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We address the problem of efficient end-to-end network monitoring of path metrics in communication networks. Our goal is to minimize the number of measurements or monitors required to maintain an acceptable estimation accuracy. We present a framework based on diffusion wavelets and nonlinear estimation. Our procedure involves the development of a diffusion wavelet basis that is adapted to the monitoring problem. This basis exploits spatial and temporal correlations in the measured phenomena to provide a compressible representation of the path metrics. The framework employs nonlinear estimation techniques using ℓ 1 minimization to generate estimates for the unmeasured paths. We describe heuristic approaches for the selection of the paths that should be monitored, or equivalently, where hardware monitors should be located. We demonstrate how our estimation framework can improve the efficiency of end-to-end delay estimation in IP networks and reduce the number of hardware monitors required to track bit-error rates in all-optical networks (networks with no electrical regenerators).

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The estimation of the quality of transmission (QoT) in optical systems with machine learning (ML) has recently been the focus of a large body of research. We discuss the sources of inaccuracy in QoT estimation in general; we propose a taxonomy for ML-aided QoT estimation; we briefly review ML-aided optical performance monitoring, a tightly related topic; and we review and compare all recently published ML-aided QoT articles.

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Transparent optical networks are the enabling infrastructure for converged multi-granular networks in the Future Internet. The cross-layer planning of these networks considers physical impairments in the network layer design. This is complicated by the diversity of modulation formats, transmission rates, amplification and compensation equipments, or deployed fiber links. Thereby, the concept of Quality of Transmission (QoT) attempts to embrace the effects of the physical layer impairments, to introduce them in a multicriterium optimization and planning process. This paper contributes in this field by the proposal and comparative evaluation of two novel offline impairment aware planning algorithms for transparent optical networks, which share a common QoT evaluation function. The first algorithm is based on an iterative global search driven by a set of binary integer linear programming formulations. Heuristic techniques are included to limit the binary programming complexity. The second algorithm performs different pre-orderings of the lightpath demand, followed by a sequential processing of the lightpath demands. The performance and the scalability of both approaches are investigated. Results reveal great scalability properties of the global search algorithm, and a performance similar to or better than the sequential schemes.