  High Throughput Computing (HTC) provides a convenient mechanism for running
thousands of tasks. Many HTC systems exploit computers which are provisioned
for other purposes by utilising their idle time - volunteer computing. This has
great advantages as it gives access to vast quantities of computational power
for little or no cost. The downside is that running tasks are sacrificed if the
computer is needed for its primary use. Normally terminating the task which
must be restarted on a different computer - leading to wasted energy and an
increase in task completion time. We demonstrate, through the use of
simulation, how we can reduce this wasted energy by targeting tasks at
computers less likely to be needed for primary use, predicting this idle time
through machine learning. By combining two machine learning approaches, namely
Random Forest and MultiLayer Perceptron, we save 51.4% of the energy without
significantly affecting the time to complete tasks.
