ENERGY EFFICIENT CONNECTION PROVISIONING IN IP OVER WDM NETWORKS

Ph.DDOCTOR OF PHILOSOPH

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

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

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

Resource Allocation for Periodic Traffic Demands in WDM Networks

Recent research has clearly established that holding-time-aware routing and wavelength assignment (RWA) schemes lead to significant improvements in resource utilization for scheduled traffic. By exploiting the knowledge of the demand holding times, this thesis proposes new traffic grooming techniques to achieve more efficient resource utilization with the goal of minimizing resources such as bandwidth, wavelength channels, transceivers, and energy consumption. This thesis also introduces a new model, the segmented sliding window model, where a demand may be decomposed into two or more components and each component can be sent separately. This technique is suitable for applications where continuous data transmission is not strictly required such as large file transfers for grid computing. Integer linear program (ILP) formulations and an efficient heuristic are put forward for resource allocation under the proposed segmented sliding window model. It is shown that the proposed model can lead to significantly higher throughput, even over existing holding-time-aware models