Proactive defragmentation in elastic optical networks under dynamic load conditions

The final publication is available at Springer via http://dx.doi.org/10.1007/s11107-018-0767-7The main weakness of elastic optical networks (EON), under dynamic traffic conditions, stems from spectrum fragmentation. A lot of research efforts have been dedicated during recent years to spectrum defragmentation. In this work, a thorough study about proactive defragmentation is carried out. Effects of the different defragmentation parameters on the EON performance are analyzed, and appropriate values of the defragmentation period, which guarantee suitable network performance while keeping the network control complexity at reasonable values, are obtained by means of extensive simulations. Benefit obtained by applying different defragmentation strategies, in terms of increase in the supported load at a given bandwidth blocking probability, is also reported. Different traffic conditions and network topologies are simulated to assess the validity of the obtained results.Peer ReviewedPostprint (author's final draft

DeepDefrag: A deep reinforcement learning framework for spectrum defragmentation

Exponential growth of bandwidth demand, spurred by emerging network services with diverse characteristics and stringent performance requirements, drives the need for dynamic operation of optical networks, efficient use of spectral resources, and automation. One of the main challenges of dynamic, resource-efficient Elastic Optical Networks (EONs) is spectrum fragmentation. Fragmented, stranded spectrum slots lead to poor resource utilization and increase the blocking probability of incoming service requests. Conventional approaches for Spectrum Defragmentation (SD) apply various criteria to decide when, and which portion of the spectrum to defragment. However, these polices often address only a subset of tasks related to defragmentation, are not adaptable, and have limited automation potential. To address these issues, we propose DeepDefrag, a novel framework based on reinforcement learning that addresses the main aspects of the SD process: determining when to perform defragmentation, which connections to reconfigure, and which part of the spectrum to reallocate them to. DeepDefrag outperforms the well-known Older-First First-Fit (OF-FF) defragmentation heuristic, achieving lower blocking probability under smaller defragmentation overhead

DeepDefrag: A deep reinforcement learning framework for spectrum defragmentation

Exponential growth of bandwidth demand, spurred by emerging network services with diverse characteristics and stringent performance requirements, drives the need for dynamic operation of optical networks, efficient use of spectral resources, and automation. One of the main challenges of dynamic, resource-efficient Elastic Optical Networks (EONs) is spectrum fragmentation. Fragmented, stranded spectrum slots lead to poor resource utilization and increase the blocking probability of incoming service requests. Conventional approaches for Spectrum Defragmentation (SD) apply various criteria to decide when, and which portion of the spectrum to defragment. However, these polices often address only a subset of tasks related to defragmentation, are not adaptable, and have limited automation potential. To address these issues, we propose DeepDefrag, a novel framework based on reinforcement learning that addresses the main aspects of the SD process: determining when to perform defragmentation, which connections to reconfigure, and which part of the spectrum to reallocate them to. DeepDefrag outperforms the well-known Older-First First-Fit (OF-FF) defragmentation heuristic, achieving lower blocking probability under smaller defragmentation overhead

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

Management of Spectral Resources in Elastic Optical Networks

Recent developments in the area of mobile technologies, data center networks, cloud computing and social networks have triggered the growth of a wide range of network applications. The data rate of these applications also vary from a few megabits per second (Mbps) to several Gigabits per second (Gbps), thereby increasing the burden on the Inter- net. To support this growth in Internet data traffic, one foremost solution is to utilize the advancements in optical networks. With technology such as wavelength division multiplexing (WDM) networks, bandwidth upto 100 Gbps can be exploited from the optical fiber in an energy efficient manner. However, WDM networks are not efficient when the traffic demands vary frequently. Elastic Optical Networks (EONs) or Spectrum Sliced Elastic Optical Path Networks (SLICE) or Flex-Grid has been recently proposed as a long-term solution to handle the ever-increasing data traffic and the diverse demand range. EONs provide abundant bandwidth by managing the spectrum resources as fine-granular orthogonal sub-carriers that makes it suitable to accommodate varying traffic demands. However, the Routing and Spectrum Allocation (RSA) algorithm in EONs has to follow additional constraints while allocating sub-carriers to demands. These constraints increase the complexity of RSA in EONs and also, make EONs prone to the fragmentation of spectral resources, thereby decreasing the spectral efficiency. The major objective of this dissertation is to study the problem of spectrum allocation in EONs under various network conditions. With this objective, this dissertation presents the author\u27s study and research on multiple aspects of spectrum allocation in EONs: how to allocate sub-carriers to the traffic demands, how to accommodate traffic demands that varies with time, how to minimize the fragmentation of spectral resources and how to efficiently integrate the predictability of user demands for spectrum assignment. Another important contribution of this dissertation is the application of EONs as one of the substrate technologies for network virtualization

A Study on Reactive and Proactive Push-Pull/Make-Before-Break Defragmentation For Dynamic RMSA

In this thesis, we investigate several defragmentation techniques, with both proactive and reactive triggering strategies, in the context of dynamic Routing, Modulation and Spectrum Assignment (RMSA) in optical flexible networks. Proactive defragmentation is executed periodically or according to some fragmentation degradation thresholds in order to maintain spectral defragmentation at an acceptable level, the defragmentation is independent of the request connection events. Reactive defragmentation, on the other hand, is performed when a new request is blocked due to insufficient spectral resources. In the context of dynamic traffic in a flexible optical network, we looked into different combinations of proactive/reactive push-pull and make-before-break defragmentations. Extensive numerical results show that reactive push-pull defragmentation performs quite well in terms of network throughput and request blocking ratio. Consequently, it is efficient in order to improve network throughput. For proactive push-pull defragmentation, we investigated two different triggering events, namely, time-driven and throughput-driven. We observed that both triggering strategies have a good performance on maintaining an efficient spectrum usage in networks. Throughput-driven strategy performs better when the network is heavily loaded, whereas time-driven strategy is a better option when the network is less loaded

