L’analyse des paysages, de ses dynamiques et ses processus environnementaux, nécessite d’acquérir régulièrement des données des sites, notamment pour le bilan glaciaire au Spitsberg et en haute montagne. A cause des mauvaises conditions climatiques communes aux latitudes polaires et à cause de leur coût, les images satellites journalières ne sont pas toujours accessibles. De ce fait, les événements rapides comme la fonte de la neige ou l'enneigement ne peuvent pas être étudiés à partir des données de télédétection à cause de leur fréquence trop faible. Nous avons complété les images satellites par un ensemble de de stations photo automatiques et autonomes qui prennent 3 photos par jour. L’acquisition de ces photos génère une grande base de données d’images. Plusieurs traitements doivent être appliqués sur les photos afin d’extraire l’information souhaitée (modifications géométriques, gestion des perturbations atmosphériques, classification, etc). Seule l’informatique est à même de stocker et gérer toutes ces informations. Le cloud computing offre en tant que services des ressources informatiques (puissance de calcul, espace de stockage, applications, etc). Uniquement le stockage de la masse de données géographique pourrait être une raison d’utilisation du cloud computing. Mais en plus de son espace de stockage, le cloud offre une simplicité d’accès, une architecture scalable ainsi qu’une modularité dans les services disponibles. Dans le cadre de l’analyse des photos in situ, le cloud computing donne la possibilité de mettre en place un outil automatique afin de traiter l’ensemble des données malgré la variété des perturbations ainsi que le volume de données. A travers une décomposition du traitement d’images en plusieurs tâches, implémentées en tant que web services, la composition de ces services nous permet d’adapter le traitement aux conditions de chacune des données. / Analyzing landscape, its dynamics and environmental evolutions require regular data from the sites, specifically for glacier mass balanced in Spitsbergen and high mountain area. Due to poor weather conditions including common heavy cloud cover at polar latitudes, and because of its cost, daily satellite imaging is not always accessible. Besides, fast events like flood or blanket of snow is ignored by satellite based studies, since the slowest sampling rate is unable to observe it. We complement satellite imagery with a set of ground based autonomous automated digital cameras which take 3 pictures a day. These pictures form a huge database. Each picture needs many processing to extract the information (geometric modifications, atmospheric disturbances, classification, etc). Only computer science is able to store and manage all this information. Cloud computing, being more accessible in the last few years, offers as services IT resources (computing power, storage, applications, etc.). The storage of the huge geographical data could, in itself, be a reason to use cloud computing. But in addition to its storage space, cloud offers an easy way to access , a scalable architecture and a modularity in the services available. As part of the analysis of in situ images, cloud computing offers the possibility to set up an automated tool to process all the data despite the variety of disturbances and the data volume. Through decomposition of image processing in several tasks, implemented as web services, the composition of these services allows us to adapt the treatment to the conditions of each of the data.

