On IP over WDM burst-switched long haul and metropolitan area networks

The IP over Wavelength Division Multiplexing (WDM) network is a natural evolution ushered in by the phenomenal advances in networking technologies and technical breakthroughs in optical communications, fueled by the increasing demand in the reduction of operation costs and the network management complexity. The unprecedented bandwidth provisioning capability and the multi-service supportability of the WDM technology, in synergy with the data-oriented internetworking mechanisms, facilitates a common shared infrastructure for the Next Generation Internet (NGJ). While NGI targets to perform packet processing directly on the optical transport layer, a smooth evolution is critical to success. Intense research has been conducted to design the new generation optical networks that retain the advantages of packet-oriented transport prototypes while rendering elastic network resource utilization and graded levels of service. This dissertation is focused on the control architecture, enabling technologies, and performance analysis of the WDM burst-switched long haul and Metropolitan Area Networks (MANs). Theoretical analysis and simulation results are reported to demonstrate the system performance and efficiency of proposed algorithms. A novel transmission mechanism, namely, the Forward Resource Reservation (ERR) mechanism, is proposed to reduce the end-to-end delay for an Optical Burst Switching (OBS)-based IP over WDM system. The ERR scheme adopts a Linear Predictive Filter and an aggressive reservation strategy for data burst length prediction and resource reservation, respectively, and is extended to facilitate Quality of Service (QoS) differentiation at network edges. The ERR scheme improves the real-time communication services for applications with time constraints without deleterious system costs. The aggressive strategy for channel holding time reservations is proposed. Specifically, two algorithms, the success probability-driven (SPD) and the bandwidth usage-driven (BUD) ones, are proposed for resource reservations in the FRRenabled scheme. These algorithms render explicit control on the latency reduction improvement and bandwidth usage efficiency, respectively, both of which are important figures of performance metrics. The optimization issue for the FRR-enabled system is studied based on two disciplines - addressing the static and dynamic models targeting different desired objectives (in terms of algorithm efficiency and system performance), and developing a \u27\u27crank back\u27\u27 based signaling mechanism to provide bandwidth usage efficiency. The proposed mechanisms enable the network nodes to make intelligent usage of the bandwidth resources. In addition, a new control architecture with enhanced address resolution protocol (E-ARP), burst-based transmission, and hop-based wavelength allocation is proposed for Ethernet-supported IP over WDM MANs. It is verified, via theoretical analysis and simulation results, that the E-ARP significantly reduces the call setup latency and the transmission requirements associated with the address probing procedures; the burst-based transport mechanism improves the network throughput and resource utilization; and the hop-based wavelength allocation algorithm provides bandwidth multiplexing with fairness and high scalability. The enhancement of the Ethernet services, in tandem with the innovative mechanisms in the WDM domain, facilitates a flexible and efficient integration, thus making the new generation optical MAN optimized for the scalable, survivable, and IP-dominated network at gigabit speed possible

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

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Producción CientíficaArtificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks. The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation of nonlinearities, and quality of transmission estimation. Then, applications related to optical network control and management are also reviewed, including topics like optical network planning and operation in both transport and access networks. Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future.Ministerio de Economía, Industria y Competitividad (Project EC2014-53071-C3-2-P, TEC2015-71932-REDT

A GMPLS/OBS network architecture enabling QoS-aware end-to-end burst transport

This paper introduces a Generalized Multi-Protocol Label Switching (GMPLS)-enabled Optical Burst Switched (OBS) network architecture featuring end-to-end QoS-aware burst transport services. This is achieved by setting up burst Label Switched Paths (LSPs) properly dimensioned to match specific burst drop probability requirements. These burst LSPs are used for specific guaranteed QoS levels, whereas the remaining network capacity can be left for best-effort burst support. Aiming to ensure the requested burst drop probability figures even under bursty traffic patterns, burst LSPs’ performance is continuously monitored. Therefore, GMPLS-driven capacity reconfigurations can be dynamically triggered whether unfavorable network conditions are detected. Through the paper, the GMPLS/OBS architecture is firstly detailed, followed by the presentation of the optimized methods used for the initial burst LSP dimensioning. The successful network performance is finally illustrated by simulations on several network scenarios.Preprin

Novel ROADM modelling with WSS and OBS to Improve Routing Performance in Optical Network

The area of optical network is on constant point of focus among the research communities owing to potential advantage of long distance communication as well as the routing issues associated with it. Majority of the implemented techniques calls for using expensive hardware or adopting narrowed technical assumptions, for which reason the routing performance couldn’t be optimized to a large extent. The present manuscript proposes a novel technique called as Optimized Routing for Peak Traffic under the scenario of uncertainties in the existing traffic scenario. The proposed solution presents some potential principle that is incorporated in the ROADM design for enhancing the capabilities of the proposed system.  The system is evaluated under multiple scenarios of analysis using blocking probabilities, OSNR, bit error rate, iteration etc. The outcome of the proposed system is compared with the one of the existing significant study to benchmark it