Gestion flexible des ressources dans les réseaux de nouvelle génération avec SDN

Abstract : 5G and beyond-5G/6G are expected to shape the future economic growth of multiple vertical industries by providing the network infrastructure required to enable innovation and new business models. They have the potential to offer a wide spectrum of services, namely higher data rates, ultra-low latency, and high reliability. To achieve their promises, 5G and beyond-5G/6G rely on software-defined networking (SDN), edge computing, and radio access network (RAN) slicing technologies. In this thesis, we aim to use SDN as a key enabler to enhance resource management in next-generation networks. SDN allows programmable management of edge computing resources and dynamic orchestration of RAN slicing. However, achieving efficient performance based on SDN capabilities is a challenging task due to the permanent fluctuations of traffic in next-generation networks and the diversified quality of service requirements of emerging applications. Toward our objective, we address the load balancing problem in distributed SDN architectures, and we optimize the RAN slicing of communication and computation resources in the edge of the network. In the first part of this thesis, we present a proactive approach to balance the load in a distributed SDN control plane using the data plane component migration mechanism. First, we propose prediction models that forecast the load of SDN controllers in the long term. By using these models, we can preemptively detect whether the load will be unbalanced in the control plane and, thus, schedule migration operations in advance. Second, we improve the migration operation performance by optimizing the tradeoff between a load balancing factor and the cost of migration operations. This proactive load balancing approach not only avoids SDN controllers from being overloaded, but also allows a judicious selection of which data plane component should be migrated and where the migration should happen. In the second part of this thesis, we propose two RAN slicing schemes that efficiently allocate the communication and the computation resources in the edge of the network. The first RAN slicing scheme performs the allocation of radio resource blocks (RBs) to end-users in two time-scales, namely in a large time-scale and in a small time-scale. In the large time-scale, an SDN controller allocates to each base station a number of RBs from a shared radio RBs pool, according to its requirements in terms of delay and data rate. In the short time-scale, each base station assigns its available resources to its end-users and requests, if needed, additional resources from adjacent base stations. The second RAN slicing scheme jointly allocates the RBs and computation resources available in edge computing servers based on an open RAN architecture. We develop, for the proposed RAN slicing schemes, reinforcement learning and deep reinforcement learning algorithms to dynamically allocate RAN resources.La 5G et au-delà de la 5G/6G sont censées dessiner la future croissance économique de multiples industries verticales en fournissant l'infrastructure réseau nécessaire pour permettre l'innovation et la création de nouveaux modèles économiques. Elles permettent d'offrir un large spectre de services, à savoir des débits de données plus élevés, une latence ultra-faible et une fiabilité élevée. Pour tenir leurs promesses, la 5G et au-delà de la-5G/6G s'appuient sur le réseau défini par logiciel (SDN), l’informatique en périphérie et le découpage du réseau d'accès (RAN). Dans cette thèse, nous visons à utiliser le SDN en tant qu'outil clé pour améliorer la gestion des ressources dans les réseaux de nouvelle génération. Le SDN permet une gestion programmable des ressources informatiques en périphérie et une orchestration dynamique de découpage du RAN. Cependant, atteindre une performance efficace en se basant sur le SDN est une tâche difficile due aux fluctuations permanentes du trafic dans les réseaux de nouvelle génération et aux exigences de qualité de service diversifiées des applications émergentes. Pour atteindre notre objectif, nous abordons le problème de l'équilibrage de charge dans les architectures SDN distribuées, et nous optimisons le découpage du RAN des ressources de communication et de calcul à la périphérie du réseau. Dans la première partie de cette thèse, nous présentons une approche proactive pour équilibrer la charge dans un plan de contrôle SDN distribué en utilisant le mécanisme de migration des composants du plan de données. Tout d'abord, nous proposons des modèles pour prédire la charge des contrôleurs SDN à long terme. En utilisant ces modèles, nous pouvons détecter de manière préemptive si la charge sera déséquilibrée dans le plan de contrôle et, ainsi, programmer des opérations de migration à l'avance. Ensuite, nous améliorons les performances des opérations de migration en optimisant le compromis entre un facteur d'équilibrage de charge et le coût des opérations de migration. Cette approche proactive d'équilibrage de charge permet non seulement d'éviter la surcharge des contrôleurs SDN, mais aussi de choisir judicieusement le composant du plan de données à migrer et l'endroit où la migration devrait avoir lieu. Dans la deuxième partie de cette thèse, nous proposons deux mécanismes de découpage du RAN qui allouent efficacement les ressources de communication et de calcul à la périphérie des réseaux. Le premier mécanisme de découpage du RAN effectue l'allocation des blocs de ressources radio (RBs) aux utilisateurs finaux en deux échelles de temps, à savoir dans une échelle de temps large et dans une échelle de temps courte. Dans l’échelle de temps large, un contrôleur SDN attribue à chaque station de base un certain nombre de RB à partir d'un pool de RB radio partagé, en fonction de ses besoins en termes de délai et de débit. Dans l’échelle de temps courte, chaque station de base attribue ses ressources disponibles à ses utilisateurs finaux et demande, si nécessaire, des ressources supplémentaires aux stations de base adjacentes. Le deuxième mécanisme de découpage du RAN alloue conjointement les RB et les ressources de calcul disponibles dans les serveurs de l’informatique en périphérie en se basant sur une architecture RAN ouverte. Nous développons, pour les mécanismes de découpage du RAN proposés, des algorithmes d'apprentissage par renforcement et d'apprentissage par renforcement profond pour allouer dynamiquement les ressources du RAN

