Cloud services have grown very quickly over the past couple of years, giving consumers and companies the chance to put services, resources and infrastructures in the hands of a provider. Therefore removing the need of providing these services themselves. This can for example lead to cost savings, better resource utilization and removing the need of technical expertise for the customers. There is big security concerns when using cloud services. Security is very important in cloud computing since people and companies store confidential data in the cloud. It must also be easy to use the services provided, since cloud services have so many users with different technical background. Since the control of services and data needed for the everyday-run of a corporation is being handled by another company, further issues needs to be concerned. The consumer needs to trust the provider, and know that they handle their data in a correct manner, and that resources can be accessed when needed. This thesis focuses on authentication and transmission encryption in cloud services. The current solutions used today to login to cloud services have been investigated and concluded that they don't satisfy the needs for cloud services. They are either insecure, complex or costly. It can also be concluded that the best encryption algorithm to use in a cloud environment is RC4, which is secure and at the same time a fast algorithm. Compared to AES, which together with RC4, are the most common encryption methods used over the Internet today, RC4 is the better choice. This thesis have resulted in an authentication and registration method that is both secure and easy to use, therefore fulfilling the needs of cloud service authentication. The method have been implemented in a fully working finished solution, that use a regular mobile phone to generate one time passwords that is used to login to cloud services. All of the data transmissions between the client and the server have been configured to use RC4 encryption. The conclusions that can be drawn is that the security proposal implemented in this thesis work functions very well, and provide good security together with an ease of use for clients that don't have so much technical knowledge.

Au cours de la dernière décennie les technologies de cloud computing ont connu un essor considérable se traduisant par la montée en flèche de la consommation électrique des data center. L’ampleur du problème a motivé de nombreux travaux de recherche s’articulant autour de solutions de réduction statique ou dynamique de l’enveloppe globale de consommation électrique d’un data center. L’objectif de cette thèse est d’intégrer les sources d’énergie renouvelables dans les modèles d’optimisation dynamique d’énergie dans un data center. Pour cela nous utilisons la programmation par contraintes ainsi que des techniques d’apprentissage machine. Dans un premier temps, nous proposons une contrainte globale d’intersection de tâches tenant compte d’une ressource à coûts variables. Dans un second temps, nous proposons deux modèles d’apprentissage pour la prédiction de la charge de travail d’un data center et pour la génération de telles courbes. Enfin, nous formalisons le problème de planification énergiquement écologique (PPEE) et proposons un modèle global à base de PPC ainsi qu’une heuristique de recherche pour le résoudre efficacement. Le modèle proposé intègre les différents aspects inhérents au problème de planification dynamique dans un data center : machines physiques hétérogènes, types d’applications variés (i.e., applications interactives et applications par lots), opérations et coûts énergétiques de mise en route et d’extinction des machines physiques, interruption/reprise des applications par lots, consommation des ressources CPU et RAM des applications, migration des tâches et coûts énergétiques relatifs aux migrations, prédiction de la disponibilité d’énergie verte, consommation énergétique variable des machines physiques. / Over the last decade, cloud computing technologies have considerably grown, this translates into a surge in data center power consumption. The magnitude of the problem has motivated numerous research studies around static or dynamic solutions to reduce the overall energy consumption of a data center. The aim of this thesis is to integrate renewable energy sources into dynamic energy optimization models in a data center. For this we use constraint programming as well as machine learning techniques. First, we propose a global constraint for tasks intersection that takes into account a ressource with variable cost. Second, we propose two learning models for the prediction of the work load of a data center and for the generation of such curves. Finally, we formalize the green energy aware scheduling problem (GEASP) and propose a global model based on constraint programming as well as a search heuristic to solve it efficiently. The proposed model integrates the various aspects inherent to the dynamic planning problem in a data center : heterogeneous physical machines, various application types (i.e., ractive applications and batch applications), actions and energetic costs of turning ON/OFF physical machine, interrupting/resuming batch applications, CPU and RAM ressource consumption of applications, migration of tasks and energy costs related to the migrations, prediction of green energy availability, variable energy consumption of physical machines.

