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A fine-grained flexible frequency grid for elastic optical transmission and space division multiplexing in conjunction with spectrally efficient modulations is an excellent solution to the coming capacity crunch. In space division multiplexed elastic optical networks (SDM-EONs), the routing, modulation, core, and spectrum assignment (RMCSA) problem is an important lightpath resource assignment problem. Intercore cross talk (XT) reduces the quality of parallel transmissions on separate cores, and the RMCSA algorithm must ensure that XT requirements are satisfied while optimizing network performance. There is an indirect trade-off between spectrum utilization and XT tolerance; while higher modulations are more spectrum efficient, they are also less tolerant of XT since they permit fewer connections on neighboring cores on the overlapping spectra. Numerous XT-aware RMCSA algorithms restrict the number of litcores, cores on which overlapping spectra are occupied, to guarantee XT constraints are met. In this paper, we present a machine learning (ML) aided threshold optimization strategy that enhances the performance ofanyRMCSA algorithm for any network model. We show that our strategy applied to a few algorithms from the literature improves the bandwidth blocking probability by up to three orders of magnitude. We also present the RMCSA algorithm called spectrum-wastage-avoidance-based resource allocation (SWARM), which is based on the idea of spectrum wastage due to spectrum requirements and XT constraints. We note that SWARM not only outperforms other RMCSA algorithms, but also its ML-optimized variant outperforms other ML-optimized RMCSA algorithms. 

Service provisioning can be enhanced with spectrally spatially flexible optical networks (SS-FONs) with multicore fibers; however, intercore crosstalk (XT) is a dominant impairment that complicates the problem of maintaining the quality of transmission (QoT) and resource allocation. The selection of modulation formats (MFs), due to their unique XT sensitivities, further increases the complexity. The routing, modulation, core, and spectrum assignment (RMCSA) problem must select the resources carefully to exploit the available capacity while meeting the desired QoT. In this paper, we propose an RMCSA algorithm called the tridental resource assignment (TRA) algorithm for transparent SS-FONs, and its variant, translucency-aware TRA (TaTRA), for translucent SS-FONs. TRA balances three different factors that affect network performance under dynamic resource allocation. We consider translucent networks with flexible regeneration and with and without modulation and spectrum conversion. Our resource assignment approach includes both an offline network planning component to calculate path priorities and an online/dynamic provisioning component to allocate resources. Extensive simulation experiments performed in realistic network scenarios indicate that TRA and TaTRA significantly reduce the bandwidth blocking probability by several orders of magnitude in some cases.