Abstract | ||
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We address the problem of predicting whether sufficient memory and CPU resources have been requested for jobs at submission time. For this purpose, we examine the task of training a supervised machine learning system to predict the outcome - whether the job will fail specifically due to insufficient resources - as a classification task. Sufficiently high accuracy, precision, and recall at this task facilitates more anticipatory decision support applications in the domain of HPC resource allocation. Our preliminary results using a new test bed show that the probability of failed jobs is associated with information freely available at job submission time and may thus be usable by a learning system for user modeling that gives personalized feedback to users. |
Year | Venue | Field |
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2018 | arXiv: Distributed, Parallel, and Cluster Computing | USable,Computer science,Predictive analytics,Decision support system,Resource allocation,User modeling,Artificial intelligence,Recall,Machine learning,Computer cluster |
DocType | Volume | Citations |
Journal | abs/1806.01116 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Dan Andresen | 1 | 0 | 0.34 |
William H. Hsu | 2 | 321 | 40.20 |
Huichen Yang | 3 | 3 | 2.50 |
Adedolapo Okanlawon | 4 | 0 | 0.34 |