Survivability with Adaptive Routing and Reactive Defragmentation in IP-over-EON after A Router Outage

The occurrence of a router outage in the IP layer can lead to network survivability issues in IP-over-elastic-optical networks with consequent effects on the existing connections used in transiting the router. This usually leads to the application of a path to recover any affected traffic by utilizing the spare capacity of the unaffected lightpath on each link. However, the spare capacity in some links is sometimes insufficient and thus needs to be spectrally expanded. A new lightpath is also sometimes required when it is impossible to implement the first process. It is important to note that both processes normally lead to a large number of lightpath reconfigurations when applied to different unaffected lightpaths. Therefore, this study proposes an adaptive routing strategy to generate the best path with the ability to optimize the use of unaffected lightpaths to perform reconfiguration and minimize the addition of free spectrum during the expansion process. The reactive defragmentation strategy is also applied when it is impossible to apply spectrum expansion because of the obstruction of the neighboring spectrum. This proposed strategy is called lightpath reconfiguration and spectrum expansion with reactive defragmentation (LRSE+RD), and its performance was compared to the first Shortest Path (1SP) as the benchmark without a reactive defragmentation strategy. The simulation conducted for the two topologies with two traffic conditions showed that LRSE+RD succeeded in reducing the lightpath reconfigurations, new lightpath number, and additional power consumption, including the additional operational expense compared to 1SP

Network Virtualization Over Elastic Optical Networks: A Survey of Allocation Algorithms

Network virtualization has emerged as a paradigm for cloud computing services by providing key functionalities such as abstraction of network resources kept hidden to the cloud service user, isolation of different cloud computing applications, flexibility in terms of resources granularity, and on‐demand setup/teardown of service. In parallel, flex‐grid (also known as elastic) optical networks have become an alternative to deal with the constant traffic growth. These advances have triggered research on network virtualization over flex‐grid optical networks. Effort has been focused on the design of flexible and virtualized devices, on the definition of network architectures and on virtual network allocation algorithms. In this chapter, a survey on the virtual network allocation algorithms over flexible‐grid networks is presented. Proposals are classified according to a taxonomy made of three main categories: performance metrics, operation conditions and the type of service offered to users. Based on such classification, this work also identifies open research areas as multi‐objective optimization approaches, distributed architectures, meta‐heuristics, reconfiguration and protection mechanisms for virtual networks over elastic optical networks

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

エラスティック光ネットワークにおけるトラヒック収容性を向上させるための無瞬断デフラグメンテーション

In elastic optical networks (EONs), a major obstacle to using the spectrum resources efficiently is spectrum fragmentation. Much of the research activities in EONs focuses on finding defragmentation methods which remove the spectrum fragmentation. Among the defragmentation methods presented in the literature, hitless defragmentation has been introduced as an approach to limit the spectrum fragmentation in elastic optical networks without traffic disruption. It facilitates the accommodation of new request by creating large spectrum blocks, as it moves active lightpaths (retuning) to fill in gaps left in the spectrum by expired ones.　Nevertheless, hitless defragmentation witnesses limitations for gradual retuning with the conventionally used first fit allocation. The first fit allocation stacks all lightpaths to the lower end of the spectrum. This leads to a large number of lightpaths that need to be retuned and are subject to interfere with each other\u27s retuning. This thesis presents two schemes, which are based on hitless defragmentation, to increase the admissible traffic in EONs.　Firstly, a route partitioning scheme for hitless defragmentation in default EONs is presented. The proposed scheme uses route partitioning with the first-last fit allocation to increase the possibilities of lightpath retuning by avoiding the retuning interference among lightpaths. The first-last fit allocation is used to set a bipartition with one partition allocated with the first fit and the second with the last fit. Lightpaths that are allocated on different partitions cannot interfere with each other. Thus the route partitioning avoids the interferences among lightpaths when retuning. The route partitioning problem is defined as an optimization problem to minimize the total interferences.　Secondly, this thesis presents a defragmentation scheme using path exchanging in 1+1 path protected EONs. For 1+1 path protection, conventional defragmentation approaches consider designated primary and backup paths. This exposes the spectrum to fragmentations induced by the primary lightpaths, which are not to be disturbed in order to achieve hitless defragmentation. The presented path exchanging scheme exchanges the path function of the 1+1 protection with the primary toggling to the backup state while the backup becomes the primary. This allows both lightpaths to be reallocated during the defragmentation process while they work as backup, offering hitless defragmentation. Considering path exchanging, a static spectrum reallocation optimization problem that minimizes the spectrum fragmentation while limiting the number of path exchanging and reallocation operations is defined.　For each of the presented schemes, after the problem is defined as an optimization problem, it is then formulated as an integer linear programming problem (ILP). A decision version of each defined problem is proven NP-complete. A heuristic algorithm is then introduced for large networks, where the ILP used to represent the problem is not tractable. The simulation results show that the proposed schemes outperform the conventional ones and improve the total admissible traffic.電気通信大学201