Emotron has a frequency inverter on the market that different industries uses in their factories. In case of errors they need to send out service to the factories in order to examine the inverter and find the error. They now want a solution that makes it possible for them to give support without leaving the office by connecting their devices to the cloud which eliminates the need to send out staff to industries. Emotron gave this task to HMS and has been possible with their product Anybus wiress bolt. By connecting the Anybus wireless bolt to the inverter it was possible to communicate with the cloud, MicrosoftAzure, where a static webb application is hosted. The application is made to look like the terminal on the inverter and has similiar structures and functionality. Through the application users can communicate withthe inverter by means of controlling the connected motor, reading registers and also write to certain registers. These registers contain different measurement and option parameters. The purpose of this thesis was to create a Proof-of-Concept solution using the Anybus wireless bolt. The thesis has shown of industries can use Anybus wireless bolt and the tag engine to make it possible to create a link between machines and the ever- growing cloud and is also the first part of a bigger project.

Le Cloud Computing est un nouveau paradigme qui fournit des ressources informatiques sous forme de services à la demande via internet fondé sur le modèle de facturation pay-per-use. Il est de plus en plus utilisé pour le déploiement et l’exécution des processus métier en général et des processus métier à base de services (SBPs) en particulier. Les environnements cloud sont généralement très dynamiques. À cet effet, il devient indispensable de s’appuyer sur des agents intelligents appelés gestionnaires autonomiques (AMs), qui permettent de rendre les SBPs capables de se gérer de façon autonome afin de faire face aux changements dynamiques induits parle cloud. Cependant, les solutions existantes sont limitées à l’utilisation soit d’un AM centralisé, soit d’un AM par service pour gérer un SBP. Il est évident que la deuxième solution représente un gaspillage d’AMs et peut conduire à la prise de décisions de gestion contradictoires, tandis que la première solution peut conduire à des goulots d’étranglement au niveau de la gestion du SBP. Par conséquent, il est essentiel de trouver le nombre optimal d’AMs qui seront utilisés pour gérer un SBP afin de minimiser leur nombre tout en évitant les goulots d’étranglement. De plus, en raison de l’hétérogénéité des ressources cloud et de la diversité de la qualité de service (QoS) requise par les SBPs, l’allocation des ressources cloud pour ces AMs peut entraîner des coûts de calcul et de communication élevés et/ou une QoS inférieure à celle exigée. Pour cela, il est également essentiel de trouver l’allocation optimale des ressources cloud pour les AMs qui seront utilisés pour gérer un SBP afin de minimiser les coûts tout en maintenant les exigences de QoS. Dans ce travail, nous proposons un modèle d’optimisation déterministe pour chacun de ces deux problèmes. En outre, en raison du temps nécessaire pour résoudre ces problèmes qui croît de manière exponentielle avec la taille du problème, nous proposons des algorithmes quasi-optimaux qui permettent d’obtenir de bonnes solutions dans un temps raisonnable / Cloud Computing is a new paradigm that provides computing resources as a service over the internet in a pay-per-use model. It is increasingly used for hosting and executing business processes in general and service-based business processes (SBPs) in particular. Cloud environments are usually highly dynamic. Hence, executing these SBPs requires autonomic management to cope with the changes of cloud environments implies the usage of a number of controlling devices, referred to as Autonomic Managers (AMs). However, existing solutions are limited to use either a centralized AM or an AM per service for managing a whole SBP. It is obvious that the latter solution is resource consuming and may lead to conflicting management decisions, while the former one may lead to management bottlenecks. An important problem in this context, deals with finding the optimal number of AMs for the management of an SBP, minimizing costs in terms of number of AMs while at the same time avoiding management bottlenecks and ensuring good management performance. Moreover, due to the heterogeneity of cloud resources and the diversity of the required quality of service (QoS) of SBPs, the allocation of cloud resources to these AMs may result in high computing costs and an increase in the communication overheads and/or lower QoS. It is also crucial to find an optimal allocation of cloud resources to the AMs, minimizing costs while at the same time maintaining the QoS requirements. To address these challenges, in this work, we propose a deterministic optimization model for each problem. Furthermore, due to the amount of time needed to solve these problems that grows exponentially with the size of the problem, we propose near-optimal algorithms that provide good solutions in reasonable time