COSTA, Philipp Bernardino. Uma abordagem para offloading em múltiplas plataformas móveis. 2014. 104 f. Dissertação (Mestrado em ciência da computação)- Universidade Federal do Ceará, Fortaleza-CE, 2014. / Submitted by Elineudson Ribeiro (elineudsonr@gmail.com) on 2016-07-12T15:14:02Z No. of bitstreams: 1 2014_dis_pbcosta.pdf: 2579064 bytes, checksum: 96c47daf2c7ccea16a7e2f6945cb1a7a (MD5) / Approved for entry into archive by Rocilda Sales (rocilda@ufc.br) on 2016-07-20T13:52:31Z (GMT) No. of bitstreams: 1 2014_dis_pbcosta.pdf: 2579064 bytes, checksum: 96c47daf2c7ccea16a7e2f6945cb1a7a (MD5) / Made available in DSpace on 2016-07-20T13:52:31Z (GMT). No. of bitstreams: 1 2014_dis_pbcosta.pdf: 2579064 bytes, checksum: 96c47daf2c7ccea16a7e2f6945cb1a7a (MD5) Previous issue date: 2014 / The mobile devices, like smartphones and tablets, have evolved considerably in last years in computational terms. Despite advances in their hardware, these devices have energy constraints regarded to their poor computing performance. Therefore, on this context, a new paradigm called Mobile Cloud Computing (MCC) has emerged. MCC studies new ways to extend the computational and energy resources, on mobile devices using the offloading techniques. A literature survey about MCC, has shown that there is no support heterogeneity on reported studies. In response, we propose a framework called MpOS (Multi-platform Offloading System), which supports the offloading technique in mobile application development, for two mobile platforms (Android and Windows Phone). Two case studies were developed with MpOS solution in order to evaluate the framework for each mobile platform. These case studies show how the offloading technique works on several perspectives. In BenchImage experiment, the offloading performance was analyzed, concerning to its execution on a remote execution site (a cloudlet on local network and public cloud in the Internet). The Collision application promotes the analysis of the offloading technique performance on real-time application, also using different serialization systems. In both experiments, results show some situations where it was better to run locally on smarphone, than performing the offloading operation and vice versa. / Os dispositivos móveis, especificamente os smartphones e os tablets, evoluíram bastante em termos computacionais nos últimos anos, e estão cada vez mais presentes no cotidiano das pessoas. Apesar dos avanços tecnológicos, a principal limitação desses dispositivos está relacionada com a questão energética e com seu baixo desempenho computacional, quando comparado com um notebook ou computador de mesa. Com base nesse contexto, surgiu o paradigma do Mobile Cloud Computing (MCC), o qual estuda formas de estender os recursos computacionais e energéticos dos dispositivos móveis através da utilização das técnicas de offloading. A partir do levantamento bibliográfico dos frameworks em MCC verificou-se, para o problema da heterogeneidade em plataformas móveis, ausência de soluções de offloading. Diante deste problema, esta dissertação apresenta um framework denominado de MpOS (Multiplataform Offloading System), que suporta a técnica de offloading, em relação ao desenvolvimento de aplicações para diferentes plataformas móveis, sendo desenvolvido inicialmente para as plataformas Android e Windows Phone. Para validação foram desenvolvidas para cada plataforma móvel, duas aplicações móveis, denominadas de BenchImage e Collision, que demonstram o funcionamento da técnica de offloading em diversos cenários. No caso do experimento realizado com BenchImage foi analisado o desempenho da aplicação móvel, em relação à execução local, no cloudlet server e em uma nuvem pública na Internet, enquanto no experimento do Collision (um aplicativo de tempo real) foi analisado o desempenho do offloading, utilizando também diferentes sistemas de serialização de dados. Em ambos os experimentos houve situações que era mais vantajoso executar localmente no smartphone, do que realizar a operação de offloading e vice-versa, por causa de diversos fatores associados com a qualidade da rede e com volume de processamento exigido nesta operação.

L'usage des plateformes de Cloud Computing offrant une Infrastructure en tant que service (IaaS) a augmenté au sein de l'industrie. Les infrastructures IaaS fournissent des ressources virtuelles depuis un catalogue de types prédéfinis. Les avancées dans le domaine de la virtualisation rendent possible la création et la destruction de machines virtuelles au fur et à mesure, avec un faible surcout d'exploitation. En conséquence, le bénéfice offert par les plate-formes IaaS est la possibilité de dimensionner une architecture virtuelle au fur et à mesure de l'utilisation, et de payer uniquement les ressources utilisées. D'un point de vue scientifique, les plateformes IaaS soulèvent de nouvelles questions concernant l'efficacité des décisions prises en terme de passage à l'échelle, et également l'ordonnancement des applications sur les plateformes dynamiques. Les travaux de cette thèse explorent ce thème et proposent des solutions à ces deux problématiques. La première contribution décrite dans cette thèse concerne la gestion des ressources. Nous avons