Trading Cycles for Information: Using Replication to Schedule Bag-of-Tasks Applications on Computational Grids

Scheduling independent tasks on heterogeneous environments, like grids, is not trivial. To make a good scheduling plan on this kind of environments, the scheduler usually needs some information such as host speed, host load, and task size. This kind of information is not always available and is often difficult to obtain. In this paper we propose a scheduling approach that does not use any kind of information but still delivers good performance. Our approach uses task replication to cope with the dynamic and heterogeneous nature of grids without depending on any information about machines or tasks. Our results show that task replication can deliver good and stable performance at the expense of additional resource consumption. By limiting replication, however, additional resource consumption can be controlled with little effect on performance.