Le Cloud Computing (CC) ouvre de nouvelles possibilités pour des services plus flexibles et efficaces pour les clients de services en nuage (CSC). Cependant, la migration vers le cloud suscite aussi une série de problèmes, notamment le fait que, ce qui autrefois était un domaine privé pour les CSC, est désormais géré par un tiers, et donc soumis à ses politiques de sécurité. Par conséquent, la disponibilité, la confidentialité et l'intégrité des CSC doivent être assurées. Malgré l'existence de mécanismes de protection, tels que le cryptage, la surveillance de ces propriétés devient nécessaire. De plus, de nouvelles menaces apparaissent chaque jour, ce qui exige de nouvelles techniques de détection plus efficaces.Les travaux présentés dans ce document vont au-delà du simple l’état de l'art, en traitant la menace interne malveillante, une des menaces les moins étudiées du CC. Ceci s'explique principalement par les obstacles organisationnels et juridiques de l'industrie, et donc au manque de jeux de données appropriés pour la détecter. Nous abordons cette question en présentant deux contributions principales.Premièrement, nous proposons la dérivation d’une méthodologie extensible pour modéliser le comportement d’un utilisateur dans une entreprise. Cette abstraction d'un employé inclut des facteurs intra-psychologiques ainsi que des informations contextuelles, et s'inspire d'une approche basée sur les rôles. Les comportements suivent une procédure probabiliste, où les motivations malveillantes devraient se produire selon une probabilité donnée dans la durée.La contribution principale de ce travail consiste à concevoir et à mettre en œuvre un cadre de détection basé sur les anomalies pour la menace susmentionnée. Cette implémentation s’enrichit en comparant deux points différents de capture de données : une vue basée sur le profil du réseau local de la entreprise, et une point de vue du cloud qui analyse les données des services avec lesquels les clients interagissent. Cela permet au processus d'apprentissage des anomalies de bénéficier de deux perspectives: (1) l'étude du trafic réel et du trafic simulé en ce qui concerne l'interaction du service de cloud computing, de manière de caractériser les anomalies; et (2) l'analyse du service cloud afin d'ajouter des statistiques prenant en compte la caractérisation globale du comportement.La conception de ce cadre a permis de détecter de manière empirique un ensemble plus large d’anomalies de l’interaction d'une entreprise donnée avec le cloud. Cela est possible en raison de la nature reproductible et extensible du modèle. En outre, le modèle de détection proposé profite d'une technique d'apprentissage automatique en mode cluster, en suivant un algorithme adaptatif non supervisé capable de caractériser les comportements en évolution des utilisateurs envers les actifs du cloud. La solution s'attaque efficacement à la détection des anomalies en affichant des niveaux élevés de performances de clustering, tout en conservant un FPR (Low Positive Rate) faible, garantissant ainsi les performances de détection pour les scénarios de menace lorsque celle-ci provient de la entreprise elle-même / Cloud Computing (CC) opens new possibilities for more flexible and efficient services for Cloud Service Clients (CSCs). However, one of the main issues while migrating to the cloud is that what once was a private domain for CSCs, now is handled by a third-party, hence subject to their security policies. Therefore, CSCs' confidentiality, integrity, and availability (CIA) should be ensured. In spite of the existence of protection mechanisms, such as encryption, the monitoring of the CIA properties becomes necessary. Additionally, new threats emerge every day, requiring more efficient detection techniques. The work presented in this document goes beyond the state of the art by treating the malicious insider threat, one of the least studied threats in CC. This is mainly due to the organizational and legal barriers from the industry, and therefore the lack of appropriate datasets for detecting it. We tackle this matter by addressing two challenges.First, the derivation of an extensible methodology for modeling the behavior of a user in a company. This abstraction of an employee includes intra psychological factors, contextual information and is based on a role-based approach. The behaviors follow a probabilistic procedure, where the malevolent motivations are considered to occur with a given probability in time.The main contribution, a design and implementation of an anomaly-based detection framework for the aforementioned threat. This implementation enriches itself by comparing two different observation points: a profile-based view from the local network of the company, and a cloud-end view that analyses data from the services with whom the clients interact. This allows the learning process of anomalies to benefit from two perspectives: (1) the study of both real and simulated traffic with respect to the cloud service's interaction, in favor of the characterization of anomalies; and (2) the analysis of the cloud service in order to aggregate data statistics that support the overall behavior characterization.The design of this framework empirically shows to detect a broader set of anomalies of the company's interaction with the cloud. This is possible due to the replicable and extensible nature of the mentioned insider model. Also, the proposed detection model takes advantage of the autonomic nature of a clustering machine learning technique, following an unsupervised, adaptive algorithm capable of characterizing the evolving behaviors of the users towards cloud assets. The solution efficiently tackles the detection of anomalies by showing high levels of clustering performance, while keeping a low False Positive Rate (FPR), ensuring the detection performance for threat scenarios where the threat comes from inside the enterprise