travaillé sur le redimensionnement automatique des applications clientes de Cloud afin de modéliser les variations d'utilisation de la plateforme. De nombreuses études ont montré des autosimilarités dans le trafic web des plateformes, ce qui implique l'existence de motifs répétitifs pouvant être périodiques ou non. Nous avons développé une stratégie automatique de dimensionnement, capable de prédire le temps d'utilisation de la plateforme en identifiant les motifs répétitifs non périodiques. Dans un second temps, nous avons proposé d'étendre les fonctionnalités d'un intergiciel de grilles, en implémentant une utilisation des ressources à la demandes.Nous avons développé une extension pour l'intergiciel DIET (Distributed Interactive Engineering Toolkit), qui utilise un marché virtuel pour gérer l'allocation des ressources. Chaque utilisateur se voit attribué un montant de monnaie virtuelle qu'il utilisera pour exécuter ses tâches. Le mécanisme d'aide assure un partage équitable des ressources de la plateforme entre les différents utilisateurs. La troisième et dernière contribution vise la gestion d'applications pour les plateformes IaaS. Nous avons étudié et développé une stratégie d'allocation des ressources pour les applications de type workflow avec des contraintes budgétaires. L'abstraction des applications de type workflow est très fréquente au sein des applications scientifiques, dans des domaines variés allant de la géologie à la bioinformatique. Dans ces travaux, nous avons considéré un modèle général d'applications de type workflow qui contient des tâches parallèles et permet des transitions non déterministes. Nous avons élaboré deux stratégies d'allocations à contraintes budgétaires pour ce type d'applications. Le problème est une optimisation à deux critères dans la mesure où nous optimisons le budget et le temps total du flux d'opérations. Ces travaux ont été validés de façon expérimentale par leurs implémentations au sein de la plateforme de Cloud libre Nimbus et de moteur de workflow MADAG présent au sein de DIET. Les tests ont été effectuées sur une simulation de cosmologie appelée RAMSES. RAMSES est une application parallèle qui, dans le cadre de ces travaux, a été portée sur des plateformes virtuelles dynamiques. L'ensemble des résultats théoriques et pratiques ont débouché sur des résultats encourageants et des améliorations. / Infrastructure as a service (IaaS) Cloud platforms are increasingly used in the IT industry. IaaS platforms are providers of virtual resources from a catalogue of predefined types. Improvements in virtualization technology make it possible to create and destroy virtual machines on the fly, with a low overhead. As a result, the great benefit of IaaS platforms is the ability to scale a virtual platform on the fly, while only paying for the used resources. From a research point of view, IaaS platforms raise new questions in terms of making efficient virtual platform scaling decisions and then efficiently scheduling applications on dynamic platforms. The current thesis is a step forward towards exploring and answering these questions. The first contribution of the current work is focused on resource management. We have worked on the topic of automatically scaling cloud client applications to meet changing platform usage. There have been various studies showing self-similarities in web platform traffic which implies the existence of usage patterns that may or may not be periodical. We have developed an automatic platform scaling strategy that predicted platform usage by identifying non-periodic usage patterns and extrapolating future platform usage based on them. Next we have focused on extending an existing grid platform with on-demand resources from an IaaS platform. We have developed an extension to the DIET (Distributed Interactive Engineering Toolkit) middleware, that uses a virtual market based approach to perform resource allocation. Each user is given a sum of virtual currency that he will use for running his tasks. This mechanism help in ensuring fair platform sharing between users. The third and final contribution targets application management for IaaS platforms. We have studied and developed an allocation strategy for budget-constrained workflow applications that target IaaS Cloud platforms. The workflow abstraction is very common amongst scientific applications. It is easy to find examples in any field from bioinformatics to geology. In this work we have considered a general model of workflow applications that comprise parallel tasks and permit non-deterministic transitions. We have elaborated two budget-constrained allocation strategies for this type of workflow. The problem is a bi-criteria optimization problem as we are optimizing both budget and workflow makespan. This work has been practically validated by implementing it on top of the Nimbus open source cloud platform and the DIET MADAG workflow engine. This is being tested with a cosmological simulation workflow application called RAMSES. This is a parallel MPI application that, as part of this work, has been ported for execution on dynamic virtual platforms. Both theoretical simulations and practical experiments have shown encouraging results and improvements.

