On Occupancy Based Randomized Load Balancing for Large Systems with General Distributions

Multi-server architectures are ubiquitous in today's information infrastructure whether for supporting cloud services, web servers, or for distributed storage. The performance of multi-server systems is highly dependent on the load distribution. This is affected by the use of load balancing strategies. Since both latency and blocking are important features, it is most reasonable to route an incoming job to a server that is lightly loaded. Hence a good load balancing policy should be dependent on the states of servers. Since obtaining information about the remaining workload of servers for every arrival is very hard, it is preferable to design load balancing policies that depend on occupancy or the number of progressing jobs of servers. Furthermore, if the system has a large number of servers, it is not practical to use the occupancy information of all the servers to dispatch or route an arrival due to high communication cost. In large-scale systems that have tens of thousands of servers, the policies which use the occupancy information of only a finite number of randomly selected servers to dispatch an arrival result in lower implementation cost than the policies which use the occupancy information of all the servers. Such policies are referred to as occupancy based randomized load balancing policies. Motivated by cloud computing systems and web-server farms, we study two types of models. In the first model, each server is an Erlang loss server, and this model is an abstraction of Infrastructure-as-a-Service (IaaS) clouds. The second model we consider is one with processor sharing servers that is an abstraction of web-server farms which serve requests in a round-robin manner with small time granularity. The performance criterion for web-servers is the response time or the latency for the request to be processed. In most prior works, the analysis of these models was restricted to the case of exponential job length distributions and in this dissertation we study the case of general job length distributions. To analyze the impact of a load balancing policy, we need to develop models for the system's dynamics. In this dissertation, we show that one can construct useful Markovian models. For occupancy based randomized routing policies, due to complex inter-dependencies between servers, an exact analysis is mostly intractable. However, we show that the multi-server systems that have an occupancy based randomized load balancing policy are examples of weakly interacting particle systems. In these systems, servers are interacting particles whose states lie in an uncountable state space. We develop a mean-field analysis to understand a server's behavior as the number of servers becomes large. We show that under certain assumptions, as the number of servers increases, the sequence of empirical measure-valued Markov processes which model the systems' dynamics converges to a deterministic measure-valued process referred to as the mean-field limit. We observe that the mean-field equations correspond to the dynamics of the distribution of a non-linear Markov process. A consequence of having the mean-field limit is that under minor and natural assumptions on the initial states of servers, any finite set of servers can be shown to be independent of each other as the number of servers goes to infinity. Furthermore, the mean-field limit approximates each server's distribution in the transient regime when the number of servers is large. A salient feature of loss and processor sharing systems in the setting where their time evolution can be modeled by reversible Markov processes is that their stationary occupancy distribution is insensitive to the type of job length distribution; it depends only on the average job length but not on the type of the distribution. This property does not hold when the number of servers is finite in our context due to lack of reversibility. We show however that the fixed-point of the mean-field is insensitive to the job length distributions for all occupancy based randomized load balancing policies when the fixed-point is unique for job lengths that have exponential distributions. We also provide some deeper insights into the relationship between the mean-field and the distributions of servers and the empirical measure in the stationary regime. Finally, we address the accuracy of mean-field approximations in the case of loss models. To do so we establish a functional central limit theorem under the assumption that the job lengths have exponential distributions. We show that a suitably scaled fluctuation of the stochastic empirical process around the mean-field converges to an Ornstein-Uhlenbeck process. Our analysis is also valid for the Halfin-Whitt regime in which servers are critically loaded. We then exploit the functional central limit theorem to quantify the error between the actual blocking probability of the system with a large number of servers and the blocking probability obtained from the fixed-point of the mean-field. In the Halfin-Whitt regime, the error is of the order inverse square root of the number of servers. On the other hand, for a light load regime, the error is smaller than the inverse square root of the number of servers

