Redesign and Deployment of Red Hat Openshift AI and IBM Watsonx workload on IBM Cloud Satellite based on CXL Disaggregated Memory by using orchestrated 5G/6G slicing for AI workloads on Distributed cloud and Cloud satellite

Redesign and Deployment of Red Hat Openshift AI and IBM Watsonx workloads on IBM Cloud Satellite based on CXL Disaggregated Memory by using orchestrated 5G/6G slicing for AI workloads on Distributed cloud and Cloud satellite

Please Note: I re- write this idea ( the description and the details of the new idea) because it was duplicate to its previous idea, therefore please refer to this new version of the idea as shown below

This idea is important because of the following:

1- it is better to make a specific 5G/6G Network slicing for AI workloads connection between 2 sides as follows:

A- Public/Private cloud side

B- the cloud satellite , Distributed cloud and EdgeAI side.

2- Redesign the configuration and orchestration to supporting the 5G/6G network slice of IBM Watsonx and other AI workloads

3- For Openshift AI:

A- This idea will efficiently manage, control and deploy the Generative AI LLMs Models, RAGs, Knoledge graphs between the cloud and on- premise data centers.

B- This idea will enable the AI microservices of Nvidia; Intel, and others working in a unified infrastructure with unified Data protection.

C- Most AI workloads of Edge computing will work with unified CXL Memory in the cloud and the on- premise data center. This will permit using multiple LLM, RAG, and Knowledge graph depending on Single Distributed Lakehouse.

4- This idea is very important to IBM Watsonx.ai and Watsonx.data because they are based on IBM Ceph and Openshift.

5- Enabling the Threat Detection easily with analysing and tracking the threatsand forensics depending on Cloud AI security intelligence workload with on-premise data.

6- This idea can be applied in the countries those have legislation about processing and analyzing the data locally inside the country.