FundaÃÃo Cearense de Apoio ao Desenvolvimento Cientifico e TecnolÃgico / Os dispositivos mÃveis, especificamente os smartphones e os tablets, evoluÃram bastante em termos computacionais nos Ãltimos anos, e estÃo cada vez mais presentes no cotidiano das pessoas. Apesar dos avanÃos tecnolÃgicos, a principal limitaÃÃo desses dispositivos està relacionada com a questÃo energÃtica e com seu baixo desempenho computacional, quando comparado com um notebook ou computador de mesa. Com base nesse contexto, surgiu o paradigma do Mobile Cloud Computing (MCC), o qual estuda formas de estender os recursos computacionais e energÃticos dos dispositivos mÃveis atravÃs da utilizaÃÃo das tÃcnicas de offloading. A partir do levantamento bibliogrÃfico dos frameworks em MCC verificou-se, para o problema da heterogeneidade em plataformas mÃveis, ausÃncia de soluÃÃes de offloading. Diante deste problema, esta dissertaÃÃo apresenta um framework denominado de MpOS (Multiplataform Offloading System), que suporta a tÃcnica de offloading, em relaÃÃo ao desenvolvimento de aplicaÃÃes para diferentes plataformas mÃveis, sendo desenvolvido inicialmente para as plataformas Android e Windows Phone. Para validaÃÃo foram desenvolvidas para cada plataforma mÃvel, duas aplicaÃÃes mÃveis, denominadas de BenchImage e Collision, que demonstram o funcionamento da tÃcnica de offloading em diversos cenÃrios. No caso do experimento realizado com BenchImage foi analisado o desempenho da aplicaÃÃo mÃvel, em relaÃÃo à execuÃÃo local, no cloudlet server e em uma nuvem pÃblica na Internet, enquanto no experimento do Collision (um aplicativo de tempo real) foi analisado o desempenho do offloading, utilizando tambÃm diferentes sistemas de serializaÃÃo de dados. Em ambos os experimentos houve situaÃÃes que era mais vantajoso executar localmente no smartphone, do que realizar a operaÃÃo de offloading e vice-versa, por causa de diversos fatores associados com a qualidade da rede e com volume de processamento exigido nesta operaÃÃo. / The mobile devices, like smartphones and tablets, have evolved considerably in last years in computational terms. Despite advances in their hardware, these devices have energy constraints regarded to their poor computing performance. Therefore, on this context, a new paradigm called Mobile Cloud Computing (MCC) has emerged. MCC studies new ways to extend the computational and energy resources, on mobile devices using the offloading techniques. A literature survey about MCC, has shown that there is no support heterogeneity on reported studies. In response, we propose a framework called MpOS (Multi-platform Offloading System), which supports the offloading technique in mobile application development, for two mobile platforms (Android and Windows Phone). Two case studies were developed with MpOS solution in order to evaluate the framework for each mobile platform. These case studies show how the offloading technique works on several perspectives. In BenchImage experiment, the offloading performance was analyzed, concerning to its execution on a remote execution site (a cloudlet on local network and public cloud in the Internet). The Collision application promotes the analysis of the offloading technique performance on real-time application, also using different serialization systems. In both experiments, results show some situations where it was better to run locally on smarphone, than performing the offloading operation and vice versa.