The connected car segment has high demands on the exchange of data between the car on the road, and a variety of services in the backend. By the end of 2020, connected services will be mainstream automotive offerings, according to Telefónica - Connected Car Industry Report 2014 the overall number of vehicles with built-in internet connectivity will increase from 10% of the overall market today to 90% by the end of the decade [1]. Connected car solutions will soon become one of the major business drivers for the industry; they already have a significant impact on existing solutions development and aftersales market. It has been more than three decades since the introduction of the first software component in cars, and since then a vast amount of different services has been introduced, creating an ecosystem of complex applications, architectures, and platforms. The complexity of the connected car ecosystem results into a range of new challenges. The backend applications must be scalable and flexible enough to accommodate loads created by the random user and device behavior. To deliver superior uptime, back-end systems must be highly integrated and automated to guarantee lowest possible failure rate, high availability, and fastest time-to-market. Connected car services increasingly rely on cloud-based service delivery models for improving user experiences and enhancing features for millions of vehicles and their users on a daily basis. Nowadays, the software applications become more complex, and the number of components that are involved and interact with each other is extremely large. In such systems, if a fault occurs, it can easily propagate and can affect other components resulting in a complex problem which is difficult to detect and debugg, therefore a robust and resilient architecture is needed which ensures the continuous availability of system in the wake of component failures, making the overall system highly available. The goal of the thesis is to gain insight into the development of highly available applications and to explore the area of fault tolerance. This thesis outlines different design patterns and describes the capabilities of fault tolerance libraries for Java platform, and design the most appropriate solution for developing a highly available application and evaluate the behavior with stress and load testing using Chaos Monkey methodologies.

Un système de surveillance environnementale collecte et analyse continuellement les flux de données générés par les capteurs environnementaux. L'objectif du processus de surveillance est de filtrer les informations utiles et fiables et d'inférer de nouvelles connaissances qui aident l'exploitant à prendre rapidement les bonnes décisions. L'ensemble de ce processus, de la collecte à l'analyse des données, soulève deux problèmes majeurs : le volume de données et la qualité des données. D'une part, le débit des flux de données générés n'a pas cessé d'augmenter sur les dernières années, engendrant un volume important de données continuellement envoyées au système de surveillance. Le taux d'arrivée des données est très élevé par rapport aux capacités de traitement et de stockage disponibles du système de surveillance. Ainsi, un stockage permanent et exhaustif des données est très coûteux, voire parfois impossible. D'autre part, dans un monde réel tel que les environnements des capteurs, les données sont souvent de mauvaise qualité, elles contiennent des valeurs bruitées, erronées et manquantes, ce qui peut conduire à des résultats défectueux et erronés. Dans cette thèse, nous proposons une solution appelée filtrage natif, pour traiter les problèmes de qualité et de volume de données. Dès la réception des données des flux, la qualité des données sera évaluée et améliorée en temps réel en se basant sur un modèle de gestion de la qualité des données que nous proposons également dans cette thèse. Une fois qualifiées, les données seront résumées en utilisant des algorithmes d'échantillonnage. En particulier, nous nous sommes intéressés à l'analyse de l'algorithme Chain-sample que nous comparons à d'autres algorithmes de référence comme l'échantillonnage probabiliste, l'échantillonnage déterministe et l'échantillonnage pondéré. Nous proposons aussi deux nouvelles versions de l'algorithme Chain-sample améliorant sensiblement son temps d'exécution. L'analyse des données du flux est également abordée dans cette thèse. Nous nous intéressons particulièrement à la détection des anomalies. Deux algorithmes sont étudiés : Moran scatterplot pour la détection des anomalies spatiales et CUSUM pour la détection des anomalies temporelles. Nous avons conçu une méthode améliorant l'estimation de l'instant de début et de fin de l'anomalie détectée dans CUSUM. Nos travaux ont été validés par