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We present a machine learning framework and a new test bed for data miningfrom 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 andmemory allocations or migrate them to a computing cloud. This task was cast asboth supervised classification and regression learning, specifically,sequential problem solving suitable for reinforcement learning. Selectingrelevant features can improve training accuracy, reduce training time, andproduce a more comprehensible model, with an intelligent system that canexplain predictions and inferences. We present a supervised learning modeltrained on a Simple Linux Utility for Resource Management (Slurm) data set ofHPC jobs using three different techniques for selecting features: linearregression, lasso, and ridge regression. Our data set represented both HPC jobsthat failed and those that succeeded, so our model was reliable, less likely tooverfit, and generalizable. Our model achieved an R^2 of 95\% with 99\%accuracy. We identified five predictors for both CPU and memory properties.

Feature Selection for Learning to Predict Outcomes of Compute Cluster Jobs with Application to Decision Support