Space-Division Multiplexing in Data Center Networks: On Multi-Core Fiber Solutions and Crosstalk-Suppressed Resource Allocation

The rapid growth of traffic inside data centers caused by the increasing adoption of cloud services necessitates a scalable and cost-efficient networking infrastructure. Space-division multiplexing (SDM) is considered as a promising solution to overcome the optical network capacity crunch and support cost-effective network capacity scaling. Multi-core fiber (MCF) is regarded as the most feasible and efficient way to realize SDM networks, and its deployment inside data centers seems very likely as the issue of inter-core crosstalk (XT) is not severe over short link spans (<1  km ) compared to that in long-haul transmission. However, XT can still have a considerable effect in MCF over short distances, which can limit the transmission reach and in turn the data center’s size. XT can be further reduced by bi-directional transmission of optical signals in adjacent MCF cores. This paper evaluates the benefits of MCF-based SDM solutions in terms of maximizing the capacity and spatial efficiency of data center networks. To this end, we present an analytical model for XT in bi-directional normal step-index and trench-assisted MCFs and propose corresponding XT-aware core prioritization schemes. We further develop XT-aware spectrum resource allocation strategies aimed at relieving the complexity of online XT computation. These strategies divide the available spectrum into disjoint bands and incrementally add them to the pool of accessible resources based on the network conditions. Several combinations of core mapping and spectrum resource allocation algorithms are investigated for eight types of homogeneous MCFs comprising 7–61 cores, three different multiplexing schemes, and three data center network topologies with two traffic scenarios. Extensive simulation results show that combining bi-directional transmission in dense core fibers with tailored resource allocation schemes significantly increases the network capacity. Moreover, a multiplexing scheme that combines SDM and WDM can achieve up to 33 times higher link spatial efficiency and up to 300 times greater capacity compared to a WDM solution

Alocação de recursos em redes ópticas elásticas baseadas em multiplexação por divisão espacial