Submitted by Irene Nascimento (irene.kessia@ufpe.br) on 2016-10-18T19:25:38Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) mestrado_thiago_jamir.pdf: 1864217 bytes, checksum: 4e432bf2e6196fc2e2fb739007114e2e (MD5) / Made available in DSpace on 2016-10-18T19:25:38Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) mestrado_thiago_jamir.pdf: 1864217 bytes, checksum: 4e432bf2e6196fc2e2fb739007114e2e (MD5) Previous issue date: 2014-04-05 / Nos últimos anos, o volume de dados gerados por indivíduos e organizações tem crescido exponencialmente. Estima-se que globalmente existia 2.7 zetabytes em 2012 e esse número tem dobrado a cada dois anos. Além disso, com a popularização de dispositivos móveis conectados, cresceu-se a necessidade de que usuários tenham acesso a arquivos de forma ubíqua. As soluções tradicionais de backup e armazenamento de arquivos online já não conseguem suprir as necessidades atuais dos usuários. A utilização de Cloud Storage para backup e sincronização de arquivos vem a ser uma ferramenta de grande valia para esse tipo de problema. Porém, implementar um sistema deste tipo vem a ser um desafio tecnológico relevante. Nesse sentido, este trabalho se propõe a resolver o problema de armazenamento de arquivos, propondo uma arquitetura de Cloud Storage para armazenamento de arquivos. Ao longo trabalho, é feita uma análise dos principais direcionadores de negócio para Cloud Storage e armazenamento de arquivos, levantando insumos para se projetar uma arquitetura. Tal arquitetura é descrita em nível de detalhe para que se possa ser implementada. Finalmente, o trabalho é validado através de uma avaliação de arquitetura cuja metodologia foi adaptada de acordo com as características da equipe de avaliação. / In the last years, the amount of data generated by individuals and organizations has grown exponentially. It is estimated that there were 2.7 zettabytes of global data in 2012, and this number has doubled each two years. In addition to this, with the popularization of mobile connected devices, the user’s need to have ubiquous access has grown. Traditional solutions for backup and online file storage can no longer meet the current needs. The use of cloud storage for backup and file synchronization becomes a tool of great value to this kind of problem. However, implementing such a system becomes a significant technological challenge. Thus, this works proposes to solve the problem of storing files, designing a Cloud Storage architecture for storing archives. Throughout work, an analysis of the key business drivers for Cloud Storage and File storage is done by lifting inputs for designing an architecture. This architecture is described in detail for level that can be implemented. Finally, the work is validated through an evaluation of architecture whose methodology was adapted according to the characteristics of the evaluation team.

A história dos sistemas ERP tem início nos anos 90 com sua adoção por grandes corporações. Seu uso tem se intensificado, bem como suas funcionalidades complementares, com o objetivo de integrar os processos de gestão da empresa dentro e fora do espaço físico tradicional. Já sua adoção pelas organizações tem sido alavancada com objetivo de redução de custos, mas, justamente o custo de sua implantação tem sido um limitador. A possibilidade de utilização do ERP em cloud computing se mostra uma alternativa viável, pois reduz uma série de custos da implantação. No entanto, a implantação do ERP em cloud computing traz influências sobre o formato em que o ERP é implantado, bem como modifica os fatores relevantes (críticos para o sucesso) da sua implantação e utilização. Este trabalho analisa os fatores críticos de sucesso existentes na literatura atual e como estes foram relevantes em implantações feitas em cloud computing nas 3 empresas estudadas. Trata-se então de um estudo de casos realizado a partir de um roteiro de entrevistas, aplicado aos responsáveis pela implantação interna e externamente em três empresas brasileiras de ramos de atuação e sistemas implantados diferentes. Este trabalho apresenta conceitos relacionados aos sistemas ERP e os fatores críticos de sucesso disponíveis na literatura, bem como uma caracterização deste novo ambiente de cloud computing e a relação existente com implantações de ERP já registrados. A partir destes resultados outros estudos podem acompanhar a evolução de cloud computing ligado ao ERP ou a partir de uma base instalada maior, segmentar as análises e até mesmo consolidar metodologias de implantação para este novo formato. Por se tratar de um estudo de caso, as conclusões não podem ser generalizadas para todas as organizações, além disso, a existência de poucos fornecedores e poucas implantações de ERP no formato cloud computing, tratando-se de uma tecnologia muito recente, conferem outra limitação para este estudo. / The history of ERP\'s systems starts in 90 years with the adoption by large corporations. Its use has intensified since then as well as additional features, in order to integrate the company\'s management processes within and outside the traditional physical space. Since its adoption by organizations have been leveraged in order to reduce costs, but, just the cost of its implementation has been a limiter. The usability of ERP on cloud computing proves a viable alternative because it reduces a number of deployment costs. However, the implementation of ERP in cloud computing brings influences on the format in which the ERP is implemented, as well as modify the relevant factors (critical success) of their deployment and use. This paper analyzes the critical success factors in the existing literature and how these were relevant in cloud computing deployments made in 3 companies studied. It is then a case study carried out from a set of interviews applied to those responsible for implementing internally and externally in three Brazilian companies midsize segments of operation and different systems deployed. This paper presents concepts related to ERP systems and the critical success factors available in the literature, as well as a characterization of this new cloud computing environment and the relationship with existing ERP implementations already registered. From these results, other studies may follow the evolution of cloud computing ERP connected to or from a larger installed base, segment analysis and even consolidate deployment methodologies for this new format. Because it is a case study, the findings can\'t be generalized to all organizations, moreover, that there are few suppliers and few ERP implementations in the format cloud computing, as it is a very recent technology, provide another limitation for this study.