This diploma thesis deals with the processing of point clouds captured by the Kinect sensor from single position. As part of this thesis an application was designed, which is able to register and reconstruct surface using selected methods. The registration of overlapping frames is based on computation of key points and their FPFH histograms from which the estimation of correspondence is computed. This estimation is then refined and redundant point filtering is performed. Surface is reconstructed from the registered and modified point cloud using Greedy Projection Triangulation. All computations are performed offline. The output of this application is textured polygonial model and an image for texture creation. With assumption of correctly set parameters the results are in a good quality for creation of virtual tours and visualization.

Graph convolutional networks (GCNs) showed promising results in learning from point cloud data. Applications of GCNs include point cloud classification, point cloud segmentation, point cloud upsampling, and more. Recently, the introduction of Deep Graph Convolutional Networks (DeepGCNs) allowed GCNs to go deeper, and thus resulted in better graph learning while avoiding the vanishing gradient problem in GCNs. By adapting impactful methods from convolutional neural networks (CNNs) such as residual connections, dense connections, and dilated convolutions, DeepGCNs allowed GCNs to learn better from non-Euclidean data. In addition, deep learning methods proved very effective in the task of point cloud upsampling. Unlike traditional optimization-based methods, deep learning-based methods to point cloud upsampling does not rely on priors nor hand-crafted features to learn how to upsample point clouds. In this thesis, I discuss the impact and show the performance results of DeepGCNs in the task of point cloud part segmentation on PartNet dataset. I also illustrate the significance of using GCNs as upsampling modules in the task of point cloud upsampling by introducing two novel upsampling modules: Multi-branch GCN and Clone GCN. I show quantitatively and qualitatively the performance results of our novel and versatile upsampling modules when evaluated on a new proposed standardized dataset: PU600, which is the largest and most diverse point cloud upsampling dataset currently in the literature.

