This repository is home to the scheduling simulator, machine learning tools, and experimental results from the SC'15 submission Improving Backfilling by using Machine Learning to predict Running Times

The following files contain metrics for all the so-called heuristic triples, in the CSV format.

experiments/data/CEA-curie/sim_analysis/metrics_complete

experiments/data/KTH-SP2/sim_analysis/metrics_complete

experiments/data/CTC-SP2/sim_analysis/metrics_complete

experiments/data/SDSC-SP2/sim_analysis/metrics_complete

experiments/data/SDSC-BLUE/sim_analysis/metrics_complete

experiments/data/Metacentrum2013/sim_analysis/metrics_complete

The Scheduling simulator used in this paper is a fork of the pyss open source scheduler. It found in the folder:

simulation/pyss/src

Implementations of the NAG algorithm for learning the model are located at:

simulation/pyss/src/predictors/