Automatic AP Behavior Characterization via Latent Space Clustering
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Solution Overview
Problem
Rule-based network assurance systems struggle to quickly characterize and troubleshoot access point (AP) behaviors in rapidly changing wireless networks with increasing mobile clients, as they rely on predefined rules that are difficult to exploit and do not provide a quick overview of AP conditions.
Innovation Solution
A machine learning-based approach that clusters observed AP features within a latent space, applies labels to these clusters, and uses them to describe future AP behaviors, enabling automatic characterization and inference of AP behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule-based network assurance systems are used to monitor AP behaviors, then network health can be determined using predefined rules, but the system becomes difficult to exploit and does not provide quick overview of AP behaviors
Solution Approach 1:
The patent introduces an intermediary characterization system that sits between the raw network metrics and the rule-based assurance system. This intermediary layer automatically characterizes AP behaviors by clustering observed features and applying labels, transforming raw data into meaningful behavioral descriptions that can be quickly interpreted while maintaining reliable network health determination through the underlying rule system
Solution Approach 2:
The patent replaces the manual mechanical process of analyzing predefined rules with an automated machine learning-based characterization system. The system automatically clusters AP features, applies behavioral labels, and generates descriptions without requiring manual rule interpretation, thereby providing quick overview while maintaining reliable monitoring
2Reliability
If predefined rules are applied to network metrics, then network health can be monitored, but the system cannot adapt to constantly changing wireless network conditions with mobile clients
Solution Approach 1:
The patent implements a dynamic characterization system that continuously learns and adapts to changing network conditions. Instead of static predefined rules, the system uses machine learning models that are trained on observed AP features and automatically update their understanding of normal versus abnormal behaviors as network conditions evolve, maintaining reliable monitoring while adapting to mobility and changing client patterns
Solution Approach 2:
The patent changes the fundamental parameters of the monitoring system from fixed rule thresholds to adaptive behavioral characteristics. By clustering observed AP features and deriving behavioral parameters from actual network data rather than predetermined values, the system maintains reliable health monitoring while becoming adaptable to constantly changing wireless conditions with mobile clients
3Reliability
If detailed network metrics are collected for analysis, then comprehensive network health assessment is possible, but the complexity of the system increases
Solution Approach 1:
The patent extracts only the most relevant behavioral characteristics from detailed network metrics through automated clustering and labeling. Instead of analyzing all raw metrics directly, the system extracts essential behavioral patterns and labels that capture the core health status, maintaining comprehensive assessment while reducing system complexity by focusing on key characteristics rather than all individual metrics
Data Source
AI summary
In one embodiment, a device receives observed access point (AP) features of one or more APs in a monitored network. The device clusters the observed AP features within a latent space to form AP feature clusters. The device applies labels to the AP feature clusters within the latent space. The device uses the applied labels to the AP feature clusters to describe future behaviors of the one or more APs in the monitored network.