des simulations et aussi par des expérimentations sur deux jeux de données réels et différents : Les données issues des capteurs dans le réseau de distribution de l'eau potable fournies dans le cadre du projet Waves et les données relatives au système de vélo en libre-service (Velib). / An environmental monitoring system continuously collects and analyzes the data streams generated by environmental sensors. The goal of the monitoring process is to filter out useful and reliable information and to infer new knowledge that helps the network operator to make quickly the right decisions. This whole process, from the data collection to the data analysis, will lead to two keys problems: data volume and data quality. On the one hand, the throughput of the data streams generated has not stopped increasing over the last years, generating a large volume of data continuously sent to the monitoring system. The data arrival rate is very high compared to the available processing and storage capacities of the monitoring system. Thus, permanent and exhaustive storage of data is very expensive, sometimes impossible. On the other hand, in a real world such as sensor environments, the data are often dirty, they contain noisy, erroneous and missing values, which can lead to faulty and defective results. In this thesis, we propose a solution called native filtering, to deal with the problems of quality and data volume. Upon receipt of the data streams, the quality of the data will be evaluated and improved in real-time based on a data quality management model that we also propose in this thesis. Once qualified, the data will be summarized using sampling algorithms. In particular, we focus on the analysis of the Chain-sample algorithm that we compare against other reference algorithms such as probabilistic sampling, deterministic sampling, and weighted sampling. We also propose two new versions of the Chain-sample algorithm that significantly improve its execution time. Data streams analysis is also discussed in this thesis. We are particularly interested in anomaly detection. Two algorithms are studied: Moran scatterplot for the detection of spatial anomalies and CUSUM for the detection of temporal anomalies. We have designed a method that improves the estimation of the start time and end time of the anomaly detected in CUSUM. Our work was validated by simulations and also by experimentation on two real and different data sets: The data issued from sensors in the water distribution network provided as part of the Waves project and the data relative to the bike sharing system (Velib).

Motivé par le besoin de la « Conception par Réutilisation », les modèles de processus configurables ont été proposés pour représenter de manière générique des modèles de processus similaires. Ils doivent être configurés en fonction des besoins d’une organisation en sélectionnant des options. Comme les modèles de processus configurables peuvent être larges et complexes, leur configuration sans assistance est sans doute une tâche difficile, longue et source d'erreurs.De plus, les organisations adoptent de plus en plus des environnements Cloud pour déployer et exécuter leurs processus afin de bénéficier de ressources dynamiques à la demande. Néanmoins, en l'absence d'une description explicite et formelle de la perspective de ressources dans les processus métier existants, la correction de la gestion des ressources du Cloud ne peut pas être vérifiée.Dans cette thèse, nous visons à (i) fournir de l’assistance et de l’aide à la configuration aux analystes avec des options correctes, et (ii) améliorer le support de la spécification et de la vérification des ressources Cloud dans les processus métier. Pour ce faire, nous proposons une approche formelle pour aider à la configuration étape par étape en considérant des contraintes structurelles et métier. Nous proposons ensuite une approche comportementale pour la vérification de la configuration tout en réduisant le problème bien connu de l'explosion d'espace d'état. Ce travail permet d'extraire les options de configuration sans blocage d’un seul coup. Enfin, nous proposons une spécification formelle pour le comportement d'allocation des ressources Cloud dans les modèles de processus métier. Cette spécification est utilisée pour valider et vérifier la cohérence de l'allocation des ressources Cloud en fonction des besoins des utilisateurs et des capacités des ressources / Motivated by the need for the “Design by Reuse”, Configurable process models are proposed to represent in a generic manner similar process models. They need to be configured according to an organization needs by selecting design options. As the configurable process models may be large and complex, their configuration with no assistance is undoubtedly a