Matching and scheduling problem proved to be a critical problematic in different domains including Cloud computing. Therefore, to ensure the commercial success of the Cloud computing paradigm, it is necessary to develop methods that allow users to optimize the use of resources. Even though there are several algorithms for scheduling applications in heterogeneous environment such as grid computing, they cannot benefit from the recent advent of Cloud computing. Indeed, these algorithms assume that the number of resources available to users is bounded, this is against the illusion of infinite resources of Cloud computing. Also, only the execution time (makespan) is taken into account. However, Cloud computing business model is based on pay as you go. Accordingly, execution cost begot using a set of resources should be considered. To overcome the limitations of existing works, this paper propose new strategies for matching and scheduling business process instances in the Cloud context. The proposed strategies aim at scheduling business process instances while minimizing two conflicting criteria on the one hand, and ensuring fairness between the considered instances on the other hand. A serie of experiments demonstrate that they present good performances