Abstract
To dynamically improve node selections of the waiting jobs under de-centralized scheduling, each node together with its neighbors is assumed to compose a subgrid, using distributed backfilling to optimize grid scheduling on each subgrid. Whenever a job terminates, distributed backfilling is triggered to rebackfill all waiting jobs on the corresponding subgrid in order of their submittal. Each subgrid is overlapped with some other, so the waiting jobs may be migrated around the grid. A simulated grid is established, while grid workload is modeled by extending workload models of parallel systems. Job speedup is used to evaluate scheduling strategies. Results show the dynamic optimization of node selections brought by distributed backfilling is grid-wide, and can improve scheduling performance remarkably as long as grid load is not too light and the job migration costs are not too high.
This work was sponsored by the National Science Foundation of China (NSFC) under the grant No. 60273085, the state High-Tech Research and Development project (863) under the grant No. 2001AA111081, and the ChinaGrid project of China.
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Wang, Q., Gui, X., Zheng, S., Xie, B. (2004). De-centralized Job Scheduling on Computational Grids Using Distributed Backfilling. In: Jin, H., Pan, Y., Xiao, N., Sun, J. (eds) Grid and Cooperative Computing - GCC 2004. GCC 2004. Lecture Notes in Computer Science, vol 3251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30208-7_42
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DOI: https://doi.org/10.1007/978-3-540-30208-7_42
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