Performance Evaluation of Non-Hitless Spectrum Defragmentation Algorithms in Elastic Optical Networks

Fragmentation in Elastic Optical Networks is an issue caused by isolated, non-aligned, and non-contiguous frequency slots that can not be used to allocate new connection request to the network, due to the optical layer restrictions imposed to the Routing and Spectrum Assignment (RSA) algorithms. To deal with this issue, several studies about Spectrum Defragmentation have been presented. In this work, we analyze the most important Non-Hitless Defragmentation Algorithms found in the literature, with proactive and reactive approaches that include rerouting and non-rerouting schemes, and compare their performance in terms of Blocking Probability, Entropy, and Bandwidth Fragmentation Ratio. Simulations results showed that the Fragmentation Aware schemes outperformed the other algorithms in low traffic load, but the Reactive schemes got better results in high traffic load.Sociedad Argentina de Informática e Investigación Operativa (SADIO

An impairment-aware resource allocation scheme for dynamic elastic optical networks

By using impairment-driven variable guardbands, our proposed dynamic resource allocation scheme accommodates 50% more traffic in comparison to existing fixed transmission-reach- and guardband-based algorithms

Priority realloc : a threefold mechanism for route and resources allocation in EONs

Cotutela Universitat Politècnica de Catalunya i Escola Politécnica da Universidade de São PauloBackbone networks are responsible for long-haul data transport serving many clients with a large volume of data. Since long-haul data transport service must rely on a robust high capacity network the current technology broadly adopted by the industry is Wavelength Division Multiplexing (WDM). WDM networks enable one single fiber to operate with multiple high capacity channels, drastically increasing the fiber capacity. In WDM networks each channel is associated with an individual wavelength. Therefore a whole wavelength capacity is assigned to a connection, causing waste of bandwidth in case the connection bandwidth requirement is less than the channel total capacity. In the last half decade, Elastic Optical Networks (EON) have been proposed and developed based on the flexible use of the optical spectrum known as the flexigrid. EONs are adaptable to clients requirements and may enhance optical networks performance. For these reasons, research community and data transport providers have been demonstrating increasingly high interest in EONs which are likely to replace WDM as the universally adopted technology in backbone networks in the near future. EONs have two characteristics that may limit its efficient resources use. The spectrum fragmentation, inherent to the dynamic EON operation, decreases the network capacity to assign resources to connection requests increasing network blocking probability. The spectrum fragmentation also intensifies the denial of service to higher rate request inducing service unfairness. Due to the fact EONs were just recently developed and proposed, the aforementioned issues were not yet extensively studied and solutions are still being proposed. Furthermore, EONs do not yet provide specific features as differentiated service mechanisms. Differentiated service strategies are important in backbone networks to guarantee client's diverse requirements in case of a network failure or the natural congestion and resources contention that may occur at some periods of time in a network. Impelled by the foregoing facts, this thesis objective is three-fold. By means of developing and proposing a mechanism for routing and resources assignment in EONs, we intend to provide differentiated service while decreasing fragmentation level and increasing service fairness. The mechanism proposed and explained in this thesis was tested in an EON simulation environment and performance results indicated that it promotes beneficial performance enhancements when compared to benchmark algorithms.Redes backbone sao responsáveis pelo transporte de dados à longa distância que atendem a uma grande quantidade de clientes com um grande volume de dados. Como redes backbone devem basear-se em uma rede robusta e de alta capacidade, a tecnologia atual amplamente adotada pela indústria é Wavelength Division Multiplexing (WDM). Redes WDM permitem que uma única fibra opere com múltiplos canais de alta largura de banda, aumentando drasticamente a capacidade da fibra. Em redes WDM cada canal está associado a um comprimento de onda particular. Por conseguinte, toda capacidade do comprimento de onda é atribuída a uma única conexão, fazendo com que parte da largura de banda seja desperdiçada no caso em que a requisição de largura de banda da conexão seja menor do que a capacidade total do canal. A partir da metade da última década, as Redes Ópticas Elásticas (Elastic Optical Networks - EON) têm sido propostas e desenvolvidas com base no uso flexível do espectro óptico conhecido como flexigrid. EONs são adaptáveis às requisiçes por banda dos clientes e podem, portanto, melhorar o desempenho das redes ópticas. Por estas razões, EONs têm recebido cada vez mais interesse dos meios de pesquisa e provedores de serviço e provavelmente substituirão WDM como a tecnologia universalmente adotada pela indústria em redes backbone. EONs têm duas características que podem limitar a utilização eficiente de recursos. A fragmentação do espectro, inerente à operação dinâmica das EONs, pode diminuir a capacidade da rede em distribuir recursos ao atender às solicitações por conexões aumentando a probabilidade de bloqueio na rede. A fragmentação do espectro também intensifica a negação de serviço às solicitações por taxa de transmissão mais elevada, gerando injustiça no serviço prestado. Como EONs foram desenvolvidas recentemente, respostas às questões acima mencionadas ainda estão sob estudo e soluções continuam sendo propostas na literatura. Além disso, EONs ainda não fornecem funções específicas como um mecanismo que proveja diferenciação de serviço. Estratégias de diferenciação de serviço são importantes em redes backbone para garantir os diversos requisitos dos clientes em caso de uma falha na rede ou do congestionamento e disputa por recursos que podem ocorrer em alguns períodos em uma rede. Impulsionada pelos fatos anteriormente mencionados, esta tese possui três objetivos. Através do desenvolvimento e proposta de um mecanismo de roteamento e atribuição de recursos para EONs, temos a intenção de disponibilizar diferenciação de serviço, diminuir o nível de fragmentação de espectro e aumentar a justiça na distribuição de serviços. O mecanismo proposto nesta tese foi testado em simulações de EONs. Resultados indicaram que o mecanismo proposto promove benefícios através do aprimoramento da performance de uma rede EON quando comparado com algoritmos de referência.Les xarxes troncals son responsables per el transport de dades a llarga distància que serveixen a una gran quantitat de clients amb un gran volum de dades. Com les xarxes troncals han d'estar basades en una xarxa robusta i d'alta capacitat, la tecnologia actual àmpliament adoptada per la indústria és el Wavelength Division Multiplexing (WDM). Xarxes WDM permeten operar amb una sola fibra multicanal d'alt ample de banda, el que augmenta molt la capacitat de la fibra. A les xarxes WDM cada canal est a associat amb una longitud d'ona particular. En conseqüència, tota la capacitat del canal es assignada a una sola connexió, fent que part dels recurs siguin perduts en el cas en que l'ample de banda sol licitada sigui menys que la capacitat total del canal. A gairebé deu anys les xarxes òptiques elàstiques (Elastic Optical Networks -EON) son propostes i desenvolupades basades en el ús visible de l'espectre òptic conegut com Flexigrid. EONs són adaptables a les sol·licituds per ample de banda dels clients i per tant poden millorar el rendiment de les xarxes òptiques. Per aquestes raons, EONs han rebut cada vegada més interès en els mitjans d’investigació i de serveis i, probablement, han de reemplaçar el WDM com la tecnologia universalment adoptada en les xarxes troncals. EONs tenen dues característiques que poden limitar l'ús eficient dels recursos seus. La fragmentació de l'espectre inherent al funcionament dinàmic de les EONs, pot disminuir la capacitat de la xarxa en distribuir els recursos augmentant la probabilitat de bloqueig de connexions. La fragmentació de l'espectre també intensifica la denegació de les sol·licituds de servei per connexions amb una major ample de banda, el que genera injustícia en el servei ofert. Com les EONs s'han desenvolupat recentment, solucions als problemes anteriors encara estan en estudi i les solucions segueixen sent proposades en la literatura. D'altra banda, les EONs encara no proporcionen funcions especifiques com mecanisme de diferenciació de provisió de serveis. Estratègies de diferenciació de servei són importants en les xarxes troncals per garantir les diverses necessitats dels clients en cas d'una fallada de la xarxa o de la congestió i la competència pels recursos que es poden produir en alguns períodes. Impulsada pels fets abans esmentats, aquesta tesi te tres objectius. A través del desenvolupament i proposta d'un mecanisme d'enrutament i assignació de recursos per EONs, tenim la intenció d'oferir la diferenciació de serveis, disminuir el nivell de fragmentació de l'espectre i augmentar l'equitat en la distribució dels serveis. El mecanisme proposat en aquesta tesi ha estat provat en simulacions EONs. Els resultats van indicar que el mecanisme promou millores en el rendiment de la EON, en comparació amb els algoritmes de referència.Postprint (published version

