Data Grid is a Grid for ubiquitous access and analysis of large-scale data. Because Data Grid is in the early stages of development, the performance of its petabyte-scale models in a realistic data processing setting has not been well investigated. By enhancing our Bricks Grid simulator to accommodated Data Grid scenarios, we investigate and compare the performance of different Data Grid models. These are categorized mainly as either central or tier models; they employ various scheduling and replication strategies under realistic assumptions of job processing for CERN LHC experiments on the Grid Datafarm system. Our results show that the central model is efficient but that the tier model, with its greater resources and its speculative class of background replication policies, are quite effective and achieve higher performance, while each tier is smaller than the central model.

The Grid Datafarm (Gfarm) architecture is designed for global petascale data-intensive computing. It provides a global parallel filesystem with online petascale storage, scalable I/O bandwidth, and scalable parallel processing, and it can exploit local I/O in a grid of clusters with tens of thousands of nodes. Gfarm parallel I/O APIs and commands provide a single filesystem image and manipulate filesystem metadata consistently. Fault tolerance and load balancing are automatically managed by file duplication or recomputation using a command history log. Preliminary performance evaluation has shown scalable disk I/O and network bandwidth on 64 nodes of the Presto III Athlon cluster. The Gfarm parallel I/O write and read operations has achieved data transfer rates of 1.74 GB/s and 1.97 GB/s, respectively, using 64 cluster nodes. The Gfarm parallel file copy reached 443 MB/s with 23 parallel streams on the Myrinet 2000. The Gfarm architecture is expected to enable petascale data-intensive Grid computing with an I/O bandwidth scales to the TB/s range and scalable computational power.