Orientador: Nelson Luis Saldanha da FonsecaDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Tecnologias de redes ópticas baseadas em fibras mono-núcleo e mono-modo possuem limite de capacidade e não conseguem suprir a demanda crescente de largura de banda. Um forma de resolver esse problema se dá através do uso de multiplexação por divisão espacial (SDM - \textit{Space-Division Multiplexing}). A transmissão de dados em SDM ocorre através de múltiplos núcleos agrupados em um único filamento de fibra, ou utilizando múltiplos modos transversais suportados por um núcleo. A combinação da flexibilidade de redes ópticas elásticas (EON - \textit{Elastic Optical Networks}) e a alta capacidade do SDM é promissora para o futuro das redes ópticas. Na camada de enlace, quando uma nova solicitação para estabelecimento de conexão chega, é necessário fazer a reserva de recursos para realizar essa conexão. A determinação dos recursos a serem alocados é dada pela solução do problema de roteamento, alocação de núcleo e \textit{slots} (RCSA - \textit{Routing, Core and Spectrum Allocation}). Na alocação de recursos, algumas restrições devem ser respeitadas, tais como a contiguidade e continuidade dos \textit{slots} de frequência, e tolerância ao \textit{crosstalk} espacial. Estas restrições implicam em uma maior complexidade para a acomodação do tráfego das conexões. A virtualização de redes permite que redes virtuais compartilhem recursos físicos, simplificando o gerenciamento de recursos na camada óptica, oferecendo flexibilidade na alocação de recursos e segurança dos serviços. Um dos principais desafios da virtualização é configurar de forma eficiente as redes virtuais, que consiste na alocação de recursos físicos para acomodá-las. Esta tese propõe soluções para o problema do RCSA em redes SDM-EON. A primeira contribuição desta tese é um algoritmo que considera o equilíbrio entre eficiência energética e bloqueio de requisições. Propõe-se um algoritmo de agregação de tráfego em lote, capitalizando na flexibilidade temporal para satisfazer requisições com o objetivo de formar lotes de requisições, aumentando assim a probabilidade de serem atendidas as requisições em um outro momento. A segunda contribuição desta tese é direcionada para a solução do problema da fragmentação, que ocorre em cenários onde pequenos conjuntos de \textit{slots} disponíveis ficam espalhados no espectro, causando o bloqueio de novas requisição. Propõem-se um conjunto de algoritmos proativos e reativos. Os algoritmos proativos utilizam diferentes técnicas, tais como, múltiplos caminhos, priorização de núcleo e área, bem como métricas de avaliação da fragmentação na composição de caminhos. O algoritmo reativo utiliza aprendizagem de máquina para fazer um rearranjo espectral e aumentar a capacidade de prevenção da fragmentação no RCSA. A terceira contribuição desta tese é uma solução para aumentar a eficiência do compartilhamento de recursos em redes virtuais. Este problema consiste na configuração de enlaces e nós virtuais para caminhos e nós físicos, respectivamente. A solução proposta introduz uma arquitetura utilizando aprendizado de máquina, que age como um assistente no processo de configuração de redes virtuaisAbstract: Optical network technologies based on a single-core and single-mode fibers have a limited capacity and cannot provide enough resources to a constant increase of bandwidth demands. One approach to overcome this is the use of Space-Division Multiplexing (SDM) which relies on sending data through multiple cores embedded into a single strand of fiber or using multiple transverse modes supported by a core. The combination of the flexibility of Elastic Optical Networks (EONs) and the high capacity of SDM is a promising solution to cope with the bandwidth demands. At the network level, when a traffic request arrives, it needs to reserve network resources to establish it. One approach to accommodate traffic demand over optical networks is the Routing, Core and Spectrum Allocation (RCSA), in which end-to-end lightpaths are offered for each individual request. In these scenarios, during the allocation process, some constraints need to be respected, such as contiguity and continuity of slots (selected in the resource selection process), and spatial crosstalk. These constraints pose extra complexity to accommodate the requests for the lightpath establishment. As one of the possible solutions, network virtualization is capable of improving the efficiency of optical networks, by allowing virtual networks to share the resources of physical networks, simplifying the management of resource and providing flexibility in resource allocation. One of the main challenges of network virtualization is to configure a virtual network efficiently which comprises allocating physical resources to accommodate incoming virtual networks. This thesis proposes solutions to the RCSA problem and the virtual network configuration problem for SDM-EON networks. The first contribution of this thesis is an algorithm to promote an equilibrium between reduction of the network energy consumption and reduction of the blocking of requests. For this purpose, we introduce a traffic grooming algorithm using batches, which takes advantage of the deadline of each request to form batches, increasing the chances of the requests to be established at another time. The second contribution of this thesis is a set of algorithms using different techniques to handle the fragmentation problem, where a small portion of available slot sequences end up scattered in a fiber link, blocking future requests, called the fragmentation problem. For this purpose, we propose proactive and reactive algorithms. Proactive algorithms use different techniques, such as multipath routing, core, and area prioritization, and metrics to use in the route selection process. The reactive algorithm uses machine learning to rearrange the spectrum and tune the RCSA algorithm to prevent the fragmentation. The third contribution of this thesis proposes a solution to improve resource sharing in network virtualization. This problem consists in configuring virtual links and nodes to physical nodes and paths. For this purpose, we propose a learning assistant control loop to handle the virtual network configuration problemMestradoCiência da ComputaçãoMestra em Ciência da Computação131025/2017-1CNP

Enabling Technology in Optical Fiber Communications: From Device, System to Networking

This book explores the enabling technology in optical fiber communications. It focuses on the state-of-the-art advances from fundamental theories, devices, and subsystems to networking applications as well as future perspectives of optical fiber communications. The topics cover include integrated photonics, fiber optics, fiber and free-space optical communications, and optical networking

Enabling Technologies for Optical Data Center Networks: Spatial Division Multiplexing

