TeraFlow proposes a novel secured transport Software Defined Networking (SDN) controller based on a microservice architecture. The objective is to foster innovation around SDN controller and evolve them to be suitable for beyond 5G networks. This paper presents two TeraFlow scenarios that involve automated network management to demonstrate its feasibility. The first scenario focuses on the necessary transformation of a network operator to support beyond 5G technologies.

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 equipment integration. Two key contributions of this project will be the incorporation by design in the TeraFlow controller of (i) security using advanced Machine Learning (ML) techniques both in the data and control planes and (ii) forensic evidence for multitenancy based on Distributed Ledgers.

TeraFlow proposes a new type of secure, cloud-native Software Defined Networking (SDN) controller that will radically advance the state-of-the-art in beyond 5G networks by introducing novel micro-services architecture, and provide revolutionary features for both flow management (service layer) and optical/microwave network equipment integration (infrastructure layer) by adapting new data models. TeraFlow will also incorporate security using Machine Learning (ML) and forensic evidence for multi-tenancy based on Distributed Ledgers.

We present and demonstrate service provisioning in partially disaggregated multi-vendor network automation scenarios with online physical impairment validation. This work uses and extends standard interfaces (OpenConfig and ONF Transport API) to retrieve network information interacting with TIP GNPy tool.

This demo shows a cloud-native SDN controller that can estimate end-to-end QoT using both analytical (GNPy) and machine learning algorithms on WDM systems. This transport SDN controller is able to scale horizontally using custom metrics.

Hierarchical Software Define Networking (SDN) architectures is used to manage the co-existence of multiple domains by having an element on top. A collaborative relation-ship between domains, might solve this issue. Blockchain may become the key element for this change to happen. This paper presents a Blockchain-based architecture to provide SDN actions to configure connectivity services in transport domains. The results presented show that the use of Blockchain is a promising candidate for inter-domain SDN control.

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 validate the deployment of isolated transport network slices in IP over DWDM network. To this end, an isolated transport network slice is deployed using multi-layer isolation mechanisms based on OpenConfig and ONF Transport API.

We present a cloud-native architecture with a machine learning QoT predictor that enables cognitive functions in transport SDN controllers. We evaluate the QoT predictor training and auto-scaling capabilities in a real WDM/SDM testbed.

This paper presents and experimentally validates a Blockchain-based architecture to manage End-to-End Network Slice Deployment by stitching Network Slice Subnets in edge/cloud computing domains connected through packet/optical transport networks domains managed by different operators.