Adam Oliner and Anand Padmanabha Iyer and Ion Stoica and 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},
    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