As a recently emerging network paradigm, Software-Defined Networking (SDN) has attracted considerable attention from both industry and academia. The most significant advantage of SDN is that the paradigm disassociates the control logic (i.e., control plane) from the forwarding process (i.e., data plane), which are usually integrated into traditional network devices. Thanks to the property of centralized control, SDN enables the flexibility of dispatching flow policies to simplify network management. However, this property also makes the SDN environment vulnerable, which will cause network paralysis when the sole SDN controller runs malfunction. Although several works have been done on deploying multiple controllers to address the failure of a centralized controller, their drawbacks are leading to inefficiency and balance loss of controller utilization, provoking resource idling as well as being incapable to suffice flow outburst. Additionally, the network operators often put a great deal of effort into discovering failure nodes to recover their networks, which can be mitigated by applying failure detection before the network deterioration occurs. Network traffic prediction can serve as a practical approach to evaluate the state of the OpenFlow-based switch and consequently detect SDN node failures in advance. As far as prediction solution is concerned, most researchers investigate either statistical modeling approaches, such as Seasonal Autoregressive Integrated Moving Average (SARIMA), or Artificial Neural Network (ANN) methods, like Long Short-Term Memory (LSTM) Neural Network. Nonetheless, few of them study the model merging these two mechanisms regarding multi-step prediction. This thesis proposes a novel system associated with Network Function Virtualization (NFV) technique to enhance the resilience of SDN network. A hybrid prediction model based on the combination of SARIMA and LSTM is introduced as part of the detection module of this system, where the potential node breakdown can be readily determined so that it can implement smart prevention and fast recovery without human interaction. The results show the proposed scheme improves the performance concerning time complexity compared with that of previous work, reaching up to 95% accuracy while shortening the detection and recovery time by the new combined prediction model.

Every day the dependence on transportation grows as local, regional, national, and international independence increases. Resilient transportation systems are needed to secure the highest possible level of service during disruptive events, including natural and man-made disasters. Because of limited resources, decision makers need guidance on how, when, and where to invest to improve resiliency of their networks. The research objective is to develop a method to assess and quantify resiliency, at pre-event conditions, using a fuzzy inference approach. This research expands previous work, refining key variable definitions, adjusting model interactions, and increasing transparency between metrics. This thesis presents the method and provides an illustrative example of the methodology using the Dominican Republic as a case study. The example explains how a transportation network responds to a disruptive event and how specific investments can increase resiliency of the network. The result of this research is a quantitative basis for decision makers to conduct cost-benefit analysis of resiliency increasing projects.

This dissertation examines the resiliency, efficiency, and environmental impact of barge shipments within the upper Ohio River basin, contrasting findings relevant to this region with assumptions and findings of broader national studies and providing alternative assessment methods. The unique attributes of this region’s inland waterways infrastructure and usage patterns are dominated by the shipment of coal; mines and powerplants with heavy and inflexible dependence on barge shipments; and the constrictions of the waterway infrastructure. Acknowledging these attributes allows for a more accurate assessment in the future of risks due to infrastructure failure and opportunities for efficiency gains. Research goals were set in three major areas: assessing the impact of an extended loss of commercial river navigation due to catastrophic infrastructure failure; assessing current and potential new efficiency metrics for inland waterways freight movement, both in terms of vessel movements and the infrastructure itself; and quantifying and assessing air emissions from regional commercial river traffic. The first research goal was to assess the