Integrating AI and data to monetize 5G Cloud Platforms

As of Q1 2022 for 5G adoption we have just passed 600M mark and expected to hit 2.5B connections by 2025 that is almost 500M every year . If we combine IoT and Device ecosystem the scale can go horrendously big .One of the biggest advantage and also challenge that comes from this once in a life time opportunity is “scale” .

Simply putting in to context “automation” and using ML/AI is a must to achieve both Network SLA’s ,efficiency and Optimizing Network TCO

AI has potential in creating value in terms of enhanced workload availability and improved performance and efficiency for 5G and Telco Cloud . However the biggest problem when it comes to use “AI” and Machine learning Telco’s is “Data” and “data Models” because simply there is no standardization or model definition on how Telco systems including Infrastructure expose the “Information” to upper layers Since data sets are huge in this domain with n permutations therefore first step to normalize is the Use case driven normalization of data that can be consumed both by Network and Data science domains . This will enable to develop a future Telco that can detect and also self heal itself .

Understanding Data Integration Architecture

Considering the 5G architecture which is based on Open API and Horizontal services design A.K.A SBA the Data integration and using AI should be an easy problem that can be divided in following to define a pipeline

  1. Telemetry and data
  2. Each layer data exposure as an API starting from Baremetal and then extending upwards towards Cloud , SDN , NFVO , Assurance etc
  3. Data models and engines to disseminate information

However it is easier said than done because of many reasons including

  1. What will be key data sets
  2. how FCAPS of each layer can be dis-aggregated i.e dropping one layer data without confirming dependency is a kill

Business Architecture

In order to address this we need to understand and gain experience from other industries and SDO’s and to see how it can both be agreed and integrated in Telco Networks , this lead us to approach this as a use case driven approach and select those domains and business challenges that can deliver quick results

"Follow the Money to deliver use cases that can monetize 5G

We have analyzed lot of use cases both from academia and industry and compiled a complete list here

From this we infer there are just too many ways Telco’s are solving same problems and this is what make us understand that there should be clear definition of “data Models” and use cases that should be defined at first steps .

The most important of which are :

  1. Using Machine Learning to Detect Noisy Neighbors in 5G Networks.

2.Towards Black-Box Anomaly Detection in Virtual Network Functions

3. Causality Inference for Failure in NFV

4. Self Adaptive Deep Learning Based System for Anomaly Detection in 5G

5. Correlating multiple Events and Data in an Ethernet Network

This leads us to define following as first steps for AI and Intelligence as applied to Telco’s

Source: LFN acumos


Data Lakes , Log analysis and correlation


Anomaly detection including pattern detection , trend and Multi layer correlation


Intelligent prediction including capacity ,SLA , Scaling and Cloud KPIs


Measure data and Synthetize it using frameworks like eBPF

Data Monetization is first to make 5G Profitable

Adressing both the Data Architecture and Business Architecture is vital as different Telco’s including in cases different BU’s in same Customer take it differently and what makes it worst is manipulate and store data lakes using different forms i.e Infrastructure metrics , Agents , Databases which is hard to apply between different data sets and hence it is biggest issue to Monetize one key assets of 5G which is “data” and hence to define a pipeline that can be shared between all of tenants including vertical industry

The latest White Paper: Intelligent Networking, AI and Machine Learning

Next Steps of “Thoth”

As said before we are defining few key use cases in LFN project “Thoth” to learn and elaborate from there applying concepts of Events , Anomaly and Prediction across layers and first phase use cases are

  1. VM failure
  2. Container Failure
  3. Node Failure
  4. Link Failure
  5. Middle layer Link failure

The detailed list can be seen here Use cases


Published by

Saad Sheikh

I am a Senior Architect with a passion to architect and deliver solutions addressing business adoption of the Cloud and Automation/Orchestration covering both Telco and IT Applications industry. My work in carrier Digital transformation involve Architecting and deploying Platforms for both Telco and IT Applications including Clouds both Open stack and container platforms, carrier grade NFV ,SDN and Infra Networking , DevOps CI/CD , Orchestration both NFVO and E2E SO , Edge and 5G platforms for both Consumer and Enterprise business. On DevOps side i am deeply interested in TaaS platforms and journey towards unified clouds including transition strategy for successful migration to the Cloud Please write to me on

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s