difficult, time-consuming and error-prone task.Moreover, organizations are increasingly adopting cloud environments for deploying and executing their processes to benefit from dynamically scalable resources on demand. Nevertheless, due to the lack of an explicit and formal description of the resource perspective in the existing business processes, the correctness of Cloud resources management cannot be verified.In this thesis, we target to (i) provide guidance and assistance to the analysts in process model configuration with correct options, and to (ii) improve the support of Cloud resource specification and verification in business processes. To do so, we propose a formal approach for assisting the configuration step-by-step with respect to structural and business domain constraints. We thereafter propose a behavioral approach for configuration verification while reducing the well-known state space explosion problem. This work allows to extract configuration choices that satisfy the deadlock-freeness property at one time. Finally, we propose a formal specification for Cloud resource allocation behavior in business process models. This specification is used to formally validate and check the consistency of the Cloud resource allocation in process models according to user requirements and resource capabilities

Cloud computing emerged as an alternative to traditional in-house data centers that businesses can leverage to increase the operation agility and employees' productivity. IT solution architects are tasked with presenting to IT managers some analysis reflecting cloud computing adoption critical barriers and challenges. This quantitative correlational study established an enhanced technology acceptance model (TAM) with four external variables: perceived security (PeS), perceived privacy (PeP), perceived connectedness (PeN), and perceived complexity (PeC) as antecedents of perceived usefulness (PU) and perceived ease of use (PEoU) in a cloud computing context. Data collected from 125 participants, who responded to the invitation through an online survey focusing on Afghanistan's main cities Kabul, Mazar, and Herat. The analysis showed that PEoU was a predictor of the behavioral intention of cloud computing adoption, which is consistent with the TAM; PEoU with an R2 = .15 had a stronger influence than PU with an R2 = .023 on cloud computing behavior intention of adoption and use. PeN, PeS, and PeP significantly influenced the behavioral intentions of IT architects to adopt and use the technology. This study showed that PeC was not a significant barrier to cloud computing adoption in Afghanistan. By adopting cloud services, employees can have access to various tools that can help increase business productivity and contribute to improving the work environment. Cloud services, as an alternative solution to home data centers, can help businesses reduce power consumption and consecutively decrease in carbon dioxide emissions due to less power demand.

In a cloud storage environment, providing geo-location assurance of data to a cloud user is very challenging as the cloud storage provider physically controls the data and it would be challenging for the user to detect if the data is stored in different datacenters/storage servers other than the one where it is supposed to be. We name this problem as the “Data Location Assurance Problem” of a Cloud Storage Environment. Aside from the privacy and security concerns, the lack of geo-location assurance of cloud data involved in the cloud storage has been identified as one of the main reasons why organizations that deal with sensitive data (e.g., financial data, health-related data, and data related to Personally Identifiable Infor-mation, PII) cannot adopt a cloud storage solution even if they might wish to. It might seem that cryptographic techniques such as Proof of Data Possession (PDP) can be a solution for this problem; however, we show that those cryptographic techniques alone cannot solve that. In this thesis, we address the data location assurance (DLA) problem of the cloud storage environment which includes but is not limited to investigating the necessity for a good data location assurance solution as well as challenges involved in providing this kind of solution; we then come up with efficient solutions for the DLA problem. Note that, for the totally dis-honest cloud storage server attack model, it may be impossible to offer a solution for the DLA problem. So the main objective of this thesis is to come up with solutions for the DLA problem for different system and attack models (from less adversarial system and attack models to more adversarial ones) available in existing cloud storage environments so that it can meet the need for cloud storage applications that exist today.