impact of an extended loss of commercial river navigation due to catastrophic infrastructure failure. The objectives of this research goal were to develop a failure scenario; to develop methodologies to identify at-risk commodity shipments, feasible alternate modes of transportation, supply chain options, and shipping costs; and to develop a methodology to assess the potential closure of facilities impacted by infrastructure failure. A hypothetical failure scenario was assessed for a year-long closure of the Monongahela River between Charleroi and Elizabeth in 2010. For this scenario, the potentially displaced volume of coal shipments from mines to powerplants for a hypothetical river shutdown in 2010 was estimated at 7.0 million tons. The resilience of the impacted facilities, the feasibility of their shipping alternatives, and their ability to re-organize into new markets were assessed, showing heavy predicted impacts for facilities within the hypothetical failure zone, minimal impacts on facilities located below the failure zone, and mixed impacts above the failure zone that depend on facility-specific shipping mode alternatives. Lost revenues were estimated for facilities that close due to an inability to adapt, as well as the replacement cost of towboats and barges trapped by a catastrophic and sudden failure. The aggregate costs to these facilities as a result of a year-long closure in 2010 were estimated at $0.56-1.7 billion. The second research goal was to assess commonly used and potential new efficiency metrics for the inland waterways. Objectives of this goal included the development of methodologies to identify, characterize, and differentiate between vessel and commodity trips; to assess efficiency metrics currently used by USACE and develop improved metrics; and to conduct stochastic time studies of commodity trips to quantify efficiency gains from infrastructure improvements. The vessel and commodity trip analyses provide a unique assessment of the inefficiencies created by the infrastructure bottlenecks within the region. Data from USACE’s Lock Performance Monitoring System and the Energy Information Administration’s Survey 923 were used to characterize and rank the vessel and commodity trips made in 2010 in terms of frequency, tonnage, and ton-miles. Such rankings can be used to prioritize optimization projects and to assess usage patterns. The analyses of various efficiency measures commonly used for the inland waterways were conducted in light of the particular constraints of operation within the upper Ohio River basin. These upriver locks differ in size, requiring vessel operators to optimize the type and configuration of barges used within the region, and causing the regional profile to differ from fleet and flotilla profiles generated at a national level or for other regions. Consideration of these differences allows for more accurate analysis of usage patterns, with implications for efficiency considerations of time and fuel consumption. Stochastic modeling of historical usage patterns allows for the comparison of time requirements with different flotilla configurations and with different infrastructure configurations. A scenario analysis on a typical regional shipment between a coal mine and powerplant was used to demonstrate the method. Results show that completion of a long delayed lock reconstruction project will reduce the time required, and thus the cost and fuel, to move commodities across the region. The savings for a 15-jumbo barge tow moving 200 miles across the study area was estimated to be 17% as a result of completion of the Lower Mon Project. The third research goal was to quantify and assess the regional impact of commercial river traffic on air quality. The specific objectives of this goal were to develop a methodology for calculating emission loadings; and to develop a methodology to assess the impact of vessel emissions on regional air monitors. An estimation of particulate emissions from the vessels’ diesel engines is presented, showing total releases of PM2.5 to be about 360 tons in 2010 across 600 river miles of the upper Ohio River basin, on the same order of magnitude as the major point source releases reported in Allegheny County, and about 25% of releases from a typical 1,700 MW regional powerplant. A screening analysis estimates PM2.5 concentrations attributable from towboats passing through the Liberty-Clairton non-attainment region, predicting that these emission levels would be orders of magnitude below the detection limits of the region’s air monitors, and would be dwarfed by the point source impacting those monitors.

