作者
Frank Po-Chen Lin, Frederick Kin Hing Phoa
发表日期
2019/10
期刊
International Journal of Machine Learning and Computing
卷号
9
期号
5
简介
Supercomputing has been indispensable in the unstoppable trend of high-speed computing evolution. This work aims at improving its running efficacy by introducing a new two-step scheduling approach. Based on the analysis of large historical data, we provide an accurate runtime estimation scheme using Instance-Based Learning (IBL) in the first step. Then a swarm intelligence based scheduling (SIBS) method is proposed to optimize the scheduling performance in terms of total runtime makespan and fair resource allocation. A method comparison on a dataset from the ALPS supercomputer, which consists of 804k workload data in 2016, shows that our proposed method outperforms the most commonly used strategy–Extensible Argonne Scheduling System (EASY).
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FPC Lin, FKH Phoa - International Journal of Machine Learning and …, 2019