Denna uppsats undersöker hur svenska startup företag ser på Cloud Computing-teknologins möjligheter att främja deras entreprenörskap. För att undersöka detta har semistrukturerade intervjuer hållits med sex svenska startups i olika branscher. Intervjuerna kretsar kring den teoretiska bas som uppsatsen har lagt fram, huvudsakligen från Ross & Blumensteins teori kring hur Cloud Computing leder startup företag till entreprenöriella fördelar. Datan har analyserats tematiskt för att hitta återkommande teman som framträtt under intervjuerna. Cloud computing kan hjälpa svenska entreprenörer genom att sänka inträdesbarriärer och alternativkostnaden vilket kan minska riskerna och ge möjlighet att använda sig av konceptet “easy failure”. Flera av Cloud computing leverantörerna erbjuder finansiering till startups vilket ytterligare stärker dessa fördelar. Flera startups ser också operationella fördelar som förenklingar till en global arbetsplats, internationella samarbeten och att nå ut till en global marknadsplats. Dock framkommer också risker kopplat till säkerheten, integritetslagar och att förlora kontroll, även om det framträdande finns en stor tillit till leverantörernas säkerhetslösningar. Det framkommer också tecken på att många startups initialt kan adoptera denna nya teknologi och undgår därmed flera av de risker som finns kopplat till själva migrationsfasen. / This paper examines how Swedish startup companies look at Cloud Computing technology's ability to develop their entrepreneurship. To investigate this, semistructured interviews have been held with six Swedish startups in different industries. The interviews revolve around the theoretical basis that the essay has presented, mainly from Ross & Blumenstein's theory of how Cloud Computing leads startup companies to entrepreneurial benefits. The data has been analyzed thematically to find recurring themes that appeared during the interviews. Cloud computing can help Swedish entrepreneurs by reducing entry barriers and the opportunity costs, which can reduce risks and provide the opportunity to use the "easy failure" concept. Several Cloud computing providers offer funding for startups, which further strengthens these benefits. Several startups also see operational benefits like simplifications to a global workplace, international collaborations, and reaching a global marketplace. They also see risks associated with security, integrity laws and losing control. Although prominent, there is a great deal of confidence in suppliers' security solutions. There are also signs that many startups can initially adopt this new technology and avoid many of the risks associated with migration itself.

