| Publisher | The University of Chicago | ||
|---|---|---|---|
| Format | 206.0KB PDF, requires Acrobat Rdr 5 | Date added | 07 Apr 2004 |
| Topics | High Performance Computing | ||
| Downloads | 37 | ||
In many scientific applications, Grid technologies and infrastructures facilitate distributed resource sharing and coordination in dynamic, heterogeneous multi-institutional environments. Replication of data can help enable high-throughput file transfer and scalable resource storage in scientific Grid applications that involve large data transfers. The selection of a replica can, however, significantly influence the efficiency of a replication scheme. Many current approaches assume that a significant amount of data is available, such as network status information, log files of historical GridFTP file transfers, and CPU status and predictions. This paper proposes a lightweight instance-based learning (IBL) algorithm to allow efficient replica selection with much less required data.
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