Cloud computing has emerged during the last decade to be widely adopted nowadays in several IT areas. It consists to propose market or not marketoriented resources as services that can be consumed in a ubiquitous, flexible and transparent way. In this PhD thesis, we deal with scheduling, one of the major cloud computing issue. According to the targeted cloud configuration, we have identified three levels of scheduling: service-level, task-level and Virtual Machine-level. We revisit the problem modeling, the design and the implementation of multi-objective metaheuristics for each scheduling level of the cloud. The proposed metaheuristicsbased schedulers address different criteria including energy consumption, greenhouse gas emissions, profit and QoS (cost and response time). We prove their adaptability to the cloud constraints by integrating them as a part of the OpenNebula cloud manager. Moreover, our schedulers have been extensively experimented using realistic cloud configurations on Grid'5000, considered as an infrastructure as a service (IAAS), and concrete scenarios based on Amazon EC2 instances and prices. The reported results show that our proposed methods outperform existing scheduling approaches in terms of all previously cited criteria.