Cloud VR/AR/MR (Virtual Reality, Augmented Reality, and Mixed Reality) services representa high-level architecture that combines large scale computer resources in a data-center structurestyle set up to render VR/AR/MR services using a combination of very high bandwidth, ultralow latency, high throughput, latest 5G (5th Generation) mobile networks to the end users.  VR refers to a three-dimensional computer-generated virtual environment made up ofcomputers, which can be explored by people for real time interaction. AR amplifies humanperception of the real world through overlapping of computer-generated graphics or interactivedata on a real-world image for enhanced experience.  According to the Virtual Reality Society’s account of the history of VR, it started from the360-degree murals from the nineteenth century [18]. Historically, live application of AR wasdisplayed when Myron Kruger used a combination of video cameras and projector in aninteractive environment in 1974. In 1998, AR was put into live display with the casting of avirtual yellow line marker during an NFL game. However, personal, and commercial use ofVR/AR was made possible starting with release of a DIY (Do it Yourself) headset calledGoogle Cardboard in 2014 by Google, which made use of a smartphone for the VR experience.In 2014, Samsung also introduced Gear VR which officially started the competition for VRdevices. Subsequently In 2014, Facebook acquired Oculus VR with the major aim ofdominating the high-end spectrum of VR headset [18]. Furthermore, wider adoption of ARbecame enhanced with the introduction of Apple’s ARKit (Augmented Reality Kit) whichserves as a development framework for AR applications for iPhones and iPads [18].  The first application of VR devices in the health industry was made possible due to healthworkers’ need to visualize complex medical data during surgery and planning of surgery in1994. Since then, commercial production of VR devices and availability of advanced networkand faster broadband have increased the adoption of VR services in the healthcare industryespecially in planning of surgery and during surgery itself [16]. Overall, the wide availabilityof VR/AR terminals, displays, controllers, development kits, advanced network, and robustbandwidth have contributed to making VR and AR services to be of valuable and importanttechnologies in the area of digital entertainment, information, games, health, military and soon. However, the solutions or services needed for the technology required an advancedprocessing platform which in most cases is not cost efficient in single-use scenarios.  The kind of devices, hardware, software required for the processing and presentation ofimmersive experiences is often expensive and dedicated to the current application itself.Technological improvement in realism and immersion means increase in cost of ownershipwhich often affected cost-benefit consideration, leading to slower adoption of the VR services[14] [15]. This is what has led to development of cloud VR services, a form of data-centerbased system, which serves as a means of providing VR services to end users from the cloudanywhere in the world, using its fast and stable transport networks. The content of the VR isstored in the cloud, after which the output in form of audio-visuals is coded and compressedusing suitable encoding technology, and thereafter transmitted to the terminals. The industrywide acceptance of the cloud VR services, and technology has made available access to payper-use-basis and hence access to high processing capability offered, which is used in iipresenting a more immersive, imaginative, and interactive experience to end users [11] [12].However, cloud VR services has a major challenge in form of network latency introduced fromcloud rendering down to the display terminal itself. This is most often caused by otherperformance indicators such as network bandwidth, coding technology, RTT (Return TripTime) and so on [19]. This is the major problem which this thesis is set to find out.  The research methodology used was a combination of empirical and experimental method,using quantitative approach as it entails the generation of data in quantitative form availablefor quantitative analysis. The research questions are: Research Question 1 (RQ1): What are the latency related performance indicators ofnetworked immersive media in mobile health applications? Research Question 2 (RQ2): What are the suitable network structures to achieve an efficientlow latency VR health application?  The answers gotten from the result analysis at the end of the simulation, show thatbandwidth, frame rate, and resolution are very crucial performance indicator to achieve theoptimal latency required for hitch-free cloud VR user experience, while the importance of otherindicators such as resolution and coding standard cannot be overemphasized. Combination ofedge and cloud architecture also proved to more efficient and effective for the achievement ofa low-latency cloud VR application functionality.  Conclusively, the answer to research question one was that, the latency relatedperformance indicators of networked immersive media in mobile health applications arebandwidth, frame rate, resolution, coding technology. For research question two, suitablenetwork structures includes edge network, cloud network and combination of cloud and edgenetwork, but in order to achieve an optimally low-latency network for cloud VR mobile healthapplication in education, combination of edge and cloud network architecture is recommended

