Virtual Network Assistant for Location-Based SLE Root Cause Analysis
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Solution Overview
Problem
Existing network management systems struggle to accurately identify transient noise as the root cause of degraded service level expectation (SLE) metrics in wireless networks, often leading to unnecessary remedial actions and resource wastage.
Innovation Solution
A network management system (NMS) utilizes location data of client devices to cluster devices experiencing poor SLE metrics, determining if the degradation is correlated with transient noise rather than a faulty network component, thereby avoiding unnecessary remedial actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If network management systems attribute performance degradations to faulty access points or switches without location analysis, then root cause identification speed is improved, but measurement precision deteriorates leading to incorrect diagnoses
Solution Approach 1:
The patent introduces location data as a new dimension for analyzing network performance issues. By clustering client devices based on their geographic locations and comparing SLE metrics across different locations, the system can determine whether performance degradations are due to transient noise (affecting specific locations) or actual device faults (affecting all locations). This spatial dimension transforms the diagnostic approach from rapid but inaccurate single-point analysis to comprehensive multi-location analysis.
2Measurement precision
If network management systems perform comprehensive location-based analysis to accurately distinguish transient noise from device faults, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system automatically performs location-based clustering and SLE metric comparison without requiring manual intervention. The network management system self-services by autonomously determining root causes through algorithmic analysis of location data and performance metrics, eliminating the need for complex manual diagnostic procedures while maintaining high accuracy.
3Reliability
If network management systems implement location-based clustering and analysis, then reliability of root cause determination is improved, but loss of information increases due to additional data processing requirements
Solution Approach 1:
The patent extracts and utilizes only the essential elements needed for accurate root cause determination: client device locations and SLE metrics. By focusing on these key data points and clustering devices based on location, the system achieves high reliability without being overwhelmed by excessive data processing. The approach extracts the critical spatial dimension from the broader network data set.
Data Source
AI summary
Techniques are described in which a network management system (NMS) is configured to determine a root cause of degraded network performance based on SLE metrics and the locations associated with network devices providing the SLE metrics. The NMS can determine service level experience (SLE) metrics associated with each client device on a network and location data for each client device of the plurality of client devices. The NMS can generate a time series of parameter vectors, where each parameter vector includes SLE metrics corresponding to each client device of the plurality of client devices. Each parameter vector is associated with the location of the client device corresponding to the SLE metrics. The NMS can determine, based on the time series of parameter vectors and associated locations, a root cause for a degradation in SLE metrics associated with the one or more of the client devices.