In optical transport networks the quality of transmission (QoT) is estimated before provisioning new connections or upgrading existing ones. Traditionally, a physical layer model (PLM) is used for QoT estimation coupled with high margins to account for the model inaccuracy and the uncertainty in the evolving physical layer conditions. Reducing the margins increases network efficiency but requires accurate QoT estimation. We present two machine learning (ML) approaches to formulate such an accurate QoT estimator. We gather physical layer feedback, by monitoring the QoT of existing connections, to understand the actual physical conditions of the network. These data are used to train either the input parameters of a PLM or a machine learning model (ML-M). The proposed ML methods account for variations and uncertainties in equipment parameters, such as fiber attenuation, dispersion, and nonlinear coefficients, or amplifier noise figure per span, which are typical in deployed networks. We evaluated the accuracy of the proposed methods under various uncertainty scenarios and compared them to QoT estimators proposed in the literature. The results indicate that our estimators yield excellent accuracy with a relatively small amount of data, outperforming other prior estimators.

We develop a framework for estimating the quality of transmission (QoT) of a new lightpath before it is established, as well as for calculating the expected degradation it will cause to existing lightpaths. The framework correlates the QoT metrics of established lightpaths, which are readily available from coherent optical receivers that can be extended to serve as optical performance monitors. Past similar studies used only space (routing) information and thus neglected spectrum, while they focused on old-generation noncoherent networks. The proposed framework accounts for correlation in both the space and spectrum domains and can be applied to both fixed-grid wavelength division multiplexing (WDM) and elastic optical networks. It is based on a graph transformation that exposes and models the interference between spectrum-neighboring channels. Our results indicate that our QoT estimates are very close to the actual performance data, that is, to having perfect knowledge of the physical layer. The proposed estimation framework is shown to provide up to $4\times {10}^{-2}$ lower pre-forward error correction bit error ratio (BER) compared to the worst-case interference scenario, which overestimates the BER. The higher accuracy can be harvested when lightpaths are provisioned with low margins; our results showed up to 47% reduction in required regenerators, a substantial savings in equipment cost.

Optical networks are designed to be operated statically: lightpaths are provisioned for uninterrupted operation for several years using high margins to anticipate the deterioration of their quality of transmission (QoT) from various factors (equipment aging, malfunctioning, and maintenance operations). Operating the network dynamically and closer to its actual capabilities increases efficiency and reduces capital expenditure. We develop a cross-layer QoT-aware toolkit that leverages monitoring information and the flexibility dimensions of elastic optical networks. It adapts the network’s parameters and regulates the QoT to achieve high efficiency. The toolkit can be used in a plethora of use cases in the deployment or during the operation phase of the network, e.g., to harvest the excessive margins when lightpaths are initially deployed or failures are repaired, to adapt the network to changing traffic demands, and to restore margins when soft failures such as equipment malfunction or aging render the QoT of certain lightpaths unacceptable. In the last case, the toolkit can be used to appropriately reconfigure the lightpaths to restore their QoT and postpone the deployment of regenerators, as indicated by our simulations.

Networks are always evolving to meet the needs of progressing and novel applications. To this end, improved network capacity, latency and security are required. Monitoring is key to achieving these objectives. It is a cornerstone for the uninterrupted network operation and the applications’ QoS management. Network tomography uses a subset of monitoring information, corresponding to partial view of the network state, to estimate wide-sense network performance metrics. In this paper, we present a novel machine learning (ML) formulation for Network Tomography. It is novel in that its features are designed under the assumption that: (i) the existence of certain links of the network is not known (e.g., due to security reasons), (ii) the routing is dynamic (non-deterministic), i.e., for the same origin–destination node pair, a different route may be selected depending on the state of certain links. These assumptions are typically present in modern networks. The formulation can be used to estimate both additive and non-additive performance metrics. We evaluate our proposal using different ML algorithms: neural networks (NNs), Gaussian regression and linear regression with interactions. Our simulations indicate that our ML formulation has better estimation accuracy compared to traditional algebraic or other ML approaches that cannot or do not take into account these two hypotheses. Regarding the accuracy of the examined ML algorithms, the differences mainly depend on the additive or not nature of the network metrics.

We present a self-configured and unified access and metro network architecture, named ARMONIA. The ARMONIA network monitors its status, and dynamically (re-)optimizes its configuration. ARMONIA leverages software defined networking (SDN) and network functions virtualization (NFV) technologies. These technologies enable the access and metro convergence and the joint and efficient control of the optical and the IP equipment used in these different network segments. Network monitoring information is collected and analyzed utilizing machine learning and big data analytics methods. Dynamic algorithms then decide how to adapt and dynamically optimize the unified network. The ARMONIA network enables unprecedented resource efficiency and provides advanced virtualization services, reducing the capital expenditures (CAPEX) and operating expenses (OPEX) and lowering the barriers for the introduction of new services. We demonstrate the benefits of the ARMONIA network in the context of dynamic resource provisioning of network slices. We observe significant spectrum and equipment savings when compared to static overprovisioning.