Performance Modelling and Optimisation of Multi-hop Networks

A major challenge in the design of large-scale networks is to predict and optimise the total time and energy consumption required to deliver a packet from a source node to a destination node. Examples of such complex networks include wireless ad hoc and sensor networks which need to deal with the effects of node mobility, routing inaccuracies, higher packet loss rates, limited or time-varying effective bandwidth, energy constraints, and the computational limitations of the nodes. They also include more reliable communication environments, such as wired networks, that are susceptible to random failures, security threats and malicious behaviours which compromise their quality of service (QoS) guarantees. In such networks, packets traverse a number of hops that cannot be determined in advance and encounter non-homogeneous network conditions that have been largely ignored in the literature. This thesis examines analytical properties of packet travel in large networks and investigates the implications of some packet coding techniques on both QoS and resource utilisation. Specifically, we use a mixed jump and diffusion model to represent packet traversal through large networks. The model accounts for network non-homogeneity regarding routing and the loss rate that a packet experiences as it passes successive segments of a source to destination route. A mixed analytical-numerical method is developed to compute the average packet travel time and the energy it consumes. The model is able to capture the effects of increased loss rate in areas remote from the source and destination, variable rate of advancement towards destination over the route, as well as of defending against malicious packets within a certain distance from the destination. We then consider sending multiple coded packets that follow independent paths to the destination node so as to mitigate the effects of losses and routing inaccuracies. We study a homogeneous medium and obtain the time-dependent properties of the packet’s travel process, allowing us to compare the merits and limitations of coding, both in terms of delivery times and energy efficiency. Finally, we propose models that can assist in the analysis and optimisation of the performance of inter-flow network coding (NC). We analyse two queueing models for a router that carries out NC, in addition to its standard packet routing function. The approach is extended to the study of multiple hops, which leads to an optimisation problem that characterises the optimal time that packets should be held back in a router, waiting for coding opportunities to arise, so that the total packet end-to-end delay is minimised

Global attraction of ODE-based mean field models with hyperexponential job sizes

Mean field modeling is a popular approach to assess the performance of large scale computer systems. The evolution of many mean field models is characterized by a set of ordinary differential equations that have a unique fixed point. In order to prove that this unique fixed point corresponds to the limit of the stationary measures of the finite systems, the unique fixed point must be a global attractor. While global attraction was established for various systems in case of exponential job sizes, it is often unclear whether these proof techniques can be generalized to non-exponential job sizes. In this paper we show how simple monotonicity arguments can be used to prove global attraction for a broad class of ordinary differential equations that capture the evolution of mean field models with hyperexponential job sizes. This class includes both existing as well as previously unstudied load balancing schemes and can be used for systems with either finite or infinite buffers. The main novelty of the approach exists in using a Coxian representation for the hyperexponential job sizes and a partial order that is stronger than the componentwise partial order used in the exponential case.Comment: This paper was accepted at ACM Sigmetrics 201

EUROPEAN CONFERENCE ON QUEUEING THEORY 2016

International audienceThis booklet contains the proceedings of the second European Conference in Queueing Theory (ECQT) that was held from the 18th to the 20th of July 2016 at the engineering school ENSEEIHT, Toulouse, France. ECQT is a biannual event where scientists and technicians in queueing theory and related areas get together to promote research, encourage interaction and exchange ideas. The spirit of the conference is to be a queueing event organized from within Europe, but open to participants from all over the world. The technical program of the 2016 edition consisted of 112 presentations organized in 29 sessions covering all trends in queueing theory, including the development of the theory, methodology advances, computational aspects and applications. Another exciting feature of ECQT2016 was the institution of the Takács Award for outstanding PhD thesis on "Queueing Theory and its Applications"

Joint location and inventory models and algorithms for deployment of hybrid electric vehicle charging stations.

This thesis describes a study of a novel concept of hybrid electric vehicle charging stations in which two types of services are offered: battery swapping and fast level-3 DC charging. The battery swapping and fast-charging service are modeled by using the M/G/s/s model and the M/G/s/$\infty$ model, respectively. In particular, we focus on the operations of joint battery swapping and fast charging services, develop four joint locations and inventory models: two for the deployment of battery swapping service, two for the deployment of hybrid electric vehicle charging service. The first model for each deployment system considers a service-level constraint for battery swapping and hybrid charging service, whereas the second for each deployment system considers total sojourn time in stations. The objective of all four models is to minimize total facility setup cost plus battery and supercharger purchasing cost. The service level, which is calculated by the Erlang loss function, depends on the stockout probability for batteries with enough state of charge (SOC) for the battery swapping service and the risk of running out of superchargers for the quick charging service. The total sojourn time is defined as the sum of the service time and the waiting time in the station. Metaheuristic algorithms using a Tabu search are developed to tackle the proposed nonlinear mixed-integer optimization model. Computational results on randomly generated instances and on a real-world case comprised of 714,000 households show the efficacy of proposed models and algorithms

Call Center Experience Optimization: A Case for a Virtual Predictive Queue

The evolution of the call center into contact centers and the growth of their use in providing customer-facing service by many companies has brought considerable capabilities in maintaining customer relationships but it also has brought challenges in providing quality service when call volumes are high. Limited in their ability to provide service at all times to all customers, companies are forced to balance the costs associated with hiring more customer service representatives and the quality of service provided by a fewer number. A primary challenge when there are not enough customer service representatives to engage the volume of callers in a timely manner is the significant wait times that can be experienced by many customers. Normally, callers are handled in accordance with a first-come, first-served policy with exceptions being skill-based routing to those customer service representatives with specialized skills. A proposed call center infrastructure framework called a Virtual Predictive Queue (VPQ) can allow some customers to benefit from a shorter call queue wait time. This proposed system can be implemented within a call center’s Automatic Call Distribution (ACD) device associated with computer telephony integration (CTI) and theoretically will not violate a first-come, first served policy

