Next Challenges in Optical Networking Research: Contribution from the CaON cluster Dimitra Simeonidou: Sergi Figuerola:

The CaON Vision of Future Optical Networks  Application driven and technology enabled High-speed data 400G, 1Tb/s Residential Media Flexible Network MULTI-BAND SSS Technology Enabled Application Driven Intelligent Adaptive Optical Networks MULTI-BAND AMPLIFIER FAST OPTICAL SWITCH SDM (DE)MUX MULTI-BAND SSS BROADBAND λ-CONVERSION Elastic use of resources Flexible use of technology Cloud

The CaON Reference model I  CaON reference model presents a layered architecture linking optical networks with future services and applications  The model promotes the convergence of the optical infrastructure layers with upper layers and aims to strategically position optical networks as key enabler of Future Internet and cloud networking service deployment

The CaON Reference model II  A bottom-up reference model, where the infrastructure and provisioning layers, together with cross-layer SLA and the management, are identified the key focus for future research trends within the CaON cluster community.  The physical infrastructure layer covers from the core to the access optical network technologies.

Key Research Challenges for Realizing the CaON Reference Model –Support for Multi-gigabit Access Rates (FP7 ALPHA, OASIS) –Spectrum management: Flexible, Elastic Optical Layer (FP7 STRONGEST, FP7 call 8 IDEALIST) Architectures on Demand –Control Plane (FP7 MAINS and STRONGEST) Targeted extensions for dynamic and data plane-aware network services –Software/Hardware Defined Network Programmability (FIRE OFELIA and FIRE call 8 ALIEN) For infrastructure and service adaptation –Optical Network and IT Convergence (FP7 GEYSERS) Infrastructure Virtualisation, Slicing and Isolation –Optical Network Cognition (FP7 CHRON, UK EPSRC Photonics HyperHighway) –Energy Efficient Optical Networks (FP7 STRONGEST and TRENT)

 Flexible allocation of resources in time and frequency in order to: –Accommodate applications with arbitrary requirements Spectrum Management: Elastic Resource Allocation Video conference/Virtual Presence Education/Remote Learning High-speed data transmission 400G, 1T Gaming

Elastic Time and Frequency plus Space Allocation  Elastic frequency allocation to enable: –Support for high-speed channels with arbitrary bandwidth requirements –Better spectral efficiency for lower bit rates  Elastic time allocation for: –Efficient all-optical switching of sub-wavelength traffic –Finer all-optical bandwidth granularities Continuous channels at various bit-rates User traffic at various bit-rates and modulation formats Space Novel Fibres and Fibre-based components

Optical Networks on Demand  Adapt to traffic profile  Support arbitrary switching-granularity  Dynamic Infrastructure Composition (including VI)  Dynamic architecture reconfiguration  Modular infrastructure planning  Seamless integration with other technology domains (network + IT)  Hitless upgrade with new functionality –Wavelength conversion –Regeneration –Optical signal processing –Space division multiplexing (multi-core, multimode) –Quantum technologies –Other?

Support of Multi-Gbps Access Rates:  Acceleration of access deployment through –Reduced total cost of ownership –Converged solutions supporting transport of mobile and fixed traffic in both front- and backhaul scenarios  Seamless integration of access and metro/aggregation –Unified control and management planes –Virtualization and context-aware networking  New solutions for simultaneous: –More users per feeder (>1000) –Higher speeds (up to 10 Gb/s peak) –longer reach (100 km)  Green and fast (1 Gb/s and beyond) home networking

Optical network control plane:  Main research challenges include –True multi-vendor and multi-carrier control plane solutions, including extensions for elastic technologies –Split architectures that decouple the control plane from the optical transport OpenFlow as an open/vendor-independent interface to network data plane Multi-technology and multi-domain path computation services coupled with traffic optimization Software Defined Networking at large –Control plane interfaces to external end-user “systems” (e.g. clouds) for any type of bandwidth-on-demand service and seamless integration with the service layer workflows.

Optical Network and IT Convergence: for High Performance, Global Reach Clouds  Provisioning over hybrid infrastructures composed of both IT resources (i.e. compute, storage, data centres) and optical networks  It will require : –Virtualise the physical optical network infrastructure (analogue or digital) –Federate heterogeneous resources from different providers –Unified management and provisioning procedures for the whole integration with the IT network infrastructures

Specific Issues in Optical Network Virtualization  Optical networks are analogue in nature –More complexity than L2/L3 (digital domain) virtualization as a result of physical layer impairments and constraints –Slice isolation is a big challenge in optical networks  Physical layer impairments –Affect the isolation between VIs –Newly composed VIs will affect the existing ones –Affect the ultimate feasibility of VIs  Wavelength continuity constraint –Affect the network resource utilization  Can we use new infrastructure capabilities such as Space Division Multiplexing (multi- core?)

Cognitive, self managed optical networks:  Dynamically re-purpose, evolve, self-adapt and self-optimize functions/devices/systems of the optical network. –Optical/opto-electronic technologies that would allow for environment-aware systems that can change any parameter based on interaction with the environment with or without user assistance –Cognitive control and management plane for dynamic infrastructure self- adaptation across heterogeneous systems.

Energy efficient optical networking:  Improve the design, planning and operations for energy aware management capable of 100 times energy consumption reduction –Introduction of new simpler protocols –Definition of energy friendly resilience –Support of planning and routing algorithms  Focus on energy efficient optical network services for applications such as P2P, grid or cloud services