpoint routers causing soft failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional network monitoring methods are used to detect silent failures, then the system can operate with simple detection mechanisms, but the ability to accurately identify root causes deteriorates due to lack of timely detection
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing network connectivity test results before failures occur. The database maintains historical connectivity data between router pairs, enabling rapid root cause analysis when silent failures happen without requiring complex real-time detection during the failure event itself.
Solution Approach 2:
The system creates copies of network connectivity states through simulated connectivity tests. By generating synthetic connectivity data between router pairs and comparing it with actual network state, the system can identify root causes of silent failures without disrupting actual network operations.
2Reliability
If comprehensive network connectivity testing is performed between all router pairs, then root cause detection capability is improved, but data volume and processing complexity worsen
Solution Approach 1:
The system segments the network into discrete router pairs for individual connectivity testing. Each router pair is evaluated independently, and connectivity results are stored in a structured database format. This segmentation allows comprehensive coverage without requiring complex holistic analysis of the entire network at once.
Solution Approach 2:
The system introduces an intermediary database layer between the connectivity tests and the analysis process. The database stores raw connectivity results and serves as an intermediary structure that enables efficient querying and pattern recognition, reducing the complexity of direct real-time analysis of comprehensive test data.
3Speed
If real-time analysis of all connectivity test results is performed, then response time is improved, but computational resource consumption worsens
Solution Approach 1:
The system performs preliminary organization of connectivity data in a database structure that enables efficient querying. By pre-structuring the data with relationships between router pairs, paths, and connectivity states, the system reduces computational requirements during actual failure analysis while maintaining fast response capability.
Data Source
AI summary
A processing system may obtain network connectivity test results, each indicating success or failure of a network connectivity test between a respective router pair from a plurality of routers of the communication network. The processing system may identify, from the connectivity test results, a burst of network connectivity results indicating network connectivity test failures, and identify, from the connectivity results, a subset of connectivity test results indicating failures from within a time period associated with the burst. The processing system may further determine, for each connectivity test result in the subset, connectivity path information including one or more routers between a router pair of the connectivity test result. The processing system may then determine, from the connectivity path information of the subset, a router with a highest number of occurrences, and may identify the router as a cause of a soft failure in the communication network.