Cloud computing has become increasingly popular over recent years, allowing computing resources to be scaled on-demand. Cloud Native applications are specifically created to run on the cloud service model. Currently, there is a research gap regarding the design and implementation of cloud native applications, especially regarding how design decisions affect metrics such as execution time and scalability of systems. The problem investigated in this thesis is whether the execution time and quality scalability, ηt of cloud native solutions are affected when housing the functionality of multiple use cases within the same cloud native application. In this work, a cloud native application for trading data analysis is presented, where the functionality of 3 use cases are implemented to the application: (1) creating reports of trade prices, (2) anomaly detection, and (3) analysis of relation diagram of trades. The execution time and scalability of the application are evaluated and compared to readily available solutions, which serve as a baseline for the evaluation. The results of use cases 1 and 2 are compared to Amazon Athena, while use case 3 is compared to Amazon Neptune. The results suggest that having functionalities combined into the same application could improve both execution time and scalability of the system. The impact depends on the use case and hardware configuration. When executing the use cases in a sequence, the mean execution time of the implemented system was decreased up to 17.2% while the quality scalability score was improved by 10.3% for use case 2. The implemented application had significantly lower execution time than Amazon Neptune but did not surpass Amazon Athena for respective use cases. The scalability of the systems varied depending on the use case. While not surpassing the baseline in all use cases, the results show that the execution time of a cloud native system could be improved by having functionality of multiple use cases within one system. However, the potential performance gains differ depending on the use case and might be smaller than the performance gains of choosing another solution. / Cloud computing har de senaste åren blivit alltmer populärt och möjliggör att skala beräkningskapacitet och resurser på begäran. Cloud native-applikationer är specifikt skapade för att köras på distribuerad infrastruktur. För närvarande finns det luckor i forskningen gällande design och implementering av cloud native applikationer, särskilt angående hur designbeslut påverkar mätbara värden som exekveringstid och skalbarhet. Problemet som undersöks i denna uppsats är huruvida exekveringstiden och måttet av kvalitetsskalbarhet, ηt påverkas när funktionaliteten av flera användningsfall intregreras i samma cloud native applikation. I det här arbetet skapades en cloud native applikation som kombinerar flera användningsfall för att analysera transaktionsbaserad börshandelsdata. Funktionaliteten av 3 användningsfall implementeras i applikationen: (1) generera rapporter över handelspriser, (2) detektering av avvikelser och (3) analys av relations-grafer. Applikationens exekveringstid och skalbarhet utvärderas och jämförs med kommersiella cloudtjänster, vilka fungerar som en baslinje för utvärderingen. Resultaten från användningsfall 1 och 2 jämförs med Amazon Athena, medan användningsfall 3 jämförs med Amazon Neptune. Resultaten antyder att systemets exekveringstid och skalbarhet kan förbättras genom att funktionalitet för flera användningsfall implementeras i samma system. Effekten varierar beroende på användningsfall och hårdvarukonfiguration. När samtliga användningsfall körs i en sekvens, minskar den genomsnittliga körtiden för den implementerade applikationen med upp till 17,2% medan kvalitetsskalbarheten ηt förbättrades med 10,3%för användningsfall 2. Den implementerade applikationen har betydligt kortare exekveringstid än Amazon Neptune men överträffar inte Amazon Athena för respektive användningsfall. Systemens skalbarhet varierade beroende på användningsfall. Även om det inte överträffar baslinjen i alla användningsfall, visar resultaten att exekveringstiden för en cloud native applikation kan förbättras genom att kombinera funktionaliteten hos flera användningsfall inom ett system. De potentiella prestandavinsterna varierar dock beroende på användningsfallet och kan vara mindre än vinsterna av att välja en annan lösning.

MCC (Mobile Cloud Computing) est un candidat très fort pour le NGN (Next Generation Network) qui permet aux utilisateurs mobiles d’avoir une mobilité étendue, une continuité de service et des performances supérieures. Les utilisateurs peuvent s’attendre à exécuter leurs travaux plus rapidement, avec une faible consommation de batterie et à des prix abordables ; mais ce n’est pas toujours le cas. Diverses applications mobiles ont été développées pour tirer parti de cette nouvelle technologie, mais chacune de ces applications possède ses propres exigences. Plusieurs MCA (Mobile Cloud Architectures) ont été proposées, mais aucune n'a été adaptée pour toutes les applications mobiles, ce qui a mené à une faible satisfaction du client. De plus, l'absence d'un modèle d'affaires (business model) valide pour motiver les investisseurs a empêché son déploiement à l'échelle de production. Cette thèse propose une nouvelle architecture de MCA (Mobile Cloud