Adam Oliner, Anand Padmanabha Iyer, Ion Stoica, Eemil Lagerspetz and Sasu Tarkoma
EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2013-17
March 8, 2013
http://www2.eecs.berkeley.edu/Pubs/TechRpts/2013/EECS-2013-17.pdf
We aim to detect and diagnose energy anomalies, abnormally heavy battery use. This paper describes a collaborative black-box method, and an implementation called Carat, for performing such diagnosis on mobile devices. A client app sends intermittent, coarse-grained measurements to a server, which identifies correlations between higher expected energy use and client properties like the running apps, device model, and operating system. The analysis quantifies the error and confidence associated with a diagnosis, suggests actions the user could take to improve battery life, and projects the amount of improvement. Carat detected all anomalies in a controlled experiment and, during a deployment to a community of more than 340,000 devices, identified thousands of energy anomalies in the wild. On average, a Carat user’s battery life increased by 10% after 10 days.
BibTeX citation:
@techreport{Oliner:EECS-2013-17, Author = {Oliner, Adam and Padmanabha Iyer, Anand and Stoica, Ion and Lagerspetz, Eemil and Tarkoma, Sasu}, Title = {Carat: Collaborative Energy Diagnosis for Mobile Devices}, Institution = {EECS Department, University of California, Berkeley}, Year = {2013}, Month = {Mar}, URL = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2013/EECS-2013-17.html}, Number = {UCB/EECS-2013-17}, Abstract = {We aim to detect and diagnose energy anomalies, abnormally heavy battery use. This paper describes a collaborative black-box method, and an implementation called Carat, for performing such diagnosis on mobile devices. A client app sends intermittent, coarse-grained measurements to a server, which identifies correlations between higher expected energy use and client properties like the running apps, device model, and operating system. The analysis quantifies the error and confidence associated with a diagnosis, suggests actions the user could take to improve battery life, and projects the amount of improvement. Carat detected all anomalies in a controlled experiment and, during a deployment to a community of more than 340,000 devices, identified thousands of energy anomalies in the wild. On average, a Carat user’s battery life increased by 10% after 10 days.} }
EndNote citation:
%0 Report %A Oliner, Adam %A Padmanabha Iyer, Anand %A Stoica, Ion %A Lagerspetz, Eemil %A Tarkoma, Sasu %T Carat: Collaborative Energy Diagnosis for Mobile Devices %I EECS Department, University of California, Berkeley %D 2013 %8 March 8 %@ UCB/EECS-2013-17 %U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2013/EECS-2013-17.html %F Oliner:EECS-2013-17