NWDAF Delay Information Storage for Timely 5G Analytics
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Solution Overview
Problem
Data analytics network elements in 5G communication networks often fail to generate results in a timely manner, leading to non-real-time adjustments in quality of service parameters, which can result in suboptimal service experiences.
Innovation Solution
A communication method and apparatus that enable data analytics network elements to determine and store delay information, allowing network elements to predetermine delay requirements by collecting, inferring, and transferring data within specified delays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the data analytics network element processes data without predetermined delay requirements, then the processing flexibility is improved, but the timeliness of data analytics results deteriorates
Solution Approach 1:
The data analytics network element performs preliminary actions by determining delay requirements before actually processing the data. The element stores delay requirement information in advance and uses this pre-determined information to guide subsequent data processing operations, ensuring that the analytics are completed within the required time frame while maintaining processing flexibility.
2Reliability
If the data analytics network element stores delay information in advance, then the ability to meet delay requirements is improved, but the system complexity increases
Solution Approach 1:
The data analytics network element serves itself by autonomously determining and storing its own delay requirement information. The element uses its processing capabilities to analyze the delay requirements of different data analytics tasks and stores this information in its own storage resources, eliminating the need for external configuration systems and reducing overall system complexity while improving reliability.
3Measurement precision
If the data analytics network element determines delay requirements for all data analytics tasks, then the quality of service adjustment accuracy is improved, but the processing overhead increases
Solution Approach 1:
The data analytics network element applies local quality by determining delay requirements specifically for each data analytics task based on its particular characteristics and requirements. Rather than applying a uniform delay requirement to all tasks, the element analyzes and stores delay requirements locally for each specific analytics operation, ensuring accurate quality of service adjustments while minimizing unnecessary processing overhead for tasks that do not require stringent delay constraints.
Data Source
AI summary
A communication method, a communication apparatus, and system are disclosed. The method includes a data analytics network element (NWDAF) sends a first request to a storage function network element. The first request includes delay information and/or indication information. The indication information indicates that the NWDAF supports analyzing data within a specified delay. The first request is used to store the delay information and/or the indication information into the storage function network element. The data analytics network element receives a first response from the storage function network element. The first response indicates, to the data analytics network element, that the delay information and/or the indication information are/is successfully stored.