NWDAF (Network Data Analytics Function) is a network function in 5G Core. With the increase in 5G requirements, a huge number of cell sites and connected devices lead to a surge in requirement of 5G bandwidth. NWDAF provides analytics to 5GC NFs and OAM. It uses standard 3GPP interfaces for centralized data collection and analytics through subscription or by request. NWDAF collects data from various resources like the NFs of the 5GC (AMF, SMF, UPF, etc), Application Functions (AF) and OAM. After collecting all the metrics it analyses and correlates the data and provides the analytics in the form of 3GPP specified APIs.
Analytics reporting information provided by NWDAF are:
Statistics - NFs/OAM request analytics target period in the past.
Predictions - NFs/OAM request analytics target period in the future.
NWDAF retrieves network data from 5G NFs and management data from OAM :
AF Data ( possibly via NEF)
UE related data from NFs
OAM global NF data
Fig 1: NWDAF Architecture
The above diagram shows the logical components of NWDAF from Aarna Networks. The NWDAF Service API, as part of 3GPP specs, has Analytics Info Service and Subscription Service. Subscription Service provides data in a periodic fashion. The Data Collector in NWDAF collects data from
NF/AF and pushes it into further layers. Data Transformation layer transforms the data before it reaches the database or data repository. Analytics - Historic is mainly for statistics which consists of Analytics Application and Analytics Algorithm. Analytics Predictions give the predictions and manage the AI/ML Model Catalogue. It also includes the Deployment Model and Training Model. Our partner Predera helps us with the MLOps part. The outputs from NWDAF are then formatted by Output Formatter and finally notifies the subscribers.
Demo Modules consist of -
● NWDAF : from Aarna Networks
● NRF: from Free5GC
● AF: is the consumer of the data from NWDAF
Explaining the NF Load Use Case -
● NF Load Use case: NWDAF provides NF load analytics of specific NFs in the form of statistics as well as predictions.
● Input data sources are OAM and NRF.
● Output analytics are Network Function Status (whether it is up and running or down), resource usage(cpu, memory, storage) and the NF load.
● Helps in infrastructure scaling and insights of service experience. Based on NF load analytics autonomous networks can take a decision to scale out and obtain insights of the running status of the NF.
Fig 2: Demo Flow
In our demo AMCOP from Aarna Networks is used for deployment and configuration of all these NFs. It can deploy the Network Functions in multiple edge clusters. However in this demo we deployed the NFs on a single edge cluster and monitor the interaction between them. The demo flow has been explained below (in reference to Fig 2).
Design and orchestrate NRF,NWDAF registers itself with NRF with the help of NF register request.
Design and orchestrate AF. AF will discover NRF through Day0 configuration and obtain the NWDAF end points
AF queries NWDAF AnalyticsInfo API to get NF_LOAD data
NWDAF analytics info API implementation internally calls CPU prediction model to get the cpu predictions
NWDAF creates output responses as per the 3GPP standards
To see the demo please follow the video.