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L'émergence de la technologie de virtualisation des fonctions réseau (NFV) a suscité un vif intérêt autour de la conception, la gestion et le déploiement de services réseau de manière flexible, automatisée et indépendante du fournisseur. La mise en œuvre de la technologie NFV devrait être une solution profitable pour les fournisseurs de services et les clients. Cependant, ce changement de paradigme, amorcé par NFV, nécessite un abandon progressif des services réseau fournis à travers des équipements dédiés. En contrepartie, un environnement totalement ou partiellement virtualisé est proposé pour instancier dynamiquement et à la demande des modules logiciels appelés fonctions de réseau virtuelles (VNF). Cette évolution soulève un ensemble de défis liés au déploiement et à l'exploitation de services, tels que l'orchestration et la gestion, la résilience des services, le contrôle de la qualité de service (QoS), l’approvisionnement des ressources, etc. En outre, la question centrale à résoudre dans le contexte NFV est la suivante : « comment placer et chaîner effacement des fonctions virtuelles d’un service afin de fournir un niveau de qualité demandé par le client tout en optimisant l'utilisation des ressources par le fournisseur de services ? ”.Ainsi, cette thèse étudie la problématique du placement et du chaînage des VNF en tenant compte de certaines exigences de service telles que le délai de bout en bout, la disponibilité du service et la consommation d'énergie, et propose un ensemble d'algorithmes et de mécanismes visant à optimiser le déploiement des services demandés/fournis. Nos contributions dans cette thèse sont triples. Premièrement, nous proposons deux algorithmes de placement et de chaînage de VNF sensibles au délai de bout-en-bout pour des applications temps-réel. Les algorithmes proposés visent à respecter le délai approprié de bout-en-bout qui dépend du service déployé (exemples : VoIP, Streaming, etc.). Deuxièmement, nous présentons une analyse comparative de la disponibilité des services et nous proposons deux mécanismes de placement et de chaînage de VNF pour garantir un niveau prédéfini de disponibilité. L’objectif est de fournir des services résilients en ajustant avec précision les paramètres du schéma de protection (nombre, type, emplacement et taille des instances VNF) nécessaires pour atteindre ce niveau de disponibilité en dépit des défaillances du réseau. Enfin, nous proposons une architecture générale qui explore la possibilité d’étendre le paradigme de la virtualisation à l’Internet des objets (IoT). À cette fin, nous définissons un mécanisme de placement et de chaînage respectant les contraintes énergétiques pour des services IoT. Notre architecture propose de découpler et de virtualiser les fonctionnalités inhérentes à un objet connecté de l’équipement IoT physique. En étendant NFV au domaine IoT, notre solution ouvre de nouvelles perspectives d’application en supportant de nouveaux cas d’usages. / The emergence of Network Function Virtualization (NFV) technology has aroused keen interest to design, manage and deploy network services in a flexible, automated and vendor-agnostic manner. Implementing NFV technology is expected to be a win-win solution for both service providers and costumers. However, this paradigm shift, sparked by NFV, calls for a progressive abandon of network services that are provided as hardware appliance and rather it proposes a fully or partially virtualized environment that offers software modules called Virtual Network Functions (VNFs). This shift rises a set of challenges related to service deployment and operation such as orchestration and management, service resiliency, Quality of Service (QoS) and resource provisioning among others. Furthermore, the core question that needs to be solved within NFV context is “What is the best way to place and chain VNFs that form a service in order to meet Service Level Agreement requirements (costumer side) while optimizing resource usage (service provider side)?”.This thesis investigates the problem of VNF Placement and Chaining considering service requirements such as end-to-end delay, service availability and energy consumption and proposes a set of algorithms and mechanisms that aim to achieve an optimized deployment of the requested/provided services. Our contributions in this thesis are threefold. First, we propose a delay-aware Placement and Chaining algorithms for delay-sensitive applications over NFV networks. The proposed algorithms aim to meet the appropriate end-to-end delay defined according to the deployed service (VoIP, Streaming, etc.). Second, we provide a comprehensive service availability benchmarking and we propose two availability-aware mechanisms for VNFs chain. The aim is to provide resilient service provisioning by fine-tuning the parameters of the protection scheme (the number, the type, the placement and the size of the spare instances) needed to reach a predefined availability level, despite network failures. Finally, we propose a framework architecture that explores the possibility to extend the virtualization paradigm to Internet of Things (IoT). Toward this end, we define an energy-aware Placement and Chaining for IoT services where inherent IoT functionalities are decoupled from specific dedicated IoT devices and instantiated on-demand. By bringing together NFV and IoT paradigms, this extension opens new perspectives and push toward designing new use cases.