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A 16-dimensional Voronoi constellation concatenated with multilevel coding is experimentally demonstrated over a 50km four-core fiber transmission system. The proposed scheme reduces the required launch power by 6dB and provides a 17dB larger operating range than 16QAM with BICM at the outer HD-FEC BER threshold., Comment: 4 pages, 4 figures, accepted by 2024 European Conference on Optical Communication (ECOC)

To cater to the demands of our rapidly growing Internet traffic, backbone networks need high-degree reconfigurable optical add/drop multiplexers (ROADMs) to simultaneously support multiple pairs of bi-directional fibers on each link. However, the traditional ROADM architecture based on the Spanke network is too complex to be directly scaled up to construct high-degree ROADMs. In addition, the widely deployed Spine-Leaf datacenter networks (DCNs) based on electrical switches consume too much power and exhibit high packet latency. Because of these issues, Clos networks are considered as promising alternatives for constructing large-scale ROADMs and all-optical DCNs. In this article, we look at a next-generation Clos-based ROADM architecture and show that it indeed provides better blocking performance with lower element and fiber complexities compared with a traditional Spanke-based ROADM architecture. We also discuss the application of a Clos network in all-optical DCNs to show that it can be used to effectively construct large-scale DCNs with significantly greater flexibility in supporting a variety of multicast services and in combining different network topologies., Comment: 7 pages, 6 figures

Virtual Data Center (VDC) embedding has drawn significant attention recently because of growing need for efficient and flexible means of Data Center (DC) resource allocation. Existing studies on VDC embedding mainly focus on improving DCs' resource utilization. However, an important problem that has not been considered in VDC embedding solutions is the creation of hot spots by excessive heat dissipation and hot air generation from racks in DCs, which have significant adverse effect on energy consumption of the cooling system and IT equipment lifespan. To address this issue, we propose a temperature-aware VDC embedding scheme to avoid hot spots by minimizing the maximum temperature of hot air emitted from each rack. Meanwhile, we also aim to reduce the total power consumption of IT equipment in this scheme. A Mixed Integer Linear Programming (MILP) model and a heuristic algorithm are developed to implement the proposed VDC embedding scheme. Numerical results show that the proposed temperature-aware embedding scheme can significantly outperforms a load-balanced embedding scheme in terms of maximum rack temperature, total power consumption of IT equipment, and VDC rejection ratio., Comment: 30 pages

In communication networks, network virtualization can usually provide better capacity utilization and quality of service (QoS) than what can be achieved otherwise. However, conventional resource allocation for virtualized networks would still follow a fixed pattern based on the predicted capacity needs of the users, even though, in reality, the actual traffic demand of a user will always tend to fluctuate. The mismatch between the fixed capacity allocation and the actual fluctuating traffic would lead to degradation of provisioned network services and inefficiency in the assigned network capacity. To overcome this, we propose a new spectrum trading (ST) scheme between virtual optical networks (VONs) in the context of an elastic optical network (EON). The key idea here is to allow different VONs to trade their spectrum resources according to the actual capacity they need at different time instants. A VON with unused spectra can then trade away its unused spectra to other VONs that are short of spectrum resources at that time. In exchange, it is rewarded with a certain amount of credit for its contribution to the ST community, which it can then use later to get extra bandwidth, if needed. The trust-worthiness of the trading records between the VONs is ensured in a distributed fashion through a blockchain-assisted account book that is updated whenever a new trade occurs. For this, we develop a software-defined control plane to enable spectrum trading in an EON. The performance of the ST scheme is evaluated and compared with a scenario without such trading. Our results show that the proposed ST scheme is effective in improving the QoS of each VON and significantly improves the overall network capacity utilization., Comment: 7 pages, 5 figures

Inter-core crosstalk is one of the most serious impairments for signal transmission in a multi-core fiber (MCF) optical network. On the other hand, because of wide deployment of data centers (DCs), we are seeing an increasing bidirectional traffic demand asymmetry, which leads to significant capacity wastage in designing and operating an optical transport network. To alleviate these effects, for an MCF optical network, we propose to assign fiber cores in an MCF in an asymmetric and counter-propagating manner. This can not only significantly reduce inter-core crosstalk between counter-propagating fiber cores but also flexibly assign different numbers of fiber cores in the opposite directions of a fiber link, thereby overcoming network capacity wastage due to the bidirectional traffic demand asymmetry. To evaluate the benefits of the proposed strategy, we consider the routing, spectrum, and core assignment (RSCA) problem for the MCF optical network. An integer linear programming (ILP) model and an auxiliary graph (AG) based heuristic algorithm are developed to optimize network spectrum resource utilization. Simulation studies show the effectiveness of the proposed core counter-propagation strategy, which can significantly outperform its counterpart, i.e., the co-propagation scheme, in terms of the total number of MCFs used and average inter-core crosstalk. In addition, the proposed RSCA heuristic algorithm is efficient to perform close to the ILP model, which can minimize the number of MCFs used and crosstalk between neighboring cores., Comment: 15 pages,14 figures

The Least Loaded (LL) routing algorithm has been in recent decades the routing method of choice in circuit switched networks and therefore it provides a benchmark against which new methods can be compared. This paper improves the performance of the LL algorithm by additionally incorporating a machine learning approach, using a conceptually simple supervised na\"ive Bayes (NB) classifier. Based on a sequence of historical network snapshots, this predicts the potential future circuit blocking probability between each node pair. These snapshots are taken for each service request arriving to the network and record the number of busy capacity units on each link at that instant. The candidate route for serving a current service request is based on both the link loads and the potential future blocking probability of the entire network in case this route is indeed used. The performance of this proposed approach is studied via simulations and compared with both the conventional LL algorithm and the Shortest Path (SP) based approach. Results indicate that the proposed supervised na\"ive Bayes classifier-assisted LL routing algorithm significantly reduces blocking probability of service connection requests and outperforms both the conventional LL and SP routing algorithms. To enable the learning process based on a large number of network snapshots, we also develop a parallel computing framework to implement parallel learning and performance evaluation. Also, a network control system supporting na\"ive Bayes classifier-assisted LL routing algorithm is addressed., Comment: 12 pages, 10 figures