MLEE: Method Level Energy Estimation — A machine learning approach

Abstract

Battery life is one of the main concerns for today’s mobile users. Due to an imbalance in demand and supply of energy in mobile devices, the burden to make battery last longer upon each charge has shifted towards application developers, who, in turn attempt to create energy efficient applications. However, mobile developers lack the tools to detect energy consumption hot-spots in their code. We aim to provide developers with a technique that helps them to precisely locate energy hot-spots at the method-level. In this paper we present MLEE, a novel approach for estimating energy consumption of methods. MLEE uses machine learning models to predict the energy consumption at method-level using software metrics as features. We use the Snapdragon power profiler to measure the energy consumption of applications using the shortest time interval to develop a method-level energy dataset for training machine learning prediction models. We demonstrate that several structural metrics of methods are highly co-related with energy consumption. Thereafter we use these features to predict the energy consumption of methods using linear regression, random forest and decision tree with an average mean-absolute-error of 2.6e−2 J.