Service Enablement Layer Analytics for 5G Data Integration
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Solution Overview
Problem
Current 5G Core (5GC) data analytics services lack definition for vertical scenarios, and there is a need for data analytics services at the service enablement layer to support end-to-end service operations, including application performance, user presence, network resource utilization, and edge network management.
Innovation Solution
Implementing a service enablement layer analytics service using an ADAE server or client that receives requests, collects data from service layer servers and clients, generates analytics results, and performs actions based on these results to optimize network and application performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data analytics services are implemented at the service enablement layer to support end-to-end service operations, then the capability to collect and analyze data for application performance, user presence, and network resource utilization is improved, but the system complexity increases due to the need to integrate multiple data sources and generate comprehensive analytics
Solution Approach 1:
The service enablement layer is segmented into distinct functional components: data collection functions for gathering data from multiple sources, data processing functions for analyzing and generating analytics, and service exposure functions for delivering analytics to consumers. This segmentation allows each component to be independently optimized and managed, reducing overall system complexity while maintaining comprehensive analytics capability.
Solution Approach 2:
The service enablement layer is designed as a universal platform that can collect data from diverse sources (service layer servers, edge networks, core network) and generate multiple types of analytics (application performance, user presence, network resource utilization) through a unified architecture. This multi-functionality approach consolidates what would otherwise be separate systems into a single versatile analytics platform.
2Measurement precision
If comprehensive data collection is performed from service layer servers and clients to generate detailed analytics results, then the measurement precision for application performance and network resource utilization is improved, but the data collection and processing time increases
Solution Approach 1:
The system performs preliminary data collection and preprocessing actions at the service enablement layer before analytics generation. Data is aggregated and structured in advance, allowing rapid analytics computation when requested. This preliminary preparation reduces the time needed for actual analytics processing while maintaining measurement precision through comprehensive data collection.
Solution Approach 2:
The data collection and processing system is designed to be dynamic, adjusting the scope and depth of data collection based on analytics requests. For time-sensitive analytics, the system can prioritize recent data and reduce collection scope; for comprehensive analytics, it can expand data gathering. This dynamic approach optimizes the balance between measurement precision and processing time.
Data Source
AI summary
Provided herein are various methods, apparatus, and systems for providing a service enablement layer analytics service in a cellular network, such as at the 3GPP service enabler layer. For example, an apparatus may receive, from an analytics service consumer, a service enablement layer data analytics request, wherein the request includes requirements of the analytics service, determine based on the service enablement layer analytics request, to collect data from a service layer server and/or a service layer client, generate analytics result based on the received server-side data, the received client-side data and the requirements of the analytics service, send, to the analytics service consumer, and perform actions or trigger actions to be performed based on the analytics results.