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Allocation optimale multicontraintes des workflows aux ressources d’un environnement Cloud Computing

Abstract : Cloud Computing is increasingly recognized as a new way to use on-demand, computing, storage and network services in a transparent and efficient way. In this thesis, we address the problem of workflows scheduling on distributed heterogeneous infrastructure of Cloud Computing. The existing workflows scheduling approaches mainly focus on the bi-objective optimization of the makespan and the cost. In this thesis, we propose news workflows scheduling algorithms based on metaheuristics. Our algorithms are able to handle more than two QoS (Quality of Service) metrics, namely, makespan, cost, reliability, availability and energy in the case of physical resources. In addition, they address several constraints according to the specified requirements in the SLA (Service Level Agreement). Our algorithms have been evaluated by simulations. We used (1) synthetic workflows and real world scientific workflows having different structures, for our applications; and (2) the features of Amazon EC2 services for our Cloud. The obtained results show the effectiveness of our algorithms when dealing multiple QoS metrics. Our algorithms produce one or more solutions which some of them outperform the solution produced by HEFT heuristic over all the QoS considered, including the makespan for which HEFT is supposed to give good results.
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Sonia Yassa. Allocation optimale multicontraintes des workflows aux ressources d’un environnement Cloud Computing. Traitement du signal et de l'image [eess.SP]. Université de Cergy Pontoise, 2014. Français. ⟨NNT : 2014CERG0730⟩. ⟨tel-01167131⟩

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