With the continuously growing popularity of cloud services, the traffic volume inside the\ua0data\ua0centers is dramatically increasing. As a result, a scalable and efficient infrastructure\ua0for\ua0data\ua0center\ua0networks\ua0(DCNs) is required. The current\ua0optical\ua0DCNs using either individual fibers or fiber ribbons are costly, bulky, hard to manage, and not scalable.\ua0Spatial\ua0division\ua0multiplexing\ua0(SDM) based on multicore or multimode (few-mode) fibers is recognized as a promising technology to increase the\ua0spatial\ua0efficiency\ua0for\ua0optical\ua0DCNs, which opens a new way towards high capacity and scalability. This tutorial provides an overview of the components, transmission options, and interconnect architectures\ua0for\ua0SDM-based DCNs, as well as potential technical challenges and future directions. It also covers the co-existence of SDM and other\ua0multiplexing\ua0techniques, such as wavelength-division\ua0multiplexing\ua0and flexible spectrum\ua0multiplexing, in\ua0optical\ua0DCNs

Optimization, Design, and Analysis of Flexible-Grid Optical Networks with Physical-Layer Constraints

The theme of this thesis is the optimization, design, and analysis of flexible-grid optical networks that are constrained by physical-layer impairments (PLIs). We consider three flexible-grid network scenarios. The networks in the first class are static nonlinear transparent backbone networks where physical-layer resources are allocated to each traffic demand. The networks in the second class are traffic-variable nonlinear translucent backbone networks where regenerator sites are necessary to recover optical signals from the accumulated noise in long-distance transmission. The third class is data-center networks based on optical spatial division multiplexing. Within each class, our focus is primarily on an efficient and balanced allocation of network resources. Both optimization formulations and heuristic algorithms are proposed for each class. The contributions of this thesis can thus be categorized into three topics, as outlined below.First, we consider the optimization of network resources in the presence of PLI. The PLI between optical connections is characterized by the Gaussian noise (GN) model and incorporated into resource allocation algorithms. As an example, for a link-level optical communication system, the spectrum usage can be reduced by roughly up to 22% by accurately modelling the PLIs and assigning proper modulation formats and spectrum to optical connections. For resource allocation in the network level, the power spectral density of each optical connection is optimized in addition to the previously mentioned resources.As a second topic, the design of flexible-grid optical networks is studied. Specifically, we consider the regenerator location problem in traffic-variable translucent backbone networks. Due to the constantly changing traffic, the PLIs suffered by optical connections are also stochastic and, thus, have to be handled from a probabilistic perspective. A statistical network assessment process is used to characterize the noise distributions suffered by optical connections on each link, based on which a heuristic algorithm is proposed to select a set of regenerator sites with the minimum blocking probability.Finally, we study the trade-off between the blocking probability and total throughput in the modular data center networks (DCNs) based on different optical spatial division multiplexing switching schemes. This performance trade-off is caused by the coexistence of traffic demands with extremely different data rates and number of requests in DCNs. A heuristic resource allocation algorithm is proposed to enable flexible tuning of the objective function and achieve a balanced network performance

Stochastische Analyse und lernbasierte Algorithmen zur Ressourcenbereitstellung in optischen Netzwerken

