Deployment of Secure Machine Learning Pipelines for Near-Real-Time Control of 6G Network Services
Creators
Description
Abstract: A ML function orchestrator deploying secure ML pipelines to support near-real-time control of network services is demonstrated. A distributed ledger supports the initial key exchange to establish secure connectivity among the agents in the pipeline
Summary:
In the rapidly evolving landscape of beyond 5G and 6G network services, Near-Real-Time (NRT) autonomous operation is crucial. Our solution focuses on decentralized control to minimize response times, deploying intelligent agents close to data plane devices while addressing security concerns through Distributed Ledger Technologies (DLT).
Key Highlights:
Autonomous agents making near-real-time decisions based on distributed measurements.
ML pipeline deployment and orchestration designed dynamically based on topological information.
Integration of technologies ensuring the security of the ML pipeline, including the innovative use of DESIRE6G DLT network.
Demo Content & Implementation:
This demo showcases a complete control and orchestration system, capable of deploying secure ML pipelines and connecting distributed agents for the NRT control of highly dynamic NS. The main innovations of this demonstration are the use of autonomous agents making NRT decisions based on measurements collected and processed in different locations and supervised by a centralized entity, the deployment of ML pipelines that involves determining their optimal design based on topological data from a dedicated system after deployment, and the integration that provides the required security to the ML pipelines. A key aspect is the showcase of the use of the DESIRE6G Distributed Ledger Technologies (DLT) network, where new agents are dynamically associated to the DLT to exchange keys through a smart contract deployed for the NS.
Files
[OFC] Demo DLT.pdf
Files
(285.4 kB)
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