Although Grids have been used extensively for executing applications with compute-intensive jobs, there exist several applications with a large number of lightweight jobs. The overall processing undertaking of these applications involves high overhead time and cost in terms of (i) job transmission to and from Grid resources and, (ii) job processing at the Grid resources. Therefore, there is a need for an efficient job grouping-based scheduling system to dynamically assemble the individual fine-grained jobs of an application into a group of jobs, and send these coarse-grained jobs to the Grid resources. This dynamic grouping should be done based on the processing requirements of each application, Grid resources' availability and their processing capability. In this paper, we present a scheduling strategy that performs dynamic job grouping activity at runtime and convey the detailed analysis by running simulations. In addition, job processing granularity size is introduced to facilitate the job grouping activity in determining the total amount of jobs that can be processed in a resource within a specified time.
|Cite as: Muthuvelu, N., Liu, J., Soe, N.L., Venugopal, S., Sulistio, A. and Buyya, R. (2005). A Dynamic Job Grouping-Based Scheduling for Deploying Applications with Fine-Grained Tasks on Global Grids. In Proc. Australasian Workshop on Grid Computing and e-Research (AusGrid 2005), Newcastle, Australia. CRPIT, 44. Buyya, R., Coddington, P. and Wendelborn, A., Eds. ACS. 41-48. |
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