Spectrally/spatially flexible optical networks (SS-FONs) are a promising solution to future traffic requirements in optical backbone networks. SS-FONs exploit the spatial dimension to increase the network capacity, while preserving the resource management flexibility, as they still operate within a flex-grid composed of small frequency slots (slices). A flex-grid makes it possible to realize transmission using super-channels (SChs) that comprise a set of contiguous slots. In this paper, we focus on spectral SChs, i.e., a SCh realized only on one spatial mode on each fiber. In SS-FONs, setting up and tearing down multiple lightpath requests within a flex-grid may result in spectrum fragmentation and, in turn, blocking of requests. In this work, we investigate several fragmentation metrics in a SS-FON. The problem of identifying appropriate metrics to measure fragmentation has been investigated in single-core elastic optical networks, but to the best of our knowledge, there is no such research available for SS-FONs. Therefore, we propose several fragmentation metrics for SS-FONs. We introduce the concept of bordering super-channels (B-SChs), i.e., SChs whose spectrum is allocated at the border of already allocated spectrum slots, which show promising results with regard to minimizing fragmentation. The investigation of all candidate B-SChs allows us to find the one that minimizes the network fragmentation and, in turn, spectrum waste. Hence, we propose a fragmentation-aware (FA) algorithm with bordering SChs that assigns optical resources to dynamic requests by utilizing information from the proposed fragmentation metrics and the set of candidate B-SChs. Experiments on a representative network topology show that the investigation of multiple B-SChs in the FA algorithm reduces the blocking probability when compared to the reference FA algorithms. Finally, we analyze the impact of a spatial continuity constraint on the network fragmentation.

Both multi-band and space division multiplexing (SDM) independently represent cost-effective approaches for next-generation optical backbone networks, particularly as data exchange between core data centers reaches the petabit-per-second scale. This paper focuses on different strategies for implementing band and SDM elastic optical network (BSDM EON) technology and analyzes the total network capacity of three sizes of backbone metro-core networks: ultra-long-, long-, and medium-distance networks related to the United States, Japan, and Spain, respectively. Two BSDM strategies are considered, namely, multi-core fibers (MCFs) and BSDM based on standard single-mode fiber (SSMF) bundles of multi-fiber pairs (BuMFPs). For MCF-based BSDM, we evaluated the performance of four manufactured trench-assisted weakly coupled (TAWC) MCFs with 4, 7, 13, and 19 cores. Simulation results reveal that, in the regime of ultra-low (UL) loss and inter-core crosstalk (ICXT), MCF-based throughput can be up to 14% higher than SSMF BuMFP-based BSDM when the core pitch exceeds 43 µm and the loss coefficient is lower than that of standard single-mode fibers. However, increasing the number of cores with (non-)standard cladding diameters, UL loss, and ICXT coefficient is not beneficial. As core counts increase up to 13 for non-standard cladding diameters (<230µm), the core pitch and loss coefficient also increase, leading to degraded performance of MCF-based BSDM compared to SSMF BuMFP-based BSDM. The results indicate that, in scenarios with 19 MFPs, SSFM BuMFP-based BSDM outperforms 19-core MCF-based scenarios, increasing the throughput by 55% to 73%, from medium-backbone networks to ultra-long ones.

The overall network volume in backbone optical networks is constantly growing, composed of many smaller network services with increasing trends and various seasonality. Due to the recent advances in machine learning algorithms, short- and long-term traffic fluctuations can be forecasted. Consequently, the backbone optical network can be adapted to traffic changes aiming to improve its performance. However, an important challenge lies in developing effective optimization methods capable of adapting to traffic changes to leverage the knowledge about the traffic. To this end, this paper addresses the time-varying traffic in spectrally-spatially flexible optical networks (SS-FONs), which are a promising technology to mitigate backbone network requirements of vast traffic volume transmission. The main contribution of this paper is twofold. Firstly, we introduce a new traffic prediction method using multioutput regression and temporal features to forecast traffic between all node pairs and integrate this prediction method into an optimization framework developed for dynamic resource allocation in translucent SS-FONs with time-varying traffic. Secondly, we evaluate potential network performance improvements from periodic lightpath reallocation through extensive numerical experiments. According to the results of experiments run on two representative optical network topologies, the proposed approach with periodic resource allocation allows achieving up to 7.8 percentage points reduction of bandwidth blocking compared to the reference scenario without reallocation. Consequently, the network requires up to 23.4% fewer transceivers to deliver the same traffic in considered scenarios and thus the power consumption savings are provided.