Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments

TL;DR

This paper presents a machine learning approach to predict idle times in volunteer computing environments, significantly reducing energy waste while maintaining task completion efficiency.

Contribution

It introduces a novel combination of Random Forest and MultiLayer Perceptron models to predict computer availability, optimizing task scheduling in volunteer HTC systems.

Findings

Reduced energy waste by 51.4% through prediction-based task targeting.

Maintained comparable task completion times despite energy savings.

Demonstrated effectiveness via simulation in volunteer computing scenarios.

Abstract

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.