GreenOracle

Abstract

Software energy consumption is a relatively new concern for mobile application developers. Poor energy performance can harm adoption and sales of applications. Unfortunately for the developers, the measurement of software energy con-sumption is expensive in terms of hardware and difficult in terms of expertise. Many prior models of software energy consumption assume that developers can use hardware instrumentation and thus cannot evaluate software runningwithin emulators or virtual machines. Some prior modelsrequire actual energy measurements from the previous versions of applications in order to model the energy consumption of later versions of the same application.In this paper, we take a big-data approach to software energy consumption and present a model that can estimate software energy consumption mostly within 10% error (in joules) and does not require the developer to train on energy measurements of their own applications. This model leverages a big-data approach whereby a collection of prior applications’ energy measurements allows us to train, trans-mit, and apply the model to estimate any foreign application’s energy consumption for a test run. Our model is based on the dynamic traces of system calls and CPU utilization.