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

A constrained maximum available frequency slots on path based online routing and spectrum allocation for dynamic traffic in elastic optical networks

Elastic optical networking is a potential candidate to support dynamic traffic with heterogeneous data rates and variable bandwidth requirements with the support of the optical orthogonal frequency division multiplexing technology (OOFDM). During the dynamic network operation, lightpath arrives and departs frequently and the network status updates accordingly. Fixed routing and alternate routing algorithms do not tune according to the current network status which are computed offline. Therefore, offline algorithms greedily use resources with an objective to compute shortest possible paths and results in high blocking probability during dynamic network operation. In this paper, adaptive routing algorithms are proposed for shortest path routing as well as alternate path routing which make routing decision based on the maximum idle frequency slots (FS) available on different paths. The proposed algorithms select an underutilized path between different choices with maximum idle FS and efficiently avoids utilizing a congested path. The proposed routing algorithms are compared with offline routing algorithms as well as an existing adaptive routing algorithm in different network scenarios. It has been shown that the proposed algorithms efficiently improve network performance in terms of FS utilization and blocking probability during dynamic network operation

Routing, Modulation and Spectrum Assignment Algorithm Using Multi-Path Routing and Best-Fit

Producción CientíficaElastic Optical Networks (EONs) are a promising optical technology to deal with the ever-increasing traffic and the vast number of connected devices of the next generation of the Internet, associated to paradigms like the Internet of Things (IoT), the Tactile Internet or the Industry 4.0, to name just a few. In this kind of optical network, each optical circuit or lightpath is provisioned by means of superchannels of variable bandwidth. In this manner, only the necessary bandwidth to accommodate the demand is allocated, improving the spectrum usage. When establishing a connection, the EON control layer determines the modulation format to be used and allocates a portion of the spectrum in a sequence of fibers from the source to the destination node providing the user-demanded bandwidth. This is known as the routing, modulation level and spectrum assignment (RMSA) problem. In this work, we firstly review the most important contributions in that area, and then, we propose a novel RMSA algorithm, multi-path best-fit (MP-BF), which uses a split spectrum multi-path strategy together with a spectrum assignment technique (best-fit), and which jointly exploit the flexibility of EONs. A simulation study has been conducted comparing the performance of EONs when using MP-BF with other proposals from the literature. The results of this study show that, by using MP-BF, the network can increase its performance in terms of lightpath request blocking ratio and supported traffic load, without affecting the energy per bit or the computation time required to find a solution

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