Techno-Economic Impact of Filterless Data Plane and Agile Control Plane in the 5G Optical Metro

Abstract

Optical metro networks evolution driven by 5G requirements face enormous challenges. Network functions virtualized in the data centers spread to the metro nodes, IP, and optical technologies must cooperate to meet the metro traffic aggregation role. Multiple technological options exist, and carriers confront the need to economically assess them, benchmarked in realistic deployments. This paper gives relevant insights to this aim. We first construct a set of metro network benchmarks. A strategic and distinctive effort is made to incorporate metro WDM topologies, traffic profiles and daily variation patterns, fault-tolerance requisites, and network operational choices, that faithfully reflect the expected 5G metro progression for a national carrier. Then, we use these networks to assess two technological choices. On one hand, the cost-effectiveness limits in terms of CAPEX reductions and energy efficiency brought from the possibility of having an agile control plane in the metro, capable of on-demand instantiation of IT and network resources. On the other hand, we investigate the benefits of replacing ROADMs by more cost-effective filterless technologies, but just limiting this replacement to degree-1 and degree-2 optical nodes, that are prevalent (e.g. >50%) in regional metro topologies. A novel capacity planning algorithm has been developed for IT, IP and optical resources allocation and dimensioning, providing fault-tolerant designs for the realistic scenarios defined. Simulation results have been obtained using the Net2Plan NIW (NFV over IP over WDM) open-source framework. Developed algorithms and part of the testing scenarios are available for inspection in public repositories of the EU METRO-HAUL project, the umbrella for our work. Our results show CAPEX benefits in the order of 10% and energy savings in the order of 20-30% stemming from the on-demand resource allocation in the metro. In addition, degree 1 and degree 2 optical nodes have shown to be a sweet spot for applying filterless switching, with mitigated impact of the associated spectrum waste.

QoT-Aware Optical Amplifier Placement in Filterless Metro Networks

“…We consider the placement of two types of OAs that differ in terms of gain 2 (G) and noise figure (NF). Booster OAs are characterized by a G range of (10-20) dB and a NF range of (6-12) dB, while pre-amp OAs by a G range of (18-32) dB and a NF range of (6.2-10.5) dB 3 . We consider the placement of OAs in three possible locations: i) at the ingress of the node (for pre-amplifiers), ii) at the egress of the node (for boosters) and iii) along the fiber (for inline amplifiers, which have the same characteristics of boosters).…”

“…FONs have been already standardized and deployed as shown in [2]. In particular, FON deployment has attracted particular interest for metro networks [3] based on ring/horseshoe topologies. In metro, network operators, in the attempt to minimize network cost, are more prone to consider node architectures without WSSs, especially taking into account the high number of nodes in a metro network.…”

“…We assume a flat OA gain profile due to the use of gain flattening filters 3. Note that a high NF corresponds to a low G and vice-versa 4.…”

On Dynamic Service Chaining in Filterless Optical Metro-Aggregation Networks

“…Ref. [19] showed that further savings can be achieved due to lower cost of data and control planes for filterless networks. Ref.…”

INSPIRING-SNI: Investigating SDN Programmability Improving Optical South- and North-Bound Interfaces

“…NIW is a library added to the Net2Plan open-source network planning software [3], specifically to model, provision, design and evaluate SDN/NFV networks. NIW was used to generate two remarkable outcomes: a demonstration with a hands-on experience at OFC [24] and a comprehensive techno-economic study for metropolitan optical networks encompassing a realistic 5G traffic model with the evaluation of the filterless technology for the data plane and an agile control plane [25].…”