A framework for data management in the grid

In Grid Computing, a job could be executed on a node that is geographically far away from its data files. These files are stored in heterogeneous storage systems located at geographically distributed virtual organizations. The current approach includes explicit data file transfers to execution nodes, which forces users to deal with different administrative policies at each site and various data access mechanisms on each storage system. This implies a lot of human interventions in order to develop dedicated programs and scripts for data transfers for job execution. This paper presents GRAVY, a framework which enables the data management between distributed file systems irrespective of their heterogeneity. This feature enables high-level schedulers integrated with GRAVY to control data placements like computational jobs (i.e., they can be queued, scheduled and monitored). GRAVY supports multiple data transport protocols and can be extended easily.