Cloud platforms have emerged as a leading solution for computation. In the meantime, large computations have shifted from big parallel tasks to workflows of smaller tasks with data dependencies between them. Task placement is a major issue on Cloud platforms, especially considering the impact of data exchanges on cost and makespan. In this paper, we investigate the consequences of network contention regarding the use of existing scheduling policies on DaaS-based platforms (DaaS for Data as a Service). We show here that the legacy algorithms use inefficient network models. We then modify those algorithms using a new model inspired by DaaS-based Cloud platforms. Thus, we manage to statically pack tasks so that a batch scheduler could deploy many real-time submitted workflows on a dynamic Cloud platform. Simulations of Fork-Join workflows deployment using SimGrid show that our algorithm reduces computation time as well as deployment costs.