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A Case Study of Profile Driven Scheduling for a Heterogeneous Cluster of Workstations

• Tags:
• machines,
• scheduling,
• decisions,
• discovery

Overview Computations such as wind-tunnel simulations, gene and protein analysis, drug discovery, and CGA rendering are run on commodity computers with very successful results. At the same time most cluster environments employ a highly heterogeneous set of machines due to the natural cycle of upgrades and purchases followed by most organizations. In such environments it is likely that some applications may be better suited for one type of machine over another and that the placement of jobs on machines can have a large impact on throughput and job completion time. This paper argues that using profile information to guide scheduling decisions can yield substantial performance improvements over a simple FCFS scheduling policy.

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