Elastic deformation has wide applications in medical simulations, therefore when it comes to designing physical behavior of objects for realistic training applications, determining material parameters so that objects behave in a desired way becomes a crucial. In this work we consider the problem of elasticity parameter estimation for deformable bodies, which is important for accurate medical simulations. Our work has two major steps: the first step is the data acquisition and preparation, and the second step is the parameter estimation. The experimental setup for data acquisition consists of depth and force sensors. Surface deformations are acquired as a series of images along with the corresponding applied forces. The contact point of the force sensor on the surface is found visually and the corresponding applied forces are acquired directly from the force sensor. A complete mesh deformation which is obtained from a surface tracking method is employed along with force measurements in the elasticity parameter estimation method. Our approach to estimate the physical material properties is based on an inverse linear finite element method. We have experimented with two approaches to optimize the elasticity parameters: a linear iterative method and a force-displacement error minimization method. The two methods were tested on the simulation data, and the second method was tested on three deformable objects. The results are presented for the data captured by two different depth sensors. The result is a set of two parameters, the Young's modulus and the Poisson's ratio, which represents the stiffness of the object under test.

The aim of my diploma thesis is to introduce cloud computing as an alternative to traditional internal information technology and its benefits for a company. Diploma thesis focuses on three main goals. The first one concerns advantages and disadvantages of cloud computing in comparison to internal information technology. The second one is identification of possible processes and services available for migration into cloud computing. The third goal of the diploma thesis is development of investment analysis which compares not only initial costs on internal information technology and cloud computing, but also costs of both variants within three years. The main contribution of the diploma thesis is to define whether the cloud computing is economically beneficial for the company or not. The argument for categorizing cloud computing is in the reason that the company does not have to use all services within the cloud computing but only these, which the company considers as the most beneficial from cost and operation point of view. Another contribution of the diploma thesis is deployment of data, services and processes into a chosen cloud computing platform. Investment analysis allows through cost comparison of both options understand whether it is more beneficial to choose cloud computing or internal Information Technology platform. During this decision making the company also considers its business character and the fact whether the company operates locally or globally.

Measurements made by the AVHRR (Advanced Very High Resolution Radiometer) on board of five NOAA polar orbiting satellites were used to retrieve cloud optical depth (τ) and cloud droplet effective radius (r[sub eff]) for marine boundary layer clouds over the Pacific Ocean west of California and over the Southern Ocean near Tasmania. Retrievals were obtained for 21 days of data acquired between 1987 and 1995 from which over 300 subscenes ~ 256 km x 256 km in size were extracted. On this spatial scale cloud fields were found to have mean τ between 8 and 32 and mean r[sub eff] between 6 and 17 μm. The frequency distribution of τ is well approximated by a two parameter gamma distribution. The gamma distribution also provides a good fit to the observed r[sub eff] distribution if the distribution is symmetric or positively skewed but fails for negatively skewed or bi-modal distributions of r[sub eff] which were also observed. The retrievals show a relationship between τ and r[sub eff] which is consistent with a simple "reference" cloud model with reff ~ r[sup 1 / 5]. The proportionality constant depends on cloud droplet number concentration N and cloud subadiabaticity β through the parameter N[sub sat] = N/ [sq rt. Β]. Departures from the reference behaviour occur in scenes with spatially coherent N[sub sat] regimes, separated by a sharp boundary. AVHRR imagery is able to separate two N[sub sat] regimes if they differ by at least 30% in most cases. Satellite retrievals of τ and r[sub eff] were compared with in situ aircraft measurement near Tasmania. The retrievals overestimated r[sub eff] by 0.7 to 3.6 μm on different flights, in agreement with results from earlier comparison studies. The r[sub eff] overestimation was found to be an offset independent of τ. The reference cloud model and the N[sub sat] retrieval were tested on aircraft data and yield results consistent with direct in situ measurements of N and 8. Spectral and multifractal analyses of the spatial structure of cloud visible radiance, τ and r[sub eff] fields in 34 satellite scenes revealed scale breaks at 3 to 2 km in all analysed scenes in agreement with some earlier observations (Davis et al. (1996a)) but in contrast with other work (Lovejoy et al. (1993)). The nonstationarity H(1) and intermittency C(1) parameters were computed for the 34 scenes, stratified using the reference cloud model and according to mean τ and r[sub eff]. Similar values of H(1) and C(1) were found in all these categories. These measurements of the frequency distribution and spatial variability of τ, r[sub eff], liquid water path (Iwp), and N[sub sat] can be used to place constraints on mesoscale models of layer clouds. / Arts, Faculty of / Geography, Department of / Graduate

The current infrastructure as a service (IaaS) cloud systems, allow users to load their own virtual machines. However, most of these systems do not provide users with an automatic mechanism to load a network topology of virtual machines. In order to specify and implement the network topology, we use software switches and routers as network elements. Before running a group of virtual machines, the user needs to set up the system once to specify a network topology of virtual machines. Then, given the user’s request for running a specific topology, our system loads the appropriate virtual machines (VMs) and also runs separated VMs as software switches and routers. Furthermore, we have developed a manager that handles physical hardware failure situations. This system has been designed in order to allow users to use the system without knowing all the internal technical details.

