Distributed computations, dealing with large amounts of data, are scheduled in Grid clusters today using either a task-centric mechanism, or a worker-centric mechanism. Because of the large data sets, the execution time is bounded by the cost of data transfer. In this paper, we introduce new worker-centric scheduling strategies that are novel in that they aim to implicitly exploit the locality of interest in order to reduce the cost of data transfer. Many Grid applications are characterized by such a locality of interest, i.e., a file is often accessed by multiple tasks and, more importantly, a set of files that are accessed by one task are also likely to be accessed together by other tasks. Our new deterministic, as well as probabilistic, scheduling algorithms implicitly exploit this feature to improve running time. Our experiments are done with traces of a real Grid application ( Coadd), and show that our algorithms are able to achieve utilization of over 90%, while reducing makespan significantly compared to task-centric approaches.