Cross-Layer KPI Analytics for Network Troubleshooting
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Solution Overview
Problem
Current network monitoring and troubleshooting methods for complex services like Voice over Long Term Evolution (VOLTE) are inefficient due to the need to collect and process large amounts of data across multiple protocols, leading to resource bottlenecks and requiring expertise from multiple domains to diagnose issues end-to-end.
Innovation Solution
The development of cross-layer key performance indicators (KPIs) and a data-driven approach to holistically manage protocols, allowing for automatic threshold crossing alarm generation, dashboard creation, and workflow drill-downs, which reduces the need to collect all event data and enables operational engineers to manage networks without requiring subject-matter expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all event data from all protocols are collected and processed, then monitoring precision and troubleshooting capability are improved, but data processing load and resource consumption increase significantly
Solution Approach 1:
The patent extracts only the essential and relevant event data from multiple protocols needed for end-to-end service monitoring, rather than collecting all event data. This selective extraction approach maintains monitoring precision while significantly reducing data processing load and resource consumption.
Solution Approach 2:
The patent segments the monitoring approach by protocol layer, processing each protocol's event data independently and selectively. This allows the system to focus on critical events from each protocol without being overwhelmed by the total volume of all event data across all protocols.
2Reliability
If event data from multiple protocols is collected for end-to-end troubleshooting, then problem detection capability is improved, but system complexity and operational difficulty increase
Solution Approach 1:
The patent merges multiple protocol-specific monitoring systems into a unified cross-layer monitoring framework. This integration maintains comprehensive problem detection capability across all protocols while simplifying the operational interface and reducing system complexity through centralized management.
Solution Approach 2:
The patent introduces an intermediary layer that translates and correlates events from different protocols into a unified troubleshooting view. This intermediary approach enables end-to-end problem detection without requiring operators to directly manage the complexity of multiple separate protocol systems.
3Measurement precision
If comprehensive event data collection is implemented across all protocols, then troubleshooting accuracy is improved, but compute and storage resources are exhausted
Solution Approach 1:
The patent applies partial action by collecting and processing only the necessary subset of event data required for accurate troubleshooting, rather than implementing excessive data collection across all protocols. This selective approach maintains troubleshooting accuracy while conserving compute and storage resources.
Solution Approach 2:
The patent applies local quality by tailoring the data collection and processing approach to each specific protocol and service requirement. This allows the system to allocate compute resources efficiently by focusing processing power on critical event data from each protocol rather than uniformly processing all event data.
4Measurement precision
If all protocols are monitored separately with separate management systems, then protocol-specific monitoring precision is maintained, but end-to-end troubleshooting efficiency decreases
Solution Approach 1:
The patent merges separate protocol-specific management systems into an integrated cross-layer monitoring framework that maintains protocol-specific monitoring precision while enabling efficient end-to-end troubleshooting through unified correlation and analysis of events across all protocols.
Data Source
AI summary
Methods, apparatus, and system for generating efficient cross-layer key performance indicators for monitoring, managing and debugging communications networks. An exemplary method embodiment includes the steps of: generating a plurality of different cross-layer key performance indicators (CL-KPIs) from a set of event data records corresponding to a first period of time and a first base protocol, each CL-KPI in said plurality of different CL-KPIs being for a different failure cause scenario; identifying a CL-KPI in the plurality of different CL-KPIs corresponding to the first period of time and the first base protocol having a highest CL-KPI value and determining a most likely failure cause scenario for said first base protocol to be the failure cause scenario associated with the identified CL-KPI having the highest CL-KPI value.