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Scheduling multiple applications on heterogeneous multi-clusters is challenging as the different applications have to compete for resources. A scheduler thus has to ensure a fair distribution of resources among the applications and prevent harmful selfish behaviors while still trying to minimize their respective completion time. In this paper we consider mixed-parallel applications, represented by graphs whose nodes are data-parallel tasks, that are scheduled in two steps: allocation and mapping. We investigate several strategies to constrain the amount of resources the scheduler can allocate to each application and evaluate them over a wide range of scenarios.