Inferring Queueing Network Models from High-precision Location Tracking Data

Stochastic performance models are widely used to analyse the performance and reliability of systems that involve the flow and processing of customers. However, traditional methods of constructing a performance model are typically manual, time-consuming, intrusive and labour-intensive. The limited amount and low quality of manually-collected data often lead to an inaccurate picture of customer flows and poor estimates of model parameters. Driven by advances in wireless sensor technologies, recent real-time location systems (RTLSs) enable the automatic, continuous and unintrusive collection of high-precision location tracking data, in both indoor and outdoor environment. This high-quality data provides an ideal basis for the construction of high-fidelity performance models. This thesis presents a four-stage data processing pipeline which takes as input high-precision location tracking data and automatically constructs a queueing network performance model approximating the underlying system. The first two stages transform raw location traces into high-level “event logs” recording when and for how long a customer entity requests service from a server entity. The third stage infers the customer flow structure and extracts samples of time delays involved in the system; including service time, customer interarrival time and customer travelling time. The fourth stage parameterises the service process and customer arrival process of the final output queueing network model. To collect large-enough location traces for the purpose of inference by conducting physical experiments is expensive, labour-intensive and time-consuming. We thus developed LocTrack- JINQS, an open-source simulation library for constructing simulations with location awareness and generating synthetic location tracking data. Finally we examine the effectiveness of the data processing pipeline through four case studies based on both synthetic and real location tracking data. The results show that the methodology performs with moderate success in inferring multi-class queueing networks composed of single-server queues with FIFO, LIFO and priority-based service disciplines; it is also capable of inferring different routing policies, including simple probabilistic routing, class-based routing and shortest-queue routing