With the emergence of cloud computing, many applications are migrated onto clouds. Video-on-demand (VoD) can be implemented on the cloud platform with geo-distributed cloud servers to serve worldwide users. New videos are distributed to these geo-distributed cloud servers. This distribution should be properly scheduled based on the videos' sizes, videos' popularities and the available network bandwidth, so that the mean completion time is minimized. We formulate this problemas a preemptive scheduling problem, prove that it is NP-hard, and design a heuristic scheduling algorithm to solve it. This algorithm iteratively determines: 1) themost preferred file to be received by the most preferred destination server by pairwise analysis and PageRank, and 2) the most preferred source servers which can transmit this file to the most preferred destination server with appropriate data rates. The simulation results demonstrate that the proposed scheduling algorithm gives much smaller mean completion time than four scheduling algorithms.

The response of cloud processes to an aerosol perturbation is one of the largest uncertainties in the anthropogenic forcing of the climate. It occurs at a variety of timescales, from the near-instantaneous Twomey effect to the longer timescales required for cloud adjustments. Understanding the temporal evolution of cloud properties following an aerosol perturbation is necessary to interpret the results of so-called “natural experiments” from a known aerosol source such as a ship or industrial site. This work uses reanalysis wind fields and ship emission information matched to observations of ship tracks to measure the timescales of cloud responses to aerosol in instantaneous (or“snapshot”) images taken by polar-orbiting satellites. As in previous studies, the local meteorological environment is shown to have a strong impact on the occurrence and properties of ship tracks, but there is a strong time dependence in their properties. The largest droplet number concentration (Nd) responses are found within 3 h of emission, while cloud adjustments continue to evolve over periods of 10 h or more. Cloud fraction is increased within the early life of ship tracks, with the formation of ship tracks in otherwise clear skies indicating that around 5 %–10%of clear-sky cases in this region may be aerosol-limited. The liquid water path (LWP) enhancement and the Nd– LWP sensitivity are also time dependent and strong functions of the background cloud and meteorological state. The nearinstant response of the LWP within ship tracks may be evidence of a bias in estimates of the LWP response to aerosol derived from natural experiments. These results highlight the importance of temporal development and the background cloud field for quantifying the aerosol impact on clouds, even in situations where the aerosol perturbation is clear.

Includes bibliographical references (pages 139-143). / The central theme of this study concerns the use of classification schemes on weather modification experiments designed to investigate the possibility of increasing rainfall from individual cumuli or cumulus cloud systems. The principal objectives of these experiments are the evaluation of treatment effects and the identification of situations where seeding with artificial ice-nuclei is likely to have positive results. The classification of experimental units into categories that are associated with significantly different physical processes aids the evaluation process and the formulation of seeding strategies in the desired manner. As part of this study a classification scheme, which stratifies convective events on the basis of the synoptic situations which give rise to and maintain the convection, is formulated. In chapter seven and eight this scheme and another scheme presently being employed on a cumulus cloud weather modification experiment are examined statistically. Investigations show that the formulated scheme attains the objectives of classification to a greater degree. Certain attributes of the second scheme, permit the development of a classification procedure whereby the most effective stratification of experimental units can be accomplished.

