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Improving Parallel Job Scheduling Using Runtime Measurements
| Citations: | 12 - 1 self |
BibTeX
@MISC{Silva_improvingparallel,
author = {Fabricio Alves Barbosa da Silva and Isaac D. Scherson},
title = {Improving Parallel Job Scheduling Using Runtime Measurements},
year = {}
}
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Abstract
We investigate the use of runtime measurements to improve job scheduling on a parallel machine. Emphasis is on gang scheduling based strategies. With the information gathered at runtime, we define a task classification scheme based on fuzzy logic and Bayesian estimators. The resulting local task classification is used to provide better service to I/O bound and interactive jobs under gang scheduling. This is achieved through the use of idle times and also by controlling the spinning time of a task in the spin block mechanism depending on the node's workload. Simulation results show considerable improvements, in particular for I/O bound workloads, in both throughput and machine utilization for a gang scheduler using runtime information compared with gang schedulers for which this type of information is not available.
Keyphrases
gang scheduler local task classification simulation result machine utilization parallel machine runtime information bound workload spin block mechanism considerable improvement interactive job task classification scheme bayesian estimator spinning time runtime measurement idle time fuzzy logic gang scheduling
