A Priority-Based Scheduling Heuristic to Maximize Parallelism of Ready Tasks for DAG Applications

In practical Cloud/Grid computing systems, DAG scheduling may be faced with challenges arising from severe uncertainty about the underlying platform. For instance, it could be hard to have explicit information about task execution time and/or the availability of resources, both may change dynamically, in difficult to predict ways. In such a setting, the development of various kinds of just-in-time scheduling schemes, which aim at maximizing the parallelism of ready tasks of DAG, seems to be a promising approach to cope with the lack of environment information and achieve efficient DAG execution. Although many attempts have been tried to develop such just-in-time scheduling heuristics, most of them are based on DAG decomposition, which results in complicated and suboptimal solutions for general DAGs. This paper presents a priority-based heuristic, which is not only easy to apply to arbitrary DAGs, but also exhibits comparable or better performance than the existing solutions.