The unprecedented growth in Internet traffic has driven the innovations in provisioning of optical resources as per the need of bandwidth demands such that the resource utilization and spectrum efficiency could be maximized. With the advent of the next generation flexible optical transponders and switches, the flexible-grid-based elastic optical network (EON) is foreseen as an alternative to the widely deployed fixed-grid-based wavelength division multiplexing networks. At the same time, the flexible resource provisioning also raises new challenges for EONs. One such challenge is the spectrum fragmentation. As network traffic varies over time, spectrum gets fragmented due to the setting up and tearing down of non-uniform bandwidth requests over aligned (i.e., continuous) and adjacent (i.e., contiguous) spectrum slices, which leads to a non-optimal spectrum allocation, and generally results in higher blocking probability and lower spectrum utilization in EONs. To address this issue, the allocation and reallocation of optical resources are required to be modeled accurately, and managed efficiently and intelligently. The modeling of routing and spectrum allocation in EONs with the spectrum contiguity and spectrum continuity constraints is well-investigated, but existing models do not consider the fragmentation issue resulted by these constraints and non-uniform bandwidth demands. This thesis addresses this issue and considers both the constraints to computing exact blocking probabilities in EONs with and without spectrum conversion, and with spectrum reallocation (known as defragmentation) for the first time using the Markovian approach. As the exact network models are not scalable with respect to the network size and capacity, this thesis proposes load-independent and load-dependent approximate models to compute approximate blocking probabilities in EONs. Results show that the connection blocking due to fragmentation can be reduced by using a spectrum conversion or a defragmentation approach, but it can not be eliminated in a mesh network topology. This thesis also deals with the important network resource provisioning task in EONs. To this end, it first presents algorithmic solutions to efficiently allocate and reallocate spectrum resources using the fragmentation factor along spectral, time, and spatial dimensions. Furthermore, this thesis highlights the role of machine learning techniques in alleviating issues in static provisioning of optical resources, and presents two use-cases: handling time-varying traffic in optical data center networks, and reducing energy consumption and allocating spectrum proportionately to traffic classes in fiber-wireless networks.Die flexible Nutzung des Spektrums bringt in Elastischen Optischen Netze (EON) neue Herausforderungen mit sich, z.B., die Fragmentierung des Spektrums. Die Fragmentierung entsteht dadurch, dass die Netzwerkverkehrslast sich im Laufe der Zeit ändert und so wird das Spektrum aufgrund des Verbindungsaufbaus und -abbaus fragmentiert. Das für eine Verbindung notwendige Spektrum wird durch aufeinander folgende (kontinuierliche) und benachbarte (zusammenhängende) Spektrumsabschnitte (Slots) gebildet. Dies führt nach den zahlreichen Reservierungen und Freisetzungen des Spektrums zu einer nicht optimalen Zuordnung, die in einer höheren Blockierungs-wahrscheinlichkeit der neuen Verbindungsanfragen und einer geringeren Auslastung von EONs resultiert. Um dieses Problem zu lösen, müssen die Zuweisung und Neuzuordnung des Spektrums in EONs genau modelliert und effizient sowie intelligent verwaltet werden. Diese Arbeit beschäftigt sich mit dem Fragmentierungsproblem und berücksichtigt dabei die beiden Einschränkungen: Kontiguität und Kontinuität. Unter diesen Annahmen wurden analytische Modelle zur Berechnung einer exakten Blockierungswahrscheinlichkeit in EONs mit und ohne Spektrumskonvertierung erarbeitet. Außerdem umfasst diese Arbeit eine Analyse der Blockierungswahrscheinlichkeit im Falle einer Neuzuordnung des Sprektrums (Defragmentierung). Diese Blockierungsanalyse wird zum ersten Mal mit Hilfe der Markov-Modelle durchgeführt. Da die exakten analytischen Modelle hinsichtlich der Netzwerkgröße und -kapazität nicht skalierbar sind, werden in dieser Dissertation verkehrslastunabhängige und verkehrslastabhängige Approximationsmodelle vorgestellt. Diese Modelle bieten eine Näherung der Blockierungswahrscheinlichkeiten in EONs. Die Ergebnisse zeigen, dass die Blockierungswahrscheinlichkeit einer Verbindung aufgrund von einer Fragmentierung des Spektrums durch die Verwendung einer Spektrumkonvertierung oder eines Defragmentierungsverfahrens verringert werden kann. Eine effiziente Bereitstellung der optischen Netzwerkressourcen ist eine wichtige Aufgabe von EONs. Deswegen befasst sich diese Arbeit mit algorithmischen Lösungen, die Spektrumressource mithilfe des Fragmentierungsfaktors von Spektral-, Zeit- und räumlichen Dimension effizient zuweisen und neu zuordnen. Darüber hinaus wird die Rolle des maschinellen Lernens (ML) für eine verbesserte Bereitstellung der optischen Ressourcen untersucht und das ML basierte Verfahren mit der statischen Ressourcenzuweisung verglichen. Dabei werden zwei Anwendungsbeispiele vorgestellt und analysiert: der Umgang mit einer zeitveränderlichen Verkehrslast in optischen Rechenzentrumsnetzen, und eine Verringerung des Energieverbrauchs und die Zuweisung des Spektrums proportional zu Verkehrsklassen in kombinierten Glasfaser-Funknetzwerken

