Location-Aware Server Instance Selection with NWDAF Analytics
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Solution Overview
Problem
Existing communication systems struggle to select the optimal server application instance for a user equipment (UE) based on its location, leading to suboptimal performance due to factors like proximity, resource utilization, and network load, without considering performance analytics.
Innovation Solution
A novel solution involving the Network Data Analytics Function (NWDAF) and Server Application Discovery Management Function (SDMF) that utilize performance analytics from Application Performance Measurement Functions (APMFs) to determine the best server application instance by analyzing historical performance data across different Edge Data Networks (EDNs), ensuring optimal communication performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If a UE is located in an edge data network service area, it receives the address of an appropriate edge-instance Server Application, which reduces latency and improves response time, but the system lacks performance analytics to determine the optimal instance selection
Solution Approach 1:
The system performs preliminary actions by collecting performance data from multiple server application instances before a UE needs to connect. The NWDAF collects data such as response times, throughput, and resource utilization metrics in advance, stores this information, and makes it available when selection decisions are needed, eliminating the need for real-time performance evaluation at the moment of connection
Solution Approach 2:
The NWDAF acts as an intermediary between the SDMF and the server application instances. It collects performance data from instances, processes this data into analytics, and provides the processed information to the SDMF for optimal instance selection, bridging the gap between raw performance metrics and decision-making
2Productivity
If the system selects server application instances without performance analytics, it can operate with simpler architecture, but communication performance is suboptimal due to not considering factors like resource utilization and network load
Solution Approach 1:
The system segments the server application deployment across multiple Edge Data Networks, with each EDN hosting multiple instances of the same application. This segmentation allows the NWDAF to collect performance data from distributed instances and enables the SDMF to select the optimal instance based on location and performance analytics, improving communication performance while maintaining manageable system architecture through modular organization
Solution Approach 2:
The NWDAF continuously collects performance feedback data from server application instances including response times, throughput, resource utilization, and network load. This feedback loop enables dynamic analysis of instance performance and allows the system to adapt selections based on current conditions, improving communication performance while the structured feedback mechanism keeps the architecture organized and manageable
3Adaptability or versatility
If multiple server application instances are deployed across different Edge Data Networks, the system can serve UEs based on location, but determining the optimal instance becomes complex without performance data analytics
Solution Approach 1:
The system replaces manual or rule-based instance selection mechanics with data-driven analytics. Instead of using simple location-based routing or static load balancing, the NWDAF collects and analyzes performance metrics from multiple instances across different EDNs, and the SDMF uses this analytics to automatically select the optimal instance, substituting complex manual optimization with automated data-driven decision-making
Solution Approach 2:
The NWDAF monitors and analyzes multiple performance parameters including response time, throughput, resource utilization, and network load across different server application instances. By tracking changes in these parameters over time and comparing them across instances, the system can dynamically determine the optimal instance for each UE based on current performance conditions, making the complex task of instance optimization measurable and manageable through parameter analysis
Data Source
AI summary
Apparatuses, methods, and systems are disclosed for selecting a server application instance. One apparatus includes a network interface that communicates with a plurality of network functions in a mobile communication network and a processor that receives a request from a first network function to provide performance analytics for a first application. Here, the request includes a present location and a requested time. Additionally, the first application comprises a group of application instances. The processor generates performance analytics for the first application by using a first collection of performance data and reports the performance analytics to the first network function. Here, the performance analytics indicate a best application instance in the group of application instances for the present location and the requested time.