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figures/survey.pdf
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figures/survey.py
| ... | ... | @@ -61,6 +61,6 @@ ax.set_xlabel('\\textbf{%d Responses}' % (len(scores)), labelpad=6) |
| 61 | 61 | ax.set_ylabel('\\textbf{Score}') |
| 62 | 62 | fig.subplots_adjust(right=0.99,top=0.98,left=0.07,bottom=0.10) |
| 63 | 63 | |
| 64 | -fig.set_size_inches(6.5,2.5) | |
| 64 | +fig.set_size_inches(6.5,2.0) | |
| 65 | 65 | |
| 66 | 66 | fig.savefig('survey.pdf') | ... | ... |
paper.tex
| 1 | 1 | \input{./include/start.tex} |
| 2 | 2 | |
| 3 | 3 | \def\theconference{HotMobile'15} |
| 4 | -\def\thetitle{The Missing Numerator: Towards a Value Measure for Smartphone | |
| 4 | +\def\thetitle{The Missing Numerator: Toward a Value Measure for Smartphone | |
| 5 | 5 | Apps} |
| 6 | 6 | \def\theauthors{Anudipa Maiti and Geoffrey Challen} |
| 7 | 7 | ... | ... |
results.tex
| ... | ... | @@ -12,45 +12,24 @@ access to a representative group of participants balanced between genders and |
| 12 | 12 | across a wide variety of age brackets, making our results more |
| 13 | 13 | representative. |
| 14 | 14 | |
| 15 | -Understanding fine-grained energy consumption dynamics such as what apps ran | |
| 16 | -for how long and how much energy each interactive session consumed while | |
| 17 | -running in the background required more information than Android normally | |
| 18 | -exposes to apps. In addition, to explore our content deliver metric we also | |
| 19 | -wanted to capture information about app usage---including foreground and | |
| 20 | -background time and use of the display and audio interface---that was not | |
| 21 | -possible to measure on unmodified Android devices. | |
| 22 | - | |
| 23 | -So to collect our dataset we took advantage of \PhoneLab{}'s ability to | |
| 24 | -modify the Android platform itself. Our modification augmented the platform | |
| 25 | -to collect the fine-grained energy consumption and app behavior information | |
| 26 | -required to understand smartphone energy consumption. We instrumented the | |
| 15 | +Understanding fine-grained energy consumption dynamics required more | |
| 16 | +information than Android normally exposes to apps. In addition, to explore | |
| 17 | +components of our value measure we also wanted to capture information about | |
| 18 | +app usage---including foreground and background time and use of the display | |
| 19 | +and audio interface---that was not possible to measure on unmodified Android | |
| 20 | +devices. So to collect our dataset we took advantage of \PhoneLab{}'s ability | |
| 21 | +to modify the Android platform itself. We instrumented the | |
| 27 | 22 | \texttt{SurfaceFlinger} and \texttt{AudioFlinger} Android platform components |
| 28 | 23 | to record usage of the screen and audio, and altered the Activity Services |
| 29 | 24 | