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.

Flexible optical networks (FONs) are being adopted to accommodate the increasingly heterogeneous traffic in today’s Internet. However, in presence of high traffic load, not all offered traffic can be satisfied at all time. As carried traffic load brings revenues to operators, traffic blocking due to limited spectrum resource leads to revenue losses. In this study, given a set of traffic requests to be provisioned, we consider the problem of maximizing operator’s revenue, subject to limited spectrum resource and physical layer impairments (PLIs), namely amplified spontaneous emission noise (ASE), self-channel interference (SCI), cross-channel interference (XCI), and node crosstalk. In FONs, adaptive modulation, multiple FEC, and the tuning of power spectrum density (PSD) can be effectively employed to mitigate the impact of PLIs. Hence, in our study, we propose a universal bandwidth-related impairment evaluation model based on channel bandwidth, which allows a performance analysis for different PSD, FEC and modulations. Leveraging this PLI model and a piecewise linear fitting function, we succeed to formulate the revenue maximization problem as a mixed integer linear program. Then, to solve the problem on larger network instances, a fast two-phase heuristic algorithm is also proposed, which is shown to be near-optimal for revenue maximization. Through simulations, we demonstrate that using adaptive modulation enables to significantly increase revenues in the scenario of high signal-to-noise ratio (SNR), where the revenue can even be doubled for high traffic load, while using multiple FECs is more profitable for scenarios with low SNR.

Accurately constructing dynamic network topology is one of the core tasks to provide on-demand security services to the ubiquitous network. Existing schemes cannot accurately construct dynamic network topologies in time. In this paper, we propose a novel scheme to construct the ubiquitous network topology. Firstly, ubiquitous network nodes are divided into three categories: terminal node, sink node, and control node. On this basis, we propose two operation primitives (i.e., addition and subtraction) and three atomic operations (i.e., intersection, union, and fusion), and design a series of algorithms to describe the network change and construct the network topology. We further use our scheme to depict the specific time-varying network topologies, including Satellite Internet and Internet of things. It demonstrates that their communication and security protection modes can be efficiently and accurately constructed on our scheme. The simulation and theoretical analysis also prove that the efficiency of our scheme, and effectively support the orchestration of protection capabilities.

In presence of a high traffic load, not all offered traffic can be satisfied at all time. Luckily, several kinds of services (e.g. video, file transfer) are elastic because they can be degraded and accepted with a lower bit-rate, corresponding to a lower quality of service (QoS) level. In this paper, we aim at maximizing network revenue leveraging elastic service provisioning. To tackle this problem, we propose an integer linear programming model as well as a decomposition method, which selects a QoS level and provisions a lightpath for elastic request. Meanwhile, in order to exploit the spectrum resources of elastic services under a progressive network load, we propose an auto-degrading provisioning scheme to trigger the network reconfiguration for existing lightpaths in an effort to increase the total network revenue. Simulation results validate the revenue improvement by supporting elastic service provisioning scheme in the scenarios of static and progressive network load.

Natural disasters have challenged the survivability of Elastic Optical Inter-DataCenter Networks (EO-DCNs), and it is urgent to establish efficient disaster protection schemes. In this paper, we investigate the disaster-resilient service provisioning problem leveraging cooperative storage system (CSS). Instead of mirrored content backup on a single DC, our proposed CSS partitions a required content into no less than three fragments if possible, each of which is then stored on a DC located in different disaster zones. Accordingly, multi-path routing with the adaptive number of working paths to distinct DCs is employed to serve each request, while a protection path is computed to protect against a disaster failure. Our main objective is to jointly minimize the spectrum usage and maximal occupied frequency slot index (MOFI) subject to disaster resilience. Besides, we also expect to cut the content storage space. To this end, we propose for the first time a CSS-based dedicated end-to-content path protection (CDP), which allows service provisioning through multiple paths with the adaptive number of paths rather than a single path. This consequently reduces at least half of the reserved spectrum on the protection path. To find the optimal CDP strategy, we formulate the studied problem as an integer linear program (ILP) and then propose a fast heuristic algorithm. Observing the trade-off between the spectrum usage and content storage space, we further design a maximum-CDP (M-CDP), which generates the maximum number of working paths to reduce the content storage space. Simulations are conducted to compare the proposed schemes with the traditional protection strategy using mirrored storage and single-path routing. Numerical results demonstrate that the proposed CSS-based protection schemes enable to cut up to 21.6% of the spectrum usage and 15% of the content storage space.

Natural disasters have challenged the survivability of Elastic Optical Inter-DataCenter Networks (EO-DCNs), and it is urgent to establish efficient disaster protection schemes. In this paper, we investigate the disaster-resilient service provisioning problem leveraging cooperative storage system (CSS) and multipath routing. The studied problem involves data center (DC) assignment, content partition and placement, working/protection paths computation, as well as spectrum allocation. Our main objective is to jointly minimize the spectrum usage and maximal frequency slot index. Besides, we also expect to cut the content storage space. To this end, we first formulate the studied CSS-based protection problem as an integer linear program (ILP), and then propose a fast heuristic algorithm to improve the network scalability in large instances. Numerical simulations are conducted to compare the proposed schemes with the traditional protection strategy using entire content replication and single path routing. Simulation results demonstrate that the CSS-based protection scheme enables to cut up to 17.8% of the spectrum usage and half of the content storage space.