ADAES Edge Load Analytics for Proactive Overload Prevention
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Solution Overview
Problem
Existing 3GPP technologies lack mechanisms for edge load analytics, specifically for edge application servers, and do not provide analytics per data network name, per data network access identifier, or per edge enabler server, failing to address data collection from sources like N6 endpoints and MEC platforms, and do not utilize analytics for proactive edge node actions.
Innovation Solution
A computer-implemented method and apparatus for edge load analytics at an Application Data Analytics Enabler Server (ADAES) that collects data from various domains, derives edge load analytics, and triggers proactive actions to manage edge node overload.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If edge load analytics are implemented at ADAES to provide statistics and predictions for edge service optimization, then edge service performance is improved, but system complexity increases due to multiple data collection domains and analytics processing requirements
Solution Approach 1:
The patent segments the edge analytics system into multiple independent components: data collection module that gathers data from various domains, data processing module that performs analytics, and action triggering module that executes optimization actions. This modular segmentation allows each component to be developed and maintained independently, reducing overall system complexity while enabling comprehensive edge service optimization through coordinated operation of the segments.
2Measurement precision
If data is collected from multiple domains including N6 endpoints and MEC platforms to derive comprehensive edge load analytics, then measurement precision is improved, but device complexity increases due to multiple data sources and collection mechanisms
Solution Approach 1:
The patent introduces an intermediary data collection module that acts as a mediator between multiple data sources (N6 endpoints, MEC platforms, edge application servers) and the analytics processing system. This intermediary module standardizes data collection interfaces and formats, enabling precise analytics by aggregating data from diverse sources while shielding the core analytics engine from the complexity of multiple data collection mechanisms.
3Reliability
If proactive actions are triggered based on edge load analytics to prevent overload and optimize performance, then reliability is improved, but device complexity increases due to action management and coordination requirements
Solution Approach 1:
The patent implements preliminary action mechanisms where the system proactively triggers optimization actions (such as load balancing, resource allocation adjustments, or service migration) based on predicted edge load conditions before actual overload occurs. The action triggering module continuously monitors analytics outputs and executes pre-defined optimization strategies in advance, improving service reliability by preventing problematic states while managing complexity through automated decision-making rules.
Data Source
AI summary
The invention provides a functionality for providing edge load analytics at an edge analytics producer as well as functionality for utilizing this edge load analytics for optimizing edge service performance. An edge analytics producer is configured to collect data and to perform edge load analytics considering data producers from different domains, to allow for edge analytics enablement. Further, based on the derived edge load analytics by the edge analytics producer, an analytics consumer is configured to generate a trigger event that indicates a predicted overload and a specific action to be performed.