Automated End to End Carrier Ethernet Provisioning over a Disaggregated WDM Metro Network with a Hierarchical SDN Control and Monitoring Platform

This demo shows how a hierarchical control plane of ONOS SDN controllers orchestrates the dynamic provisioning of end-to-end Carrier Ethernet circuits on a composite network, programming the whole data path from the CPE to the core optical equipment

Dynamic (re)configuration of optical networks based on monitoring information: Field trial

We demonstrate dynamic reconfiguration based on an innovative control paradigm, named pre-programming. Experiment has been successfully carried on in a field trial at Telecom Italia

Toward efficient, reliable, and autonomous optical networks: The ORCHESTRA solution [Invited]

Optical networks have historically been designed to be operated statically. Connections are overprovisioned so that they remain uninterrupted over several (e.g., 10) years, using high physical-layer margins to cover the evolution of the physical conditions and modeling uncertainties. As a first step, we can increase the efficiency without sacrificing network reliability by removing uncertainties and reducing long-term margins, observing and adjusting them at intermediate periods. This requires certain automation steps in monitoring and data processing. Increasing the efficiency further, and thus further reducing the margins, comes at a trade-off in reliability, and should be done according to service classes and the level of network automation. The ORCHESTRA network makes use of coherent optical transponders as software-defined optical performance monitors (soft-OPMs) to improve the optical network observability. ORCHESTRA developed digital signal processing (DSP) OPM algorithms and a hierarchical monitoring plane to carry and process physical-layer monitoring data. ORCHESTRA uses data analytics methods to understand the physical-layer conditions and feed cross-layer optimization algorithms. ORCHESTRA closes the observe-decide-act control loop, automating the mechanisms required to trade efficiency for reliability

Pre-Emptive Detection and Localization of Failures Towards Marginless Operations of Optical Networks

Operating optical networks much closer to their physical capacity is very tempting but necessarily requires much improvement on the way failures are handled. In this paper, we experimentally demonstrate the ability of the ORCHESTRA solution for early detection and localization of failures, to proactively mitigate their impact and thus guarantee smooth operation without traffic interruption

Field trial evaluation and SDN configurability of an optical terabit transmitter based on passive polymer and InP technology

A tunable, bandwidth variable, SP-IQ (Single Polarization In Phase and Quadrature) transmitter based on the hybrid integration of passive polymer PolyBoards with InP photonic chips and InP-DHBT electronic chips having the potential for operation at rates up to 64 Gbaud is presented. Device transmission performance were evaluated in a field trial in TIM metro regional network, in a complex testing environment where TIM SDN controller performed transmitter configuration through a properly developed SDO (Software Defined Optics) agent and instantiated optical connections through WDM metro network

Marginless operation of optical networks

Considering flexible technologies available nowadays, operating optical networks much closer to their physical capacities is very tempting but necessarily requires efficient network automation. To achieve this, the two main challenges are handling failures, and accurately predicting performance in dynamic environments. We experimentally demonstrate the ability of the ORCHESTRA solution for early detection and localization of failures, to preventively mitigate their impact, and thus guarantee smooth network operation. Then, leveraging machine learning for live performance estimation and closed-loop software-defined network control, we demonstrate a fully automated reconfiguration of marginless connections undergoing critical performance variations over 228 km of field-deployed fiber

Field Trial of Marginless Operations of An Optical Network Facing Ageing and Performance Fluctuations

Leveraging machine learning for monitoring-based performance estimation and closed-loop SDN-based network control, we demonstrate the first fully automated reconfiguration of marginless connections undergoing critical performance variations over 228km of field-deployed fiber