Carlos Natalino

A novel framework for in-network P4 processing of distributed telemetry data is presented, enabling effective soft failure detection and recovery strategies enforced in just few microseconds.

We demonstrate a scalable processing of OPM data using ML to detect anomalies in optical services at run time. A dashboard will show operational SDN controller metrics, raw OPM data, and the ML assessment result.

We present P5; an event-driven policy framework that allows network operators to realize end-to-end policies on top of P4-based data planes in an intuitive and effective manner.

To support the pervasive digital evolution, optical network infrastructures must be able to quickly and effectively adapt to changes arising from traffic dynamicity or external factors such as faults and attacks.

Unsupervised learning (UL) is a technique to detect previously unseen anomalies without needing labeled datasets. We propose the integration of a scalable UL-based inference component in the monitoring loop of an SDN-controlled optical network.

We demonstrate zero-touch device bootstrapping, monitoring, and L3-VPN service management using the novel TeraFlow OS SDN controller prototype.

The paper describes the Optical Security Manager module and focuses on the role of Machine Learning (ML) techniques. Issues related to accuracy, run-time complexity and interpretability of ML outputs are described and coping strategies outlined.

We propose and demonstrate a set of microservice-based security components able to perform physical layer security assessment and mitigation in optical networks. Results illustrate the scalability of the attack detection mechanism and the agility in mitigating attacks.

TeraFlow proposes a new type of secure, cloudnative Software Defined Networking controller that will radically advance the state-of-the-art in beyond 5G networks by introducing novel micro-services architecture and providing revolutionary features for flow management and optical/microwave network