James Michael Curley Scrapbooks Volume 126

The James Michael Curley Scrapbook Collection consists of digitized microfilmed copies of notebooks kept by Curley from 1914-1937. These notebooks contain news clippings that were drawn primarily from Boston newspapers. Curley was born in Roxbury, MA in 1874. He served four terms as Mayor of Boston: 1914–1918, 1922–1926, 1930–1934 and 1946–1950. He also served as Governor of Massachusetts from 1935-1937. In addition to Curley’s political career, the scrapbooks also include clippings about his first wife Mrs. Mary Herlihy Curley (1884-1930) and their daughter Mary D. Curley (1909-1950). A selection of the notebooks were microfilmed in 1962. The microfilm can be found in the holdings of Dinand Library, Holy Cross’s main library. Some clipping were distorted during the microfilming process. This volume includes news clippings from 1935.https://crossworks.holycross.edu/curley_scrapbooks/1156/thumbnail.jp

XTadGAN: Generative Adversarial Networks to Detect Extremely Rare Anomalies

A detecção de anomalias em séries temporais é fundamental para identificar atividades fraudulentas, detetar falhas em processos e monitorizar a saúde de sistemas complexos. As Redes Adversariais Generativas (GANs) têm mostrado resultados promissores neste domínio, superando quer abordagens tradicionais, quer abordagens mais recentes baseadas em machine learning. No entanto, todos estes métodos apresentam dificuldades e limitações em detetar anomalias extremamente raras. Esta dissertação tem como objetivo modificar e estender o modelo TadGAN e investigar o potencial desta abordagem para detetar anomalias extremamente raras (XTadGAN). Além disso, argumentamos que não existe uma metodologia sistemática para avaliar e comparar o desempenho de diferentes métodos de detecção de anomalias, especificamente em relação à sua sensibilidade a variações na frequência de anomalias. Nesse sentido, esta tese também explora o desenvolvimento de um índice de sensibilidade para patamares crescentes de raridade de anomalias, a ser aplicado ao nosso modelo proposto e a outros métodos de referência. O trabalho desenvolvido contribuirá significativamente para o campo da deteção de anomalias, introduzindo uma metodologia robusta e precisa para comparar o desempenho de diferentes abordagens, preenchendo assim uma lacuna crucial na investigação atual. O índice de sensibilidade proposto neste estudo é relevante, uma vez que fornece uma métrica robusta que pode ser utilizada para desenvolver testes de comparação padronizados que permitam entender melhor as vantagens e limitações de cada modelo e orientar investigação futura no sentido de melhorar o desempenho em aplicações reais. Além disso, a análise proposta lançará luz sobre como as GANs em particular, e outros métodos em geral, podem ser otimizados para detetar anomalias extremamente raras em séries temporais de forma mais precisa.Anomaly detection in time series data is critical for identifying fraudulent activities, detecting process failures, and monitoring the health of complex systems. Generative Adversarial Networks (GANs) have recently shown promising results in this domain, outperforming traditional as well as more recent machine learning approaches. However, all of these methods struggle with extremely rare anomalies. This thesis aims to modify and extend the TadGAN model and investigate the potential of this approach to better detect extremely rare anomalies (XTadGAN). Furthermore, we argue that there is an absence of a systematic methodology to assess and compare the performance of different anomaly detection methods, specifically with respect to their sensitivity to variations in the frequency of anomalies. Therefore, this thesis also explores the development of a sensitivity index for increasing orders of anomaly rarity, to be applied to our proposed extended model and other benchmark methods. The developed work will make a valuable contribution to the field of anomaly detection by introducing a robust and accurate framework for comparing the performance of different approaches, hopefully filling a crucial gap in current research. The sensitivity index proposed in this study is significant as it provides a robust metric that can be used to conduct standardized comparison tests to better understand the strengths and limitations of each model and guide future research to improve performance in real-world applications. Moreover, the proposed analysis will shed light on how GANs in particular, and other methods in general, can be optimized to more accurately detect extremely rare anomalies in time series data

St. Cloud Tribune Vol. 17, No. 29, March 11, 1926

Newspaper for St. Cloud (Fla.) from 1926.https://stars.library.ucf.edu/cfm-stcloudtribune/1185/thumbnail.jp

Theoretical advancements and applications in singular spectrum analysis.

