CMTS Data Stream Analysis via Telemetry Correlation
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Solution Overview
Problem
Existing cable television and content distribution networks face challenges in rapidly identifying network fault sources, particularly in customer premises equipment (CPE), which can lead to intermittent connections and upstream faults, degrading network performance and quality of service.
Innovation Solution
The system employs a telemetry data capture device (TDCD) to receive and extract data streams from a cable modem termination system (CMTS), and a computer-implemented analytics services engine to correlate anomalies in upstream data with cable modem operational settings, identifying CPE associated with degraded operations and facilitating fault correction and performance improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional network monitoring methods are used to identify CPE faults, then network infrastructure remains simple and easy to manage, but fault identification speed is slow and network performance degradation is not detected in real-time
Solution Approach 1:
The monitoring system is segmented into specialized functional modules: TDCD for data capture, analytics engine for correlation analysis, and separate processing streams for different data types. This segmentation enables parallel processing of multiple data streams simultaneously, reducing fault identification time while keeping each module's complexity manageable and focused on a specific task.
Solution Approach 2:
The patent introduces intermediary components including data buffers that decouple data capture from analysis, and correlation engines that mediate between raw telemetry data and fault detection. These intermediaries enable real-time processing without requiring direct coupling between all system components, thus reducing overall system complexity while improving response speed.
2Measurement precision
If comprehensive CMTS data streams are captured and analyzed in real-time, then fault detection accuracy improves, but data processing requirements and system resource consumption increase
Solution Approach 1:
The system extracts only the most relevant telemetry data fields from comprehensive CMTS data streams using configured data extraction rules. By taking out only the critical parameters needed for specific fault detection scenarios, the system maintains high detection accuracy while significantly reducing the volume of data requiring processing and the associated resource consumption.
Solution Approach 2:
The analytics engine performs partial correlation analysis by focusing on specific data combinations and correlation patterns based on detected anomaly types. Rather than exhaustively analyzing all possible data relationships, the system applies targeted analysis only where needed, reducing computational overhead while maintaining accurate fault detection for the most critical issues.
3Measurement precision
If detailed correlation analysis is performed between upstream data anomalies and CPE operational settings, then identification of specific faulty CPE improves, but analysis time and computational complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-configuring correlation rules, data extraction templates, and analysis parameters before actual fault detection occurs. Telemetry data is pre-processed and stored in structured formats with relevant fields already extracted and organized. When anomalies are detected, the system can immediately apply pre-configured analysis rules without needing to set up the analytical framework during the actual fault identification process, thus reducing analysis time while maintaining accurate CPE identification.
4Productivity
If real-time data capture and analysis is implemented at wire speeds, then network performance monitoring improves, but infrastructure complexity and implementation difficulty increase
Solution Approach 1:
The TDCD and analytics engine are designed as universal, multi-functional platforms that can monitor multiple CMTS devices, analyze various types of telemetry data, and detect different fault conditions through a single integrated system. The correlation engine can be configured through software to handle different data formats and analysis requirements without requiring separate hardware implementations for each monitoring scenario, thus improving monitoring efficiency while reducing implementation complexity through standardization.
Data Source
AI summary
Systems, methods, architectures, mechanisms or apparatus for analyzing cable modem termination system (CMTS) streams by correlating anomalies found in full spectrum CMTS upstream data to changes in cable modem operational settings to identify and correct network fault conditions, model CMTS behavior, improve network performance and the like.