ADRENALINE Testbed ® - Experimental research testbed on high-performance and large-scale intelligent optical transport networks

ADRENALINE FEDER
Start: 01 June 2002
End: 31 December 2017
Funding: Internal
Status: Ongoing
Division: Communication Networks
Department: Optical Networks & Systems (ONS)
Web:  http://networks.cttc.es/ons/adrenaline/

The ADRENALINE testbed® (originally an acronym standing for All-optical Dynamic REliable Network hAndLINg IP/Ethernet Gigabit traffic with QoS)  has evolved from being a GMPLS-enabled Intelligent Optical Network – developed by the Optical Networking research area of the CTTC, see Fig. 1 (*) —  towards an advanced experimental research on high-performance and large-scale intelligent optical transport networks, consisting of a SDN/NFV Cloud Computing Platform and Core Network for 5G Services and a EOS Experimental Platform for Optical OFDM Systems. The project has moved to http://networks.cttc.es/ons/adrenaline/

(*) The ADRENALINE architecture was conceived to jointly address the ever-growing and bursty bandwidth demands of IP data traffic, and the need of replacing the SONET/SDH nodes in transport core networks with cost-efficient solutions. The architecture leverages Ethernet circuit-oriented technologies (enabling flexible packet aggregation and grooming) along with the high-bandwidth offered by the reconfigurable wavelength-routed network functionality (providing end-to-end all-optical connections), both controlled by the intelligence provided by the GMPLS architecture and framework. In the ADRENALINE testbed, we experimentally assess the application of a unified GMPLS control plane, in which Ethernet and optical layers are controlled by a single instance of the control plane, seamlessly providing reconfigurable Ethernet connections over dynamic lightpaths. Advanced and adapted software applications and tools for experimental research have also been designed and deployed, allowing the rapid operation and maintenance of the testbed, including common tasks such as the configuration and parameterization of the network topology, the generation of client requests modelling the behaviour of network customers, or the monitoring, data-mining and statistical processing of obtained results allowing researchers to obtain numerical performance data and to perform experimental research and quantitative comparative analysis.