Combinatorial Properties and Defragmentation Algorithms in WSW1 Switching Fabrics

A spectrum defragmentation problem in elastic optical networks was considered under the assumption that all connections can be realized in switching nodes. But this assumption is true only when the switching fabric has appropriate combinatorial properties. In this paper, we consider a defragmentation problem in one architecture of wavelength-space-wavelength switching fabrics. First, we discuss the requirements for this switching fabric, below which defragmentation does not always end with success. Then, we propose defragmentationalgorithms and evaluate them by simulation. The results show that proposed algorithms can increase the number of connections realized in the switching fabric and reduce the loss probability

Cost-Effective Delay-Constrained Optical Fronthaul Design for 5G and Beyond

With the rapid growth of the telecom sector heading towards 5G and 6G and the emergence of high-bandwidth and time-sensitive applications, mobile network operators (MNOs) are driven to plan their networks to meet these new requirements in a cost-effective manner. The cloud radio access network (CRAN) has been presented as a promising architecture that can decrease capital expenditures (Capex) and operating expenditures (Opex) and improve network performance. The fronthaul (FH) is a part of the network that links the remote radio head (RRH) to the baseband unit (BBU); these links need high-capacity and low latency connections necessitating costeffective implementation. On the other hand, the transport delay and FH deployment costs increase if the BBU is not placed in an appropriate location. In this paper, we propose an integer linear program (ILP) that simultaneously optimizes BBU and FH deployment resulting in minimal capital expenditures (Capex). Simulations are run to compare the performance of star and tree topologies with the varying line of sight probabilities (LoS) and delay thresholds. We consider fiber-optic (FO) and free-space optics (FSO) technologies as FH for the CRAN. Finally, we provide an analysis of Opex and the total costs of ownership (TCO), i.e., a technoeconomic analysis

Combinatorial Properties and Defragmentation Algorithms in WSW1 Switching Fabrics

A spectrum defragmentation problem in elastic optical networks was considered under the assumption that all connections can be realized in switching nodes. But this assumption is true only when the switching fabric has appropriate combinatorial properties. In this paper, we consider a defragmentation problem in one architecture of wavelength-space-wavelength switching fabrics. First, we discuss the requirements for this switching fabric, below which defragmentation does not always end with success. Then, we propose defragmentationalgorithms and evaluate them by simulation. The results show that proposed algorithms can increase the number of connections realized in the switching fabric and reduce the loss probability

Multiclass scheduling algorithms for the DAVID metro network

Abstract—The data and voice integration over dense wavelength-division-multiplexing (DAVID) project proposes a metro network architecture based on several wavelength-division-multiplexing (WDM) rings interconnected via a bufferless optical switch called Hub. The Hub provides a programmable interconnection among rings on the basis of the outcome of a scheduling algorithm. Nodes connected to rings groom traffic from Internet protocol routers and Ethernet switches and share ring resources. In this paper, we address the problem of designing efficient centralized scheduling algorithms for supporting multiclass traffic services in the DAVID metro network. Two traffic classes are considered: a best-effort class, and a high-priority class with bandwidth guarantees. We define the multiclass scheduling problem at the Hub considering two different node architectures: a simpler one that relies on a complete separation between transmission and reception resources (i.e., WDM channels) and a more complex one in which nodes fully share transmission and reception channels using an erasure stage to drop received packets, thereby allowing wavelength reuse. We propose both optimum and heuristic solutions, and evaluate their performance by simulation, showing that heuristic solutions exhibit a behavior very close to the optimum solution. Index Terms—Data and voice integration over dense wavelength-division multiplexing (DAVID), metropolitan area network, multiclass scheduling, optical ring, wavelength-division multiplexing (WDM). I

Experimental SDN Control Solutions for Automatic Operations and Management of 5G Services in a Fixed Mobile Converged Packet-Optical Network

5G networks will impose network operators to accommodate services demanding heterogeneous and stringent requirements in terms of increased bandwidth, reduced latency, higher availability, etc. as well as enabling emerging capabilities such as slicing. Operators will be then forced to make notable investments in their infrastructure but the revenue is not envisaged to be proportional. Thereby, operators are seeking for more cost-effective solutions to keep their competitiveness. An appealing solution is to integrate all (broadband) services including both fixed and mobile in a convergent way. This is referred to as Fixed Mobile Convergence (FMC). FMC allows seamlessly serving any kind of access service over the same network infrastructure (access, aggregation and core) and relying on common set of control and operation functions. To this end, FMC leverages the benefits provided by Software Defined Networking (SDN) and Network Function Virtualization (NFV). First, we discuss some of the explored FMC solutions and technologies, from both structural and functional perspectives Next, focusing on a Multi-Layer (Packet and Optical) Aggregation Network, we report two implemented and experimentally validated SDN/NFV orchestration architectures providing feasibleThis work has been partially funded by the Spanish Ministry MINECO projects DESTELLO (TEC2015-69256-R) and 5G-REFINE (TEC2017-88373-R), and the EU H2020 5G TRANSFORMER project (grant no. 761536)

GPON PLOAMd Message Analysis Using Supervised Neural Networks

This paper discusses the possibility of analyzing the orchestration protocol used in gigabit-capable passive optical networks (GPONs). Considering the fact that a GPON is defined by the International Telecommunication Union Telecommunication sector (ITU-T) as a set of recommendations, implementation across device vendors might exhibit few differences, which complicates analysis of such protocols. Therefore, machine learning techniques are used (e.g., neural networks) to evaluate differences in GPONs among various device vendors. As a result, this paper compares three neural network models based on different types of recurrent cells and discusses their suitability for such analysis

MongoDB Database as Storage for GPON Frames

This work is focused on creating an open-source software-based solution for monitoring traffic transmitted through gigabit passive optical network. In this case, the data are captured by the field-programmable gate array (FPGA) card and reassembled using parsing software from a passive optical network built on the International Telecommunication Unit telecommunication section (ITU-T) G.984 gigabit-capable passive optical network GPON recommendation. Then, the captured frames are converted by suitable software into GPON frames, which will be further processed for analysis. Due to the high transfer rate of GPON recommendations, the work describes the issue of writing to the Mongo database system. In order to achieve the best possible results and minimal loss of transmitted frames, a series of tests were performed. The proposed test scenarios are based on different database writing approaches and are implemented in the Python and C# programming languages. Based on our results, it has been shown that the high processing speed is too high for Python processing. Critical operations must be implemented in the C# programming language. Due to rapid application development, Python can only be used for noncritical time-consuming data processing operations

Statistical assessment of open optical networks

In order to cope with the increase of the final user traffic, operators and vendors are pushing towards physical layer aware networking as a way to maximize the network capacity. To this aim, optical networks are becoming more and more open by exposing physical parameters enabling fast and reliable estimation of the lightpath quality of transmission. This comes in handy not only from the point of view of the planning and managing of the optical paths but also on a more general picture of the whole optical network performance. In this work, the Statistical Network Assessment Process (SNAP) is presented. SNAP is an algorithm allowing for estimating different network metrics such as blocking probability or link saturation, by generating traffic requests on a graph abstraction of the physical layer. Being aware of the physical layer parameters and transceiver technologies enables assessing their impact on high level network figures of merit. Together with a detailed description of the algorithm, we present a comprehensive review of several results on the networking impact of multirate transceivers, flex-grid spectral allocation as a means to finely exploit lightpath capacity and of different Space Division Multiplexing (SDM) solutions