Accurate Online Power Estimation and Automatic Battery Behavior Based Power Model Generation for Smartphones Lide Zhangy Birjodh Tiwanay Zhiyun Qiany Zhaoguang Wangy Robert P. Dicky Z. Morley Maoy Lei Yang? yEECS Department, University of Michigan ? Google Inc. Ann Arbor, MI, USA Mountain View, CA, USA {lide,tiwana,zhiyunq,zgw,dickrp,zmao}@umich.edu
[email protected] ABSTRACT platforms have incorporated power-saving features, allowing This paper describes PowerBooter, an automated power components to dynamically adjust their power consumptions model construction technique that uses built-in battery volt- based on required functionality and performance. However, age sensors and knowledge of battery discharge behavior to using these features wisely (or at least avoiding undermin- monitor power consumption while explicitly controlling the ing their benefits) requires that software developers under- power management and activity states of individual com- stand the implications of their design decisions. Unfortu- ponents. It requires no external measurement equipment. nately, many software developers have limited experience We also describe PowerTutor, a component power manage- with energy-constrained portable embedded systems such as ment and activity state introspection based tool that uses smartphones. As a consequence, many smartphone applica- the model generated by PowerBooter for online power esti- tions are unnecessarily power-hungry. mation. PowerBooter is intended to make it quick and easy End users have difficulty determining which applications for application developers and end users to generate power are energy-efficient, and which squander energy; as a re- models for new smartphone variants, which each have dif- sult, application users may blame short battery lifespans on ferent power consumption properties and therefore require the operating system or hardware platform instead of un- different power models.