Singular Spectrum Analysis (SSA) is a nonparametric time series analysis and forecasting technique which has witnessed an augment in applications in the recent past. The increased application of SSA is closely associated with its superior filtering and signal extraction capabilities which also differentiates it from the classical time series methods. In brief, the SSA process consists of decomposing a time series for signal extraction and then reconstructing a less noisy series which is used for forecasting. The aim of this research is to develop theoretical advancements in SSA, supported by empirical evidence to further promote the value, effectiveness and applicability of the technique in the field of time series analysis and forecasting. To that end, this research has four main contributions. Initially, given the reliance of this research towards improving forecasting processes, it is mandatory to compare and distinguish between the predictive accuracy of forecasts for statistically significant differences. The first contribution of this research is the introduction of a complement statistical test for comparing between the predictive accuracy of two forecasts. The proposed test is based on the principles of cumulative distribution functions and stochastic dominance, and is evaluated via both a simulation study and empirical evidence. Governments, practitioners, researchers and private organizations publish a variety of forecasts each year. Such forecasts are generally computed using multivariate models and are widely used in decision making processes given the considerably high level of anticipated forecast accuracy. The classical multivariate methods consider modelling multiple information pertaining to the same time period or with a time lag into the past. Multivariate Singular Spectrum Analysis (MSSA) is a relatively new and alternative technique for generating forecasts from multiple time series. The second contribution of this research is the introduction of a novel theoretical development which seeks to exploit the information contained in published forecasts (which represent data with a time lag into the future) for generating a new and improved (comparatively more accurate) forecast by taking advantage of the MSSA technique’s capability at modelling time series with different series lengths. In brief, the proposed multivariate theoretical development seeks to exploit the forecastability of forecasts by considering not only official and professional forecasts, but also forecasts obtained via other time series models. The productive application of SSA and MSSA depends largely on the selection of SSA and MSSA parameters, i.e. the Window Length, L, and the number of eigenvalues r which are used for decomposition and reconstruction of time series. Over the years, a variety of mathematically complex, time consuming and labour intensive approaches which require detailed knowledge on the theory underlying SSA have been proposed and developed for the selection of SSA and MSSA parameters. However, the highly labour intensive and complex nature of such approaches have not only discouraged the application of this method by those not conversant with the underlying theory, but also limited SSA and MSSA to offline applications. The third and final contribution of this research proposes new, automated and optimized, SSA and MSSA algorithms for the selection of SSA parameters and thereby enables obtaining optimal SSA and MSSA forecasts (optimized by minimising a loss function). This development opens up the possibility of using SSA and MSSA for online forecasting in the future

An econometric analysis of the forward freight market

The success or failure of a derivatives (futures or forward) contract is determined by its ability to perform its economic functions efficiently, and therefore, to provide benefits to economic agents, over and above the benefits they derive from the spot market. These economic functions are price discovery and risk management through hedging. A considerable amount of empirical research has been directed towards examining these functions in different financial and commodity derivatives markets. The evidence however, on the over-the-counter FFA market is very limited. This thesis therefore, by investigating these issues provides new evidence in the literature for a forward market with some unique characteristics such as the trading of a service. Our empirical results can be summarised as follows. First, the FFA contracts perform their price discovery function efficiently since forward prices contribute to the discovery of new information regarding both current and expected spot prices. Furthermore, most FFA contracts contribute in the volatility of the relevant spot rate, and therefore, further support the notion of price discovery. Second, the introduction of FFA contracts has not had a detrimental effect on the volatility of the underlying spot market. On the contrary, it appears that there has been an improvement in the way that news is transmitted into prices following the onset of FFA trading. Third, FFA prices fail to reduce market risk to the extent evidenced in other markets in the literature and, hence, the FFA market does not perform its risk management function satisfactorily; this is thought to be the result of the lack of the cost-of-carry arbitrage relationship of storable assets that keeps spot and derivatives prices close together. Fourth, there seems to be a positive relationship between bid-ask spreads and expected price volatility in most FFA trading routes. Finally, in the routes where the cointegrating vector is restricted to be the lagged basis, the VECM generates more accurate forecasts than the VAR model and in the routes where the cointegrating vector is not restricted to be the lagged basis the VAR generates more accurate forecasts than the VECM model