GNPy is an open source project of the Telecom Infra Project (TIP). It aims at the development of a software library for the abstraction of physical layer data transport, implemented as wavelength division multiplexing (WDM) through open-source code and open APIs, with the purpose of automatizing disaggregated, multi-vendor open optical networks. We first introduce the GNPy library of applications and functionalities, then, we present validation of the GNPy core. The quality-of-transmission estimator (QoT-E) enables a quick and accurate prediction of the unique performance metric in WDM data transport of optical networks: the Generalized Signal-to-Noise Ratio (GSNR). The presented validation experiments show an excellent accuracy in predicting the GSNR compared to the values obtained experimentally. Finally, we present the GNPy integration with the ONOS network OS in a multi-vendor scenario including open reconfigurable add drop multiplexers (ROADMs) and the related validation experiment.

The difficulty of building large data centers in dense metro areas is pushing big cloud providers towards a different approach to scaling: multiple smaller data centers within tens of kilometers of each other, comprising a "region". We show that networking this small number of nearby sites with each other is a surprisingly challenging and multi-faceted problem. We draw out the operational goals and constraints of such networks, and highlight the design trade-offs involved using data from Microsoft Azure's regions.

Fiber optic cables connecting data centers are an expensive but important resource for large organizations. Their importance has driven a conservative deployment approach, with redundancy and reliability baked in at multiple layers. In this work, we take a more aggressive approach and argue for adapting the capacity of fiber optic links based on their signal-to-noise ratio (SNR). We investigate this idea by analyzing the SNR of over 8,000 links in an optical backbone for a period of three years. We show that the capacity of 64% of 100 Gbps IP links can be augmented by at least 75 Gbps, leading to an overall capacity gain of over 134 Tbps. Moreover, adapting link capacity to a lower rate can prevent up to 25% of link failures. Our analysis shows that using the same links, we get higher capacity, better availability, and 32% lower cost per gigabit per second. To accomplish this, we propose RADWAN, a traffic engineering system that allows optical links to adapt their rate based on the observed SNR to achieve higher throughput and availability while minimizing the churn during capacity reconfigurations. We evaluate RADWAN using a testbed consisting of 1,540 km fiber with 16 amplifiers and attenuators. We then simulate the throughput gains of RADWAN at scale and compare them to the gains of state-of-the-art traffic engineering systems. Our data-driven simulations show that RADWAN improves the overall network throughput by 40% while also improving the average link availability.

Fiber optic cables are the workhorses of today's Internet services. Operators spend millions of dollars to purchase, lease and maintain their optical backbone, making the efficiency of fiber essential to their business. In this work, we make a case for adapting the capacity of optical links based on their signal-to-noise ratio (SNR). We show two immediate benefits of this by analyzing the SNR of over 2000 links in an optical backbone over a period of 2.5 years. First, the capacity of 80% of IP links can be augmented by 75% or more, leading to an overall capacity gain of 145 Tbps in a large optical backbone in North America. Second, at least 25% of link failures are caused by SNR degradation, not complete loss-of-light, highlighting the opportunity to replace link failures by link flaps wherein the capacity is adjusted according to the new SNR. Given these benefits, we identify the disconnect between current optical and networking infrastructure which hinders the deployment of dynamic capacity links in wide area networks (WANs). To bridge this gap, we propose a graph abstraction that enables existing traffic engineering algorithms to benefit from dynamic link capacities. We evaluate the feasibility of dynamic link capacities using a small testbed and simulate the throughput gains from deploying our approach.