Le Cloud Computing représente une des plus importantes avancées numériques de ces dix dernières années. Le modèle de service offert par le cloud computing est basé sur une allocation élastique et à la demande des ressources et une facturation au plus juste de leur utilisation. Plusieurs catégories d’application sont en train de migrer vers le cloud (par exemple : les NFV et les applications du Big Data). D’autres domaines applicatifs, soumis à une législation plus stricte, sont plus frileux. Leurs exigences sont souvent liées à des problématiques de sécurité et/ou à la non satisfaction par les ressources proposées par un cloud unique, peuvent trouver des réponses dans l’utilisation conjointe de plusieurs fournisseurs de cloud computing (CSPs). L’exploitation simultanée, flexible, efficace et simplifiée de plusieurs clouds requiert des propriétés qui assurent sa viabilité et son acceptation tout d’abord par les fournisseurs de clouds qui proposent des offres hétérogènes et non interopérables et qui souvent pour des raisons commerciales, ne sont pas disposés à coopérer pour faciliter le multi-cloud « à la carte » ; mais aussi du point de vue du consommateur de services cloud dont les contraintes sont le temps de mise en service et le besoin d’exprimer les besoins en services et leur configuration de manière simple et transparente et de permettre la définition de configurations de déploiement adaptées aux besoins de chaque consommateur, comparables à celles offertes par les clouds privés et optimisées pour tirer profit des spécificités de chaque fournisseur de cloud. Dans cette thèse, nous proposons un framework permettant d’exprimer indépendamment de toute solution sous-jacente les besoins en services et en fournisseurs de cloud et de générer efficacement des infrastructures de déploiement extensibles, adaptatives et contrôlables par le consommateur. Cette solution est composée de MANTUS, un outil permettant l’expression des besoins et la génération automatique d’instances, d’ORBITS, des infrastructures de déploiement multi-cloud adaptables dynamiquement grâce aux mécanismes autonomiques offerts par MANTUS.La solution proposée se décline en plusieurs contributions : tout d’abord l’extension d’un framework IaC (Infrastructure as a Code) existant, dédié à la construction de solutions multi-cloud par des mécanismes de tissage, caractéristiques de la programmation par aspects (AOP), permettant ainsi d’injecter et d’extraire des ressources à la demande. Cette extension, expérimentée sur le framework TOSCA a nécessité la proposition du langage TML (Tosca Manipulation Language) permettant de formaliser et de manipuler ces extensions. Les expérimentations effectuées montrent une surcharge acceptable induite par le mécanisme de tissage. La seconde contribution a été de doter le framework IaC TOSCA d’un outil d’expression des besoins et d’un algorithme de matching en terme, d’une part, de nombre et de caractéristiques de fournisseur de cloud -notamment la localisation- et d’autre part en terme de caractéristiques des services proposés par ceux-ci, permettant de trouver la configuration multi-cloud la plus adaptée. La troisième contribution est la définition d’une architecture « template » multi-couche, ORBITS, offrant des mécanismes d’interopérabilité inter-cloud et une vision haut niveau indépendante des clouds sous jacents. Enfin la dernière contribution proposée est la pile de virtualisation U-cloud et les protocoles liée à son déploiement qui permettent de délimiter les zones contrôlables par le consommateur des zones contrôlées par le provider. Cette proposition est basée sur l’utilisation conjointe de la virtualisation imbriquée (Nested Virtualization) et des micro-hyperviseurs réduisant la zone d’attaque (TCB). / Cloud Computing represents one of the most important changes in information and communications technology (ICT) of the latest ten years. However, after a decade since its commercial debut, there are still several applications that cloud computing is not able to fully serve. These are the applications that, due to their particularly stringent requirements, must rely simultaneously on multiple Cloud Service Providers (CSPs), rather than only one. Multiple CSPs can in fact offer a better availability, improve QoS, and break the business dependence w.r.t. a single CSP. A cloud infrastructure based on multiple CSPs is called multi-cloud.Despite the benefits of multi-clouds, organisations (i.e developers and operators of IT services) seldom accept the challenge of building applications and crossing multiple CSP domains. In fact, multi-CSP architectures come at the cost of more complex applications and the logic to in terms of architecture and performance optimization.Recently, Multi-cloud client-oriented architectures emerged as important approach to construct multi-cloud applications. It provides cloud consumers a mechanism to allocate resources over multiple CSPs without requiring any cooperation among the CSPs themselves.In particular, Infrastructure as Code-based (IaC-based) represent the reference paradigm when building multi-cloud applications.However, the adoption of IaC in the multi-cloud context us limited by the fact that the cloud consumer cannot easily reuse the infrastructure code across different applications. This is due to two major problems, which we investigate in this manuscript.First, infrastructure are composed of functional (e.g. resources for applications) and non-functional services (e.g. monitoring). Non-functional related code should be shared at most across different applications and cloud consumers. However, this separation between functional and non-functional code is often blurred and, therefore, non-functional code is hard to be shared across them. This enables the possibility of code re-using across different cloud consumers (e.g., their different multi-cloud infrastructures) and static analysis of infrastructure templates. Furthermore, we present a TML (TOSCA Manipulation Language) aspect specification language to dynamically inject “non-functional” services to the virtual multi-cloud infrastructure. Secondly, the multi-cloud paradigm is limited by the “least common denominator” barrier. The cloud consumer can hardly obtain an optimized usage of resources and services through existing IaC frameworks. Despite compatible with different CSPs, those frameworks do not specialize the output according to deployment context.To tackle the “under-specialization” of multi-cloud templates, we introduce a “context-based matching” scheduling algorithm to select the most compelling set of CSPs according to the cloud consumer needs.To validate such contributions, we defined an end-to-end workflow to optimize a multi-cloud infrastructure definition. More precisely, in our model, the cloud consumer initially models the IaC code as an high-level graph of services, leveraging the combination of TML and context-based matching adoption. The output of this workflow is the instantation of such optimized and fully-featured multi-cloud on most suitable CSPs. We implemented Mantus, a multi-cloud compiler, which encapsulates this workflow and we benchmarked this implementation according to different perspectives as scalability and performance.

Guaranteeing Quality of Service (QoS) in an (edge-)cloud environment is one of the biggest open problems in the field of cloud computing. Currently, deploy- ment of cloud applications is managed by cloud orchestration systems, such as Kubernetes. These systems make deployment of applications in cloud easier than ever, offering their users the benefits of availability, scalability and resilience. However, at the moment they are not capable of optimizing the deployment of cloud applications with respect to performance QoS metrics, such as response time and throughput. The thesis proposes an approach that provides probabilistic guarantees on the performance QoS metrics in an (edge-)cloud environment. The approach is based on assessing the performance of cloud applications and subsequently controlling their deployment in a way that the applications are deployed only in the environ- ments in which their performance does not violate their QoS requirements. The thesis also presents a proof-of-concept implementation of that approach. The im- plementation verifies the effectiveness of the approach and will serve for further research.

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