Architecture) qui positionne l'opérateur de téléphonie mobile au cœur de cette technologie avec un modèle d'affaires de recettes. Cette architecture, nommée OCMCA (Operator Centric Mobile Cloud Architecture), relie l'utilisateur d’un côté et le fournisseur de services Cloud (CSP) de l'autre côté, et héberge un cloud dans son réseau. La connexion OCMCA / utilisateur peut utiliser les canaux multiplex menant à un service beaucoup moins cher pour les utilisateurs, mais avec plus de revenus, et de réduire les embouteillages et les taux de rejet pour l'opérateur. La connexion OCMCA / CSP est basée sur la fédération, ainsi un utilisateur qui a été enregistré avec n’importe quel CSP, peut demander que son environnement soit déchargé de cloud hébergé par l'opérateur de téléphonie mobile afin de recevoir tous les services et les avantages de OCMCA.Les contributions de cette thèse sont multiples. Premièrement, nous proposons OCMCA et nous prouvons qu'il a un rendement supérieur à toutes les autres MCA (Mobile Cloud Architectures). Le modèle d'affaires (business model) de cette architecture se concentre sur la liberté de l'abonnement de l'utilisateur, l'utilisateur peut ainsi être abonné à un fournisseur de cloud et être toujours en mesure de se connecter via cette architecture à son environnement à l'aide du déchargement et de la fédération... / Mobile cloud computing is a very strong candidate for the title "Next Generation Network" which empowers mobile users with extended mobility, service continuity and superior performance. Users can expect to execute their jobs faster, with lower battery consumption and affordable prices; however this is not always the case. Various mobile applications have been developed to take advantage of this new technology, but each application has its own requirements. Several mobile cloud architectures have been proposed but none was suitable for all mobile applications which resulted in lower customer satisfaction. In addition to that, the absence of a valid business model to motivate investors hindered its deployment on production scale. This dissertation proposes a new mobile cloud architecture which positions the mobile operator at the core of this technology equipped with a revenue-making business model. This architecture, named OCMCA (Operator Centric Mobile Cloud Architecture), connects the user from one side and the Cloud Service Provider (CSP) from the other and hosts a cloud within its network. The OCMCA/user connection can utilize multicast channels leading to a much cheaper service for the users and more revenues, lower congestion and rejection rates for the operator. The OCMCA/CSP connection is based on federation, thus a user who has been registered with any CSP, can request her environment to be offloaded to the mobile operator's hosted cloud in order to receive all OCMCA's services and benefits...

La croissance des appareils connectés, principalement due au grand nombre de déploiements de l'internet des objets et à l'émergence des services de cloud mobile, introduit de nouveaux défis pour la conception d'architectures de services dans le Cloud Computing Mobile (CCM) du cloud computing mobile. Nous montrons dans cette thèse comment l'architecture orientée services SOA peut être une solution clé pour fournir des services cloud mobiles distribués et comment la plate-forme OSGi peut être un cadre adaptatif et efficace pour fournir une telle implémentation. Nous adaptons le cadre CCM proposé à différents contextes d'architecture. Le premier est un modèle centré traditionnel, où les appareils mobiles sont réduits à consommer des services. Le second est un modèle distribué où la puissance de l'interaction de mobile à mobile offre des opportunités illimitées de services de valeur, et enfin, l'architecture à trois niveaux est considérée avec l'introduction de la notion de cloudlet. Pour chaque contexte, nous explorons la performance de notre cadre axé sur le service et le comparons à d'autres solutions existantes. / The growth of connected devices, mostly due to the large number of internet of things IoT deployments and the emergence of mobile cloud services, introduces new challenges for the design of service architectures in mobile cloud computing MCC. An MCC framework should provide elasticity and scalability in a distributed and dynamic way while dealing with limited environment resources and variable mobile contexts web applications, real-time, enterprise services, mobile to mobile, hostile environment, etc. that may include additional constraints impacting the design foundation of cloud services. We show in this thesis how service-oriented architecture SOA can be a key solution to provide distributed mobile cloud services and how OSGi platform can be an adaptive and efficient framework to provide such implementation. We adapt the proposed MCC framework to different architecture contexts. The first one is a traditional centric model, where mobile devices are reduced to consuming services. The second one is a distributed model where the power of mobile-to-mobile interaction offers unlimited value-services opportunities, and finally, three-tier architecture is considered with the introduction of the cloudlet notion. For each context, we explore the performance of our service-oriented framework, and contrast it with alternative existing solutions.