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Multi-band transmission is a promising technical direction for spectrum and capacity expansion of existing optical networks. Due to the increase in the number of usable wavelengths in multi-band optical networks, the complexity of resource allocation problems becomes a major concern. Moreover, the transmission performance, spectrum width, and cost constraint across optical bands may be heterogeneous. Assuming a worst-case transmission margin in U, L, and C-bands, this paper investigates the problem of throughput maximization in multi-band optical networks, including the optimization of route, wavelength, and band assignment. We propose a low-complexity decomposition approach based on Column Generation (CG) to address the scalability issue faced by traditional methodologies. We numerically compare the results obtained by our CG-based approach to an integer linear programming model, confirming the near-optimal network throughput. Our results also demonstrate the scalability of the CG-based approach when the number of wavelengths increases, with the computation time in the magnitude order of 10 s for cases varying from 75 to 1200 wavelength channels per link in a 14-node network. Code of this publication is available at github.com/cchen000/CG-Multi-Band. 

Abstract Efficient network management in optical backbone networks is essential to manage continuous traffic growth. To accommodate this growth, network operators need to upgrade their infrastructure at appropriate times. Given the cost constraint of upgrading the entire network at once, upgrading the network periodically in multiple batches is a more pragmatic approach to meet the growing demands. While multi-period, batch-upgrade strategies to increase network capacity from the conventional C band to C+L bands have been proposed, they did not consider so far the possibility to re-provision existing traffic. In this work, we investigate how to selectively re-provision connections from C band to L band during a batch upgrade. This is to ensure greater availability of C-band resources which can help to delay network upgrade and hence reduce upgrade cost, while limiting the number of disrupted connections in the network. This study proposes two re-provisioning strategies, namely, Budget-Based (BB) and Margin-Aware (MA) re-provisioning, which rely on the Quality of Transmission (QoT) of lightpaths. These strategies leverage the knowledge of Generalized Signal-to-Noise Ratio (GSNR) to choose which lightpaths to re-provision. We compare these strategies with a baseline distance-based strategy that uses path length to select and re-provision lightpaths. We also incorporate Machine Learning techniques for QoT estimation of lightpaths to reduce the computational time required for optical-path feasibility check. Numerical results show that, compared to distance-based strategy, BB and MA strategies reduce disruption by about 22% and 27%, respectively, in representative network topologies. 

Large-scale carrier networks are fundamental ICT infrastructures that support future 5G/6G services, and their resilience is a primary societal concern. Differently from single-carrier networks (in which one carrier owns multiple networks), in multi-carrier network ecosystems (in which the networks in the fields are operated by different carriers), cooperation among such different carriers is crucial to achieve resilience against large-scale failures. However, such cooperation is challenging since carriers may not disclose confidential information, e.g., detailed resource availability. In this study, we investigate how to perform carrier cooperative recovery in the case of large-scale failures/disasters. We propose two-stage carrier-carrier cooperative recovery planning by incorporating a coordinated scheduling for faster recovery. Through numerical evaluation, we confirm the potential benefit of carrier cooperation in terms of both recovery time and recovery cost reduction. 

Multi-band transmission is a promising solution for capacity enhancement in optical networks. We propose a novel strategy, named C to C+L Upgrade (CLU), to gradually upgrade links from C to C+L bands. We develop a Recurrent Neural Network (RNN)-based model to efficiently predict links for upgrade, based on network state and resource utilization, to reduce blocking and upgrade cost. Our results show that CLU outperforms baseline strategies (which do not employ predictive decisions) by upgrading fewer links at appropriate times. 

To accommodate the growing demand for cloud services, telecom carriers’ networks and datacenter (DC) facilities form large network–cloud ecosystems (ecosystems for short) physically supporting these services. These large-scale ecosystems are continuously evolving and must be highly resilient to support critical services. Open and disaggregated optical-networking technologies promise to enhance the interoperability across telecom carriers and DC operators, thanks to their open interfaces in both the data plane and control/management plane. In the first part of this paper, we focus on a single entity (e.g., a telecom carrier or an emerging telecom/DC partnership company) that owns both the network and DC infrastructures in the ecosystem. We introduce a solution by leveraging open and disaggregated technologies to enhance the resilience of the optical networks within a multi-vendor and multi-domain ecosystem. In the second part of this paper, we consider the case when the networks and DCs are owned by different entities. Also, in this case, cooperation among datacenter providers (DCPs) and carriers is crucial to provide failure/disaster resilience to today’s cloud services. However, such cooperation is more challenging since DCPs and carriers, being different entities, may not disclose confidential information, e.g., detailed resource availability. Hence, we introduce a solution to enhance the resilience of such multi-entity ecosystems through cooperation between DCPs and carriers without violating confidentiality. 

We investigate optimized placement of hybrid EDFA/Raman amplifiers in (C+L) networks to avoid lightpath degradation due to ISRS. We numerically compare eight strategies for amplifier deployment showing that an optimized placement of Raman amplification can lead to 40% fewer amplifiers compared to baseline deployment practices. 

We investigate the problem of enhancing the resilience of future optical network-cloud ecosystems. We introduce new solutions to build disaster-resilient single-and multi-entity network-cloud ecosystems with openness, disaggregation, and cooperation between networks and clouds.