Les systèmes déployés dans être testés pendant l’élasticité, ce qui entraîne plusieurs problématiques. D’abord, l’exécution d’un test pendant l’élasticité peut exiger de conduire le CBS dans une succession de comportements élastiques spécifiques, càd., une séquence d’ajout/retrait de ressources, qui nécessite des variations précises de la charge des requêtes envoyées au cloud. Seconde, certaines adaptations du CBS ne sont réalisées qu’à un moment précis, par exemple après un ajout de ressources et, par conséquent, leurs tests doivent être synchronisés avec des états spécifiques du CBS. Troisième, les testeurs doivent rejouer les tests pendant l’élasticité de manière déterministe afin de déboguer et corriger le CBS. Quatrième, la création des tests pendant l’élasticité est complexe et laborieuse dû au large nombre de paramètres, et à la particularité du cloud computing. Enfin, seulement quelques combinaisons de paramètres peuvent causer des problèmes au CBS, que les cas de test créés au hasard peuvent manquer, alors qu’un jeu de tests couvrant toutes les combinaisons possibles serait trop grand et impossible à exécuter. Dans cette thèse, nous abordons toutes ces problématiques en proposant plusieurs approches :1) une approche qui conduit les CBSs dans une suite de comportements élastiques prédéfinis, 2) une approche qui synchronise l’exécution des tests selon les états du CBS, 3) une approche qui permette la reproduction des tests pendant l’élasticité, 4) un langage spécifique à ce domaine (DSL, selon l’acronyme anglais) qui résume la mise en œuvre des tests pendant l’élasticité, 5) une approche qui génère des petits ensembles de tests pendant l’élasticité tout en révélant des problèmes liés à l’élasticité. / Systems deployed on elastic infrastructures deal with resource variations by adapting themselves, which may cause errors, or even degrade their performance. Therefore, we must test the Cloud-Based Systems(CBSs) throughout elasticity, which faces problematics. First, executing elasticity tests may require the lead of CBS throughout a specific elastic behavior, i. e.,sequence of resource changes, which depends on an accurate workload generation. Second, CBS adaptations occur at a precise moment, such as after a resource scale out, which requires to test them during a specific CBS states. Third, testers must re-execute elasticity tests in a deterministic manner to debug and fix the CBS. Fourth, implementing elasticity tests is complex and laborious given the wide possibility of parameters, and the peculiarity of cloud computing. Finally, specific combinations of parameters may cause the system issues, where random tests may miss such combinations, while a test set that covers all thecombinations may be large and impractical to execute. In this thesis, we tackle all these five problematics by proposing several approaches: 1) an approach to drive the CBS throughout preset elastic behaviors, 2) an approach to synchronize tests according to the CBS states, 3) an approach to enable reproducing elasticity testing, 4) a Domain Specific Language (DSL)-basedapproach to abstract the elasticity testing implementation, and 5) an approach to generate small sets of tests that reveal relevant elasticity-related issues.

Cloud computing a connu un succès impressionnant au cours des dernières années. Cette expansion en permanence se manifeste clairement non seulement par l'omniprésence du Cloud dans les environnements académiques et industriels mais aussi par la hausse impressionnante du chiffre d'affaire des services Cloud. Ce succès important réalisé en peu de temps est achevé grâce à la caractéristique d'élasticité du Cloud. Afin de bien tirer profit de cette nouvelle architecture software et hardware, les fournisseurs de service Cloud requièrent des stratégies adéquates pour une gestion efficace des équipements physiques continuellement allouées à plusieurs clients simultanément. En effet, le fournisseur Cloud doit respecter le Service Level Agreement (SLA) et assurer la continuité de service afin de minimiser le coût des pénalités. Dans cette thèse, nous abordons la problématique de l'instanciation (i.e., mapping) des réseaux virtuels au sein du réseau coeur du Cloud tout en considérant la fiabilité des équipements physiques (i.e., routeurs et liens).Notre objectif principal est de maximiser le chiffre d'affaires du fournisseur Cloud par le biais de i) la maximisation du taux des réseaux virtuels acceptés dans le réseau coeur du Cloud etii) la minimisation des pénalités induites par les interruptions de service en raison de pannes des équipements du réseau. Il a été démontré que ce type de problème est NP-dur donc aucune solution optimale ne peut être calculée à large échelle. Premièrement, on a proposé un algorithme nommé PR-VNE qui favorise l'utilisation des équipements les plus fiables, lors de l'instanciation des réseaux virtuels, afin de minimiser l'impact néfaste des pannes. Cette proposition exploite les avantages de la métaheuristique de colonie d'abeilles pour assurer une qualité optimisée de mapping de la solution en termes d'optimalité. Il est à souligner que PR-VNE ne réserve aucune ressource de secours dans le cas où panne se présente. Deuxièmement, on a défini