De-Centralized Job Scheduling On Computational Grids Using Distributed Backfilling

To dynamically improve system selections of the waiting jobs under de-centralized scheduling frameworks, each computation system together with its neighbors is assumed to compose a subgrid, and distributed backfilling is proposed as the scheduling strategy in each subgrid. Whenever a job terminates, distributed backfilling is triggered to re-backfill all waiting jobs on the corresponding subgrid in order of their submittal. Each subgrid is overlapped with another, so the waiting jobs may be migrated around the Grid. To evaluate distributed backfilling, Grid resources and scheduling framework are configured, the model of Grid workload is constructed by extending the workload models of parallel systems and the Job Scheduling Simulation System is designed to simulate the process of de-centralized job scheduling. to addition, job speedup is presented as performance metrics of scheduling strategies. Results show the dynamic optimization of system 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. Copyright (c) 2006 John Wiley & Sons, Ltd.