Electrical Engineering
      and Computer Sciences

Electrical Engineering and Computer Sciences

COLLEGE OF ENGINEERING

UC Berkeley

Carat: Collaborative Energy Diagnosis for Mobile Devices

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://www.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://www.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://www.eecs.berkeley.edu/Pubs/TechRpts/2013/EECS-2013-17.html
%F Oliner:EECS-2013-17