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Task Scheduling and File Replication for Data-Intensive Jobs with Batch-shared I/O
2006 15th IEEE International Conference on High Performance Distributed Computing
This paper addresses the problem of efficient execution of a batch of data-intensive tasks with batch-shared I/O behavior, on coupled storage and compute clusters. Two scheduling schemes are proposed: 1) a 0-1 Integer Programming (IP) based approach, which couples task scheduling and data replication, and 2) a bi-level hypergraph partitioning based heuristic approach (BiPartition), which decouples task scheduling and data replication. The experimental results show that: 1) the IP scheme
doi:10.1109/hpdc.2006.1652155
dblp:conf/hpdc/KhannaVCKKSS06
fatcat:fjjimqogvjdyncrbztsmomcuom