Stochastic optimization of staffing for multiskill call centers

Dans cette thèse, nous étudions le problème d’optimisation des effectifs dans les centres d’appels, dans lequel nous visons à minimiser les coûts d’exploitation tout en offrant aux clients une qualité de service (QoS) élevée. Nous introduisons également l'utilisation de contraintes probabilistes qui exigent que la qualité de service soit satisfaite avec une probabilité donnée. Ces contraintes sont adéquates dans le cas où la performance est mesurée sur un court intervalle de temps, car les mesures de QoS sont des variables aléatoires sur une période donnée. Les problèmes de personnel proposés sont difficiles en raison de l'absence de forme analytique pour les contraintes probabilistes et doivent être approximées par simulation. En outre, les fonctions QoS sont généralement non linéaires et non convexes. Nous considérons les problèmes d’affectation personnel dans différents contextes et étudions les modèles proposés tant du point de vue théorique que pratique. Les méthodologies développées sont générales, en ce sens qu'elles peuvent être adaptées et appliquées à d'autres problèmes de décision dans les systèmes de files d'attente. La thèse comprend trois articles traitant de différents défis en matière de modélisation et de résolution de problèmes d'optimisation d’affectation personnel dans les centres d'appels à compétences multiples. Les premier et deuxième article concernent un problème d'optimisation d'affectation de personnel en deux étapes sous l'incertitude. Alors que dans le second, nous étudions un modèle général de programmation stochastique discrète en deux étapes pour fournir une garantie théorique de la consistance de l'approximation par moyenne échantillonnale (SAA) lorsque la taille des échantillons tend vers l'infini, le troisième applique l'approche du SAA pour résoudre le problème d’optimisation d'affectation de personnel en deux étapes avec les taux d’arrivée incertain. Les deux articles indiquent la viabilité de l'approche SAA dans notre contexte, tant du point de vue théorique que pratique. Pour être plus précis, dans le premier article, nous considérons un problème stochastique discret général en deux étapes avec des contraintes en espérance. Nous formulons un problème SAA avec échantillonnage imbriqué et nous montrons que, sous certaines hypothèses satisfaites dans les exemples de centres d'appels, il est possible d'obtenir les solutions optimales du problème initial en résolvant son SAA avec des échantillons suffisamment grands. De plus, nous montrons que la probabilité que la solution optimale du problème de l’échantillon soit une solution optimale du problème initial tend vers un de manière exponentielle au fur et à mesure que nous augmentons la taille des échantillons. Ces résultats théoriques sont importants, non seulement pour les applications de centre d'appels, mais également pour d'autres problèmes de prise de décision avec des variables de décision discrètes. Le deuxième article concerne les méthodes de résolution d'un problème d'affectation en personnel en deux étapes sous incertitude du taux d'arrivée. Le problème SAA étant coûteux à résoudre lorsque le nombre de scénarios est important. En effet, pour chaque scénario, il est nécessaire d'effectuer une simulation pour estimer les contraintes de QoS. Nous développons un algorithme combinant simulation, génération de coupes, renforcement de coupes et décomposition de Benders pour résoudre le problème SAA. Nous montrons l'efficacité de l'approche, en particulier lorsque le nombre de scénarios est grand. Dans le dernier article, nous examinons les problèmes de contraintes en probabilité sur les mesures de niveau de service. Notre méthodologie proposée dans cet article est motivée par le fait que les fonctions de QoS affichent généralement des courbes en S et peuvent être bien approximées par des fonctions sigmoïdes appropriées. Sur la base de cette idée, nous avons développé une nouvelle approche combinant la régression non linéaire, la simulation et la recherche locale par région de confiance pour résoudre efficacement les problèmes de personnel à grande échelle de manière viable. L’avantage principal de cette approche est que la procédure d’optimisation peut être formulée comme une séquence de simulations et de résolutions de problèmes de programmation linéaire. Les résultats numériques basés sur des exemples réels de centres d'appels montrent l'efficacité pratique de notre approche. Les méthodologies développées dans cette thèse peuvent être appliquées dans de nombreux autres contextes, par exemple les problèmes de personnel et de planification dans d'autres systèmes basés sur des files d'attente avec d'autres types de contraintes de QoS. Celles-ci soulèvent également plusieurs axes de recherche qu'il pourrait être intéressant d'étudier. Par exemple, une approche de regroupement de scénarios pour atténuer le coût des modèles d'affectation en deux étapes, ou une version d'optimisation robuste en distribution pour mieux gérer l'incertitude des données.In this thesis, we study the staffing optimization problem in multiskill call centers, in which we aim at minimizing the operating cost while delivering a high quality of service (QoS) to customers. We also introduce the use of chance constraints which require that the QoSs are met with a given probability. These constraints are adequate in the case when the performance is measured over a short time interval as QoS measures are random variables in a given time period. The proposed staffing problems are challenging in the sense that the stochastic constraints have no-closed forms and need to be approximated by simulation. In addition, the QoS functions are typically non-linear and non-convex. We consider staffing optimization problems in different settings and study the proposed models in both theoretical and practical aspects. The methodologies developed are general, in the sense that they can be adapted and applied to other staffing/scheduling problems in queuing-based systems. The thesis consists of three articles dealing with different challenges in modeling and solving staffing optimization problems in multiskill call centers. The first and second articles concern a two-stage staffing optimization problem under uncertainty. While in the first one, we study a general two-stage discrete stochastic programming model to provide a theoretical guarantee for the consistency of the sample average approximation (SAA) when the sample sizes go to infinity, the second one applies the SAA approach to solve the two-stage staffing optimization problem under arrival rate uncertainty. Both papers indicate the viability of the SAA approach in our context, in both theoretical and practical aspects. To be more precise, in the first article, we consider a general two-stage discrete stochastic problem with expected value constraints. We formulate its SAA with nested sampling. We show that under some assumptions that hold in call center examples, one can obtain the optimal solutions of the original problem by solving its SAA with large enough sample sizes. Moreover, we show that the probability that the optimal solution of the sample problem is an optimal solution of the original problem, approaches one exponentially fast as we increase the sample sizes. These theoretical findings are important, not only for call center applications, but also for other decision-making problems with discrete decision variables. The second article concerns solution methods to solve a two-stage staffing problem under arrival rate uncertainty. It is motivated by the fact that the SAA version of the two-stage staffing problem becomes expensive to solve with a large number of scenarios, as for each scenario, one needs to use simulation to approximate the QoS constraints. We develop an algorithm that combines simulation, cut generation, cut strengthening and Benders decomposition to solve the SAA problem. We show the efficiency of the approach, especially when the number of scenarios is large. In the last article, we consider problems with chance constraints on the service level measures. Our methodology proposed in this article is motivated by the fact that the QoS functions generally display ``S-shape'' curves and might be well approximated by appropriate sigmoid functions. Based on this idea, we develop a novel approach that combines non-linear regression, simulation and trust region local search to efficiently solve large-scale staffing problems in a viable way. The main advantage of the approach is that the optimization procedure can be formulated as a sequence of steps of performing simulation and solving linear programming models. Numerical results based on real-life call center examples show the practical viability of our approach. The methodologies developed in this thesis can be applied in many other settings, e.g., staffing and scheduling problems in other queuing-based systems with other types of QoS constraints. These also raise several research directions that might be interesting to investigate. For examples, a clustering approach to mitigate the expensiveness of the two-stage staffing models, or a distributionally robust optimization version to better deal with data uncertainty

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