  We present a machine learning framework and a new test bed for data mining
from the Slurm Workload Manager for high-performance computing (HPC) clusters.
The focus was to find a method for selecting features to support decisions:
helping users decide whether to resubmit failed jobs with boosted CPU and
memory allocations or migrate them to a computing cloud. This task was cast as
both supervised classification and regression learning, specifically,
sequential problem solving suitable for reinforcement learning. Selecting
relevant features can improve training accuracy, reduce training time, and
produce a more comprehensible model, with an intelligent system that can
explain predictions and inferences. We present a supervised learning model
trained on a Simple Linux Utility for Resource Management (Slurm) data set of
HPC jobs using three different techniques for selecting features: linear
regression, lasso, and ridge regression. Our data set represented both HPC jobs
that failed and those that succeeded, so our model was reliable, less likely to
overfit, and generalizable. Our model achieved an R^2 of 95\% with 99\%
accuracy. We identified five predictors for both CPU and memory properties.
