Network Device Performance Diagnosis via Cross-Correlated Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing network connectivity issues are challenging to diagnose due to the inability of conventional approaches to accurately distinguish between local and global performance problems, often resulting in unreliable Internet access and disruptions for users and enterprises.
Innovation Solution
A system that includes an analysis server with sub-optimal performance determination logic, which classifies network devices as performing sub-optimally by cross-correlating data from network devices and geographic clusters, using IP address trees, BGP data, and independent location data to determine whether disruptions are caused by Wide Area Network issues or local problems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional network diagnostic approaches are used, then network connectivity issues can be detected, but the ability to accurately distinguish between local and global performance problems is insufficient
Solution Approach 1:
The system segments the network diagnostic process into multiple independent analysis components: geographic cluster analysis, subnetwork analysis, and individual device analysis. Each component evaluates specific aspects of network performance and combines results to determine whether disruptions are caused by local or global issues, thereby improving diagnostic accuracy and reliability
Solution Approach 2:
The system introduces an analysis server as an intermediary that collects and correlates data from multiple sources including network devices, geographic location data, and subnetwork information. This intermediary processes raw data through structured logic to produce accurate diagnostic conclusions about network performance issues
2Measurement precision
If comprehensive network data is collected from multiple sources, then diagnostic confidence increases, but system complexity increases
Solution Approach 1:
The system divides complex network data into structured segments: geographic cluster data, subnetwork data, and individual device data. Each segment is processed independently through specific analysis logic, then combined to form comprehensive diagnostics. This segmentation manages complexity while maintaining high diagnostic confidence
Solution Approach 2:
The system transforms raw network data into standardized parameters organized in structured formats (IP address trees, geographic coordinates, subnetwork classifications). This parameter transformation enables efficient processing and correlation of diverse data sources without proportionally increasing system complexity
Data Source
AI summary
In one example, a server classifies a subnetwork of network devices as performing sub-optimally. The server also classifies a geographic cluster of network devices as performing sub-optimally. The server determines whether a particular sub-optimally performing network device is in both the subnetwork and the geographic cluster. If it is determined that the particular sub-optimally performing network device is in both the subnetwork and the geographic cluster, the server identifies the particular sub-optimally performing network device as performing sub-optimally due to a performance issue with a Wide Area Network to which the particular sub-optimally performing network device belongs.