Reconfigurable Optically Interconnected Systems

With the immense growth of data consumption in today's data centers and high-performance computing systems driven by the constant influx of new applications, the network infrastructure supporting this demand is under increasing pressure to enable higher bandwidth, latency, and flexibility requirements. Optical interconnects, able to support high bandwidth wavelength division multiplexed signals with extreme energy efficiency, have become the basis for long-haul and metro-scale networks around the world, while photonic components are being rapidly integrated within rack and chip-scale systems. However, optical and photonic interconnects are not a direct replacement for electronic-based components. Rather, the integration of optical interconnects with electronic peripherals allows for unique functionalities that can improve the capacity, compute performance and flexibility of current state-of-the-art computing systems. This requires physical layer methodologies for their integration with electronic components, as well as system level control planes that incorporates the optical layer characteristics. This thesis explores various network architectures and the associated control plane, hardware infrastructure, and other supporting software modules needed to integrate silicon photonics and MEMS based optical switching into conventional datacom network systems ranging from intra-data center and high-performance computing systems to the metro-scale layer networks between data centers. In each of these systems, we demonstrate dynamic bandwidth steering and compute resource allocation capabilities to enable significant performance improvements. The key accomplishments of this thesis are as follows. In Part 1, we present high-performance computing network architectures that integrate silicon photonic switches for optical bandwidth steering, enabling multiple reconfigurable topologies that results in significant system performance improvements. As high-performance systems rely on increased parallelism by scaling up to greater numbers of processor nodes, communication between these nodes grows rapidly and the interconnection network becomes a bottleneck to the overall performance of the system. It has been observed that many scientific applications operating on high-performance computing systems cause highly skewed traffic over the network, congesting only a small percentage of the total available links while other links are underutilized. This mismatch of the traffic and the bandwidth allocation of the physical layer network presents the opportunity to optimize the bandwidth resource utilization of the system by using silicon photonic switches to perform bandwidth steering. This allows the individual processors to perform at their maximum compute potential and thereby improving the overall system performance. We show various testbeds that integrates both microring resonator and Mach-Zehnder based silicon photonic switches within Dragonfly and Fat-Tree topology networks built with conventional equipment, and demonstrate 30-60% reduction in execution time of real high-performance benchmark applications. Part 2 presents a flexible network architecture and control plane that enables autonomous bandwidth steering and IT resource provisioning capabilities between metro-scale geographically distributed data centers. It uses a software-defined control plane to autonomously provision both network and IT resources to support different quality of service requirements and optimizes resource utilization under dynamically changing load variations. By actively monitoring both the bandwidth utilization of the network and CPU or memory resources of the end hosts, the control plane autonomously provisions background or dynamic connections with different levels of quality of service using optical MEMS switching, as well as initializing live migrations of virtual machines to consolidate or distribute workload. Together these functionalities provide flexibility and maximize efficiency in processing and transferring data, and enables energy and cost savings by scaling down the system when resources are not needed. An experimental testbed of three data center nodes was built to demonstrate the feasibility of these capabilities. Part 3 presents Lightbridge, a communications platform specifically designed to provide a more seamless integration between processor nodes and an optically switched network. It addresses some of the crucial issues faced by the works presented in the previous chapters related to optical switching. When optical switches perform switching operations, they change the physical topology of the network, and they lack the capability to buffer packets, resulting in certain optical circuits being unavailable. This prompts the question of whether it is safe to transmit packets by end hosts at any given time. Lightbridge was developed to coordinate switching and routing of optical circuits across the network, by having the processors gain information about the current state of the optical network before transmitting packets, and being able to buffer packets when the optical circuit is not available. This part describes details of Lightbridge which is constituted by a loadable Linux kernel module along with other supporting modifications to the Linux kernel in order to achieve the necessary functionalities