NWDAF Edge Analytics for Predictive MEC Resource Allocation
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Solution Overview
Problem
Existing MEC platforms face challenges in managing limited compute resources and lack a mechanism to incorporate user behavior data for optimizing edge resource utilization and improving Quality of Experience (QoE).
Innovation Solution
Implementing a Network Data Analytics Function (NWDAF) that receives user data from User Equipment (UE) devices, applies AI/ML techniques to predict user behavior, and optimizes edge computational resources through an Edge Analytics Engine (EAE) using enhanced interfaces like EDGE-10 for data exchange.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If MEC platforms transfer high network computing loads onto edge servers, then service delivery speed and latency are improved, but compute resources become limited and require careful management
Solution Approach 1:
The system performs preliminary actions by collecting user behavior data in advance through the Edge Analytics Engine and using AI/ML to predict future user behavior patterns. This allows the MEC platform to pre-allocate and pre-position compute resources and content based on predicted demands, rather than reacting to actual usage patterns in real-time. The NWDAF function proactively subscribes to user data and generates analytics reports before resource constraints become critical.
Solution Approach 2:
The system implements dynamic resource management by continuously monitoring user behavior data and adjusting compute resource allocation in real-time. The NWDAF function dynamically subscribes to different user data streams based on changing conditions, and the MEC platform dynamically repositions content and adjusts service delivery based on predicted user behavior. This dynamic adaptation allows optimal resource utilization without over-provisioning.
2Quantity of substance
If distributed load handling is implemented among edge nodes and central nodes, then resource utilization is optimized, but processing delay increases between nodes
Solution Approach 1:
The system applies local quality by using AI/ML to predict which specific edge nodes will have high demand based on user behavior data, and then pre-positioning content and compute resources locally at those specific nodes. Rather than uniformly distributing load across all nodes, the system optimizes resource placement at each local edge node based on predicted local demand patterns, minimizing inter-node data transfer while maintaining high resource utilization.
Solution Approach 2:
The NWDAF function acts as an intermediary between user devices and edge nodes, collecting user behavior data and generating predictive analytics that guide resource distribution decisions. This intermediary layer enables centralized intelligence to coordinate distributed edge nodes efficiently, optimizing resource utilization across the network while minimizing processing delays through informed decision-making.
3Quantity of substance
If user behavior data is collected and analyzed, then edge resource utilization and Quality of Experience are optimized, but system complexity increases
Solution Approach 1:
The NWDAF function serves multiple purposes: it collects user behavior data, performs AI/ML analysis, generates predictive analytics, and provides guidance for resource allocation. This multi-functional approach consolidates what could be separate complex systems into a single unified function, reducing overall system complexity while achieving optimized resource utilization and improved Quality of Experience.
Solution Approach 2:
The system implements self-service by using AI/ML algorithms to automatically analyze user behavior data and generate resource allocation decisions without requiring manual intervention. The NWDAF function autonomously subscribes to user data streams, processes the information, and provides actionable insights, reducing the operational complexity of managing edge resources while maintaining high utilization efficiency.
Data Source
AI summary
Systems and methods described herein provide an application function and interface for a Network Data Analytics Function (NWDAF) to receive, from User Equipment (UE) devices, user data for edge applications. An NWDAF receives an analytics request for user analytics for an edge application and sends, to an Edge Analytics Engine (EAE) associated with an edge location, a data request based on the analytics request. The EAE receives user data from an Edge Enabler Client (EEC) on the UE device that is connected to the edge location. The EAE forwards the user data to the network device. The NWDAF receives the user data responsive to the analytics request and generates an analytics report based on the user data.