Experiences on Data Flow Models for DAG Applications Executed on Heterogeneous and Changing Computational Environments

I. Hernandez, B. Aleman, and H. Marin (Mexico)


DAG scheduling, reactive scheduling, workflow execution, heterogeneous computing


We consider the problem of scheduling DAG applications onto heterogeneous and dynamic distributed computational platforms. These applications often require to compute and move a consider able amount of data among tasks. Most mapping methods focus on scheduling strategies which use the shape and static informa tion of the DAG. They do not consider the mechanism through which communication of task results is actually achieved. We have found that ignoring this issue may negatively affect the per formance of the application. In this paper, we explore two differ ent models to allow the transfer of data among tasks, the PUSH model and the PULL model. Both models were implemented within the GTP (Global Task Positioning) model. GTP is a re active scheduling method for DAG applications characterised by allowing rescheduling and migration of tasks in response to sig nificant variations in resource characteristics. We present the per formance of the GTP model when using the PUSH or the PULL model.

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