Error detection and error classification: failure awareness in data transfer scheduling
International Journal of Autonomic Computing
Workflow Scheduling to Minimize Data Movement Using Multi-constraint Graph Partitioning
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
Integration of Workflow Partitioning and Resource Provisioning
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
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In this paper, a workflow scheduling approach, which consists of two algorithms, is proposed. A submitted workflow is first partitioned into subgraphs on the global Grid level by the graph partitioning algorithm according to features of the workflow itself and the status of selected available resource clusters. Then, at the resource cluster level, metatasks in each subgraph are allocated to computational resources by the metatask mapping algorithm. To reduce the total makespan of a workflow, the schedule of raw input data preloading are considered by the two algorithms. This two-phase approach does not require detailed resource information or control privilege on every Grid resource for Grid schedulers at the global Grid level, so that the dependence on Grid information services is reduced and the higher priority of local resource management policies is respected.