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.
CITATION STYLE
da Silva, F. A. B., & Scherson, I. D. (2000). Improving parallel job scheduling using runtime measurements. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1911, pp. 18–39). Springer Verlag. https://doi.org/10.1007/3-540-39997-6_2
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