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First, I would like to thank my supervisors for their support and advices. Next, I would like to thank my thesis's reviewers Christophe Cérin and Jean-Louis Pazat, and the members of my thesis committee: Christine Froidevaux, Célia Ghedini Ralha, and Christine Morin. I appreciate all yours comments and questions.Additionally, I would like to thank the professors Vander Alves, Genaína Rodrigues, Li Weigang, and Jacques Blanc for their comments, suggestions, and discussions.Next, I am very thankful to Katia Evrat and Valérie Berthou for kindly helping me in various administrative works. In addition, I am grateful to the authors that sent me their papers when I requested them.After, I would like to thank all my team members in INRIA, especially Michael Kruse and Taj Muhammad Khan. I also thank my friends Cícero Roberto, Antônio Junior, Éric Silva, André Ribeiro, Francinaldo Araujo, Emerson Macedo, and the former Logus's team. Moreover, I would like to thank the people I collaborated with during the last years.I further acknowledge the financial assistance of CAPES/CNPq through the program science without borders (grant 237561/2012-3) during the year of 2013, and Campus France/INRIA (project Pascale) during the period of February to July and November to December 2014.Last but not least, I would like to thank my parents and my family for their unconditional support and patience.

International audienceNowadays, cloud users face three important problems: (a) choosing one or more appropriate cloud provider(s) to run their application(s), (b) selecting appropriate cloud resources, which implies having enough information about the available resources, including their characteristics and constraints, and (c) configuring the cloud resources. These problems are mostly due to the wide range of resources. These resources usually have distinct dependencies, and they are offered at various clouds' layers. In this complex scenario, the users often have to handle cloud resources and their dependencies manually. This is an error-prone and time-consuming activity, even for skilled cloud users and system administrators. In this context, this paper proposes a software product line engineering (SPLE) method and a tool to deal with these issues. Our SPL-based engineering method enables a declarative and goal-oriented strategy. Furthermore, it allows resource selection and configuration in inter-cloud environments. In our proposal, the cloud users specify their applications and requirements, and our tool automatically selects and configures a suitable computing environment, taking into account temporal and functional dependencies. Experimental results on Amazon EC2 and Google Compute Engine (GCE) show that our approach enables unskilled users to have access to advanced inter-cloud computing configurations, without being concerned with the characteristics of each cloud

International audienceConfiguring and executing applications across multiple clouds is a challenging task due to the various terminologies used by the cloud providers. Therefore, we advocate the use of autonomic systems to do this work automatically. Thus, in this paper, we propose and evaluate Dohko, an autonomic and goal-oriented system for inter-cloud environments. Dohko implements self-configuration, self-healing, and context-awareness properties. Likewise, it relies on a hierarchical P2P overlay (a) to manage the virtual machines running on the clouds and (b) to deal with inter-cloud communication. Furthermore, it depends on a software product line engineering method to enable applications’ deployment and reconfiguration, without requiring pre-configured virtual machine images. Experimental results show that Dohko can free the users from the duty of executing non-native cloud application on single and over many clouds. In particular, it tackles the lack of middleware prototypes that can support different scenarios when using simultaneous services from multiple clouds