un algorithme qui adopte non seulement la stratégie préventive mais aussi un mécanisme curatif pour remédier aux pannes imprévisibles. Cet algorithme nommé CG-VNE est conçu par une modélisation basée sur la théorie des jeux. CG-VNE reconfigure (re-instancie) les ressources virtuelles impactées par les pannes dans d'autres équipements réseau sans pour autant réserver des ressources de secours. Finalement, on a considéré la vision macroscopique en définissant un algorithme qui traite un lot de requêtes tout en considérant la fiabilité. Cet algorithme nommé BR-VNE se base principalement sur la stratégie Monte-Carlo Tree Search pour trouver la meilleure séquence de mapping. L'évaluation des performances de nos propositions prouve leurs efficacités par rapport aux méthodes concurrentes de la littérature en terme de : i) taux de réseaux virtuels acceptés, ii) taux de requêtes impactées par les pannes et iii) revenu du fournisseur de service Cloud / Cloud computing has known an impressive success over the last few years and is continuously emerging personal and profesional life thanks to its elasticity, pricing model, etc. This innovative technology has attracted both industrial and research communities and becomes omnipresent.In order to take benefit from the Cloud expansion, providers require effecient management strategies to properly supply their physical capabilities such as network resources. Besides, Cloud providers have to respect the Service Level Agreements (SLA) and avoid any outage impact in order to guarantee the Cloud-based service continuity. In this thesis we tackle the problem of reliable virtual network embedding within the Cloud backbone by considering the impact of physical equipments' outages. Our main focus is to improve the provider's turnover by i) maximizing the acceptance rate of the incoming virtual networks issued from clients' requestand ii) minimizing the penalties induced by service disruption due to the physical failures. This optimization problem is NP-hard with multi-objective and non-linear formalization. To cope with this complexity and since reaching the optimal solution is computationally intractable, we propose different strategies that aim to respect the aformentionned objectives. First, we propose a preventive approach named PR-VNE that urges the use of reliable network resources in order to avoid the physical failures impact.PR-VNE strongly relies on the Artificial Bee Colony metaheuristic to reach an optimized solution. It should be highlighted that PR-VNE does not adopt a recovering mechanism to deal with the network outages. Second, we devise a new reactive approach named CG-VNE that does not consider any backup resources but re-embed the impacted virtual resources once an outage occurs in the underlying network. As well as this reactive mechanism, CG-VNE adopts the same preventive strategy like PR-VNE by avoiding the unreliable resources. It should be noted that CG-VNE is devised basing on a Game Theory framework by defining a collaborative mapping game. Finally, we deal with the survivable batch mapping problem that considers the embedding of a virtual networks set instead of one client request. We introduce a new reliable batch embedding strategy named BR-VNE that relies on Monte-Carlo Tree Search algorithm.BR-VNE delegates the embedding of one virtual network request to any online algorithm and focuses to find the best mapping sequence order. The performance evaluation of our algorithms leads to efficient results

Distributed cloud computing, when integrated with smartphone capabilities, contribute to building an efficient multimedia e-health application for mobile devices. Unfortunately, mobile devices alone do not possess the ability to run complex machine learning algorithms, which require large amounts of graphic processing and computational power. Therefore, offloading the computationally intensive part to the cloud, reduces the overhead on the mobile device. In this thesis, we introduce two such distributed cloud computing models, which implement machine learning algorithms in the cloud in parallel, thereby achieving higher accuracy. The first model is based on MapReduce SVM, wherein, through the use of Hadoop, the system distributes the data and processes it across resizable Amazon EC2 instances. Hadoop uses a distributed processing architecture called MapReduce, in which a task is mapped to a set of servers for processing and the results are then reduced back to a single set. In the second method, we implement cloud virtualization, wherein we are able to run our mobile application in the cloud using an Android x86 image. We describe a cloud-based virtualization mechanism for multimedia-assisted mobile food recognition, which allow users to control their virtual smartphone operations through a dedicated client application installed on their smartphone. The application continues to be processed on the virtual mobile image even if the user is disconnected for some reason. Using these two distributed cloud computing models, we were able to achieve higher accuracy and reduced timings for the overall execution of machine learning algorithms and calorie measurement methodologies, when implemented on the mobile device.