  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.

Distributed, Parallel, and Cluster Computing;Machine Learning