Dans cette thèse, nous proposons une approche pour la sécurité programmable dans le cloud distribué. Plus spécifiquement, nous montrons de quelle façon cette programmabilité peut contribuer à la protection de services cloud distribués, à travers la génération d'images unikernels fortement contraintes. Celles-ci sont instanciées sous forme de machines virtuelles légères, dont la surface d'attaque est réduite et dont la sécurité est pilotée par un orchestrateur de sécurité. Les contributions de cette thèse sont triples. Premièrement, nous présentons une architecture logique supportant la programmabilité des mécanismes de sécurité dans un contexte multi-cloud et multi-tenant. Elle permet l'alignement et le paramétrage de ces mécanismes pour des services cloud dont les ressources sont réparties auprès de différents fournisseurs et tenants. Deuxièmement, nous introduisons une méthode de génération à la volée d'images unikernels sécurisées. Celle-ci permet d'aboutir à des ressources spécifiques et contraintes, qui intègrent les mécanismes de sécurité dès la phase de construction des images. Elles peuvent être élaborées réactivement ou proactivement pour répondre à des besoins d'élasticité. Troisièmement, nous proposons d'étendre le langage d'orchestration TOSCA, afin qu'il soit possible de générer automatiquement des ressources sécurisées, selon différents niveaux de sécurité en phase avec l'orchestration. Enfin, nous détaillons un prototypage et un ensemble d'expérimentations permettant d'évaluer les bénéfices et limites de l'approche proposée / In this thesis, we propose an approach for software-defined security in distributed clouds. More specifically, we show to what extent this programmability can contribute to the protection of distributed cloud services, through the generation of secured unikernel images. These ones are instantiated in the form of lightweight virtual machines, whose attack surface is limited and whose security is driven by a security orchestrator. The contributions of this thesis are threefold. First, we present a logical architecture supporting the programmability of security mechanims in a multi-cloud and multi-tenant context. It permits to align and parameterize these mechanisms for cloud services whose resources are spread over several providers and tenants. Second, we introduce a method for generating secured unikernel images in an on-the-fly manner. This one permits to lead to specific and constrained resources, that integrate security mechanisms as soon as the image generation phase. These ones may be built in a reactive or proactive manner, in order to address elasticity requirements. Third, we propose to extend the TOSCA orchestration language, so that is is possible to generate automatically secured resources, according to different security levels in phase with the orchestration. Finally, we detail a prototyping and extensive series of experiments that are used to evaluate the benefits and limits of the proposed approach

The purpose of this study is to investigate which factors that influence cloud adoption and contribute to existing research about cloud computing. Ten hypotheses were derived from the Technological, Organizational, and Environmental (TOE) framework combined with the Diffusion of Innovation (DOI) framework. Data was collected using a questionnaire and 91 individuals working in several industries and different countries participated in the study. The research model was tested using a quantitative approach using partial least squares structural equation modeling (PLS-SEM). The main factors that were identified as drivers for adoption of cloud computing are: digital strategy, competitive pressure, trading partner support, standardization, firm size and network and collaboration. These findings have important implications and great value to the research field, companies and cloud providers as they could formulate better strategies for a successful cloud adoption. The study also provides a new approach in research about cloud adoption where the type of enterprise system migrated to the cloud is taken into consideration. Findings in this study points towards system specific requirements influencing the adoption rate.

Die Verwaltung großer Mengen von Testfahrtdaten verschiedener Fahrzeuge stellt eine Herausforderung bei der Entwicklung autonomer Fahrassistenzsysteme dar. Um eine zentrale Anlaufstelle zu haben, auf die von überallher zugegriffen werden kann, ist die Verwendung einer Cloudplattform eine zukunftsweisende Methode. Der Aufbau einer solchen Plattform, deren Schnittstellen, sowie die Client-Programme, welche für die Verbindung der Testfahrtdatenquellen zu dieser Cloudplattform benötigt werden, werden in dieser Arbeit konzeptionell entwickelt, an konkreten Beispielen umgesetzt und diese Implementierungen ausgewertet.

Cloudspeicherdienste sind zu einem Standard für den Austausch großer Datenmengen in virtuellen Gemeinschaften geworden, sowohl im privaten Umfeld als auch im öffentlichen Bereich. Einfache Bedienbarkeit sowie nahtlose Integration in Applikationen, Betriebssystemen und Endgeräten sind wesentliche Bausteine dieses Siegeszuges.