The success of grid computing depends on the existence of grid middleware that provides core services such as security, data management, resource information, and resource brokering and scheduling. Current general-purpose grid resource brokers deal only with computation requirements of applications, which is a limitation for data grids that enable processing of large scientific data sets. In this paper, a new data-aware resource brokering scheme, which factors both computational and data transfer requirements into its cost models, has been implemented and tested. The experiments reported in this paper clearly demonstrate that both factors should be considered in order to efficiently schedule data intensive tasks.
['Huy Le', 'Paul D. Coddington', 'Andrew L. Wendelborn']
A data-aware resource broker for data grids