Network Backend Automating Root Cause Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Wireline network operators face challenges in efficiently managing and maintaining large-scale wireline communication networks due to complex data aggregation from disparate nodes, leading to sub-optimal performance and poor customer experience, as existing systems struggle with real-time data processing and root cause identification of malfunctions.
Innovation Solution
A scalable network backend with an open architecture that combines multiple data feeds from disparate nodes in real-time, utilizing big data technologies and machine learning to automate monitoring, analysis, and optimization, distinguishing between new and existing nodes for robust installation and maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data from multiple disparate nodes is aggregated manually, then analysis completeness improves, but processing time and operational complexity increase significantly
Solution Approach 1:
The system enables automated self-service through machine learning models that autonomously analyze network data, identify malfunctions, and generate reports without requiring manual engineer intervention. The backend automatically processes data from disparate nodes, performs root cause analysis, and delivers insights, freeing operators from time-consuming manual analysis while maintaining comprehensive information coverage.
Solution Approach 2:
Manual mechanical analysis processes are replaced with automated computational systems. Machine learning algorithms substitute human engineers' analytical work, automatically processing and correlating data from multiple nodes to identify patterns and root causes, dramatically reducing processing time while preserving analytical depth.
2Measurement precision
If manual analysis of network data is performed, then operational costs are reduced, but analysis accuracy and speed deteriorate
Solution Approach 1:
Manual analysis is replaced with machine learning-based automated analysis systems. These systems process network data with superior speed and accuracy by leveraging computational algorithms that can simultaneously analyze multiple data sources and identify subtle patterns that human analysts might miss, while operating continuously without fatigue.
Solution Approach 2:
The system transforms analysis parameters by using machine learning models that dynamically adjust analysis thresholds, data sampling rates, and processing priorities based on network conditions. This enables the system to maintain high accuracy across varying data volumes and complexity levels while optimizing processing speed for different operational scenarios.
3Reliability
If comprehensive monitoring of all nodes is implemented, then network health improves, but system complexity and processing requirements increase
Solution Approach 1:
The system extracts and isolates only the most critical features and indicators from the vast amount of network data using machine learning techniques. By focusing on key performance indicators and anomaly patterns rather than processing every raw data point, the system maintains comprehensive monitoring capability while reducing computational complexity and processing requirements.
Solution Approach 2:
The monitoring system is segmented into modular components, with different machine learning models handling specific node types or failure modes. This segmentation allows the system to scale monitoring coverage to all nodes while managing complexity through specialized, targeted analysis modules rather than a monolithic processing system.
4Speed
If real-time data processing is implemented, then response time to malfunctions improves, but computational resources and costs increase
Solution Approach 1:
The system implements periodic processing intervals where data is analyzed at optimized frequencies based on network conditions and criticality levels. Non-critical nodes or stable conditions use lower processing frequencies, while critical events or unstable conditions trigger higher-frequency analysis. This periodic approach enables real-time responsiveness while significantly reducing average computational resource consumption compared to continuous maximum-intensity processing.
Data Source
AI summary
A method and system of managing fixed line network elements. Data from disparate sources is received by a processing layer of a monitoring server via a wireline communication network. The intelligence layer determines whether a first node from the disparate nodes is new or pre-existing. Based on a determination by the intelligence layer whether a node is new or pre-existing, different static rules are applied to the received data from the first node. Contextual information is retrieved from the measurements megastore related to the first node. A root cause of a malfunction of the first node is determined. A notification is generated based on the root cause of the malfunction.