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In this article, we revisit the problem of scheduling dynamically generated directed acyclic graphs (DAGs) of multi-processor tasks (M-tasks). A DAG is a basic model for expressing workflows applications where each node represents a task of the workflow. We present a novel algorithm (DMHEFT) for scheduling dynamically generated DAGs onto a heterogeneous collection of clusters. The scheduling decisions are based on the predicted runtime of an M-task as well as the estimation of the redistribution costs between data-dependent tasks. The algorithm also takes care of unfavorable placements of M-tasks by considering the postponing of ready tasks even if idle processors are available. We evaluate the scheduling algorithm by comparing the resulting makespans to the results obtained by using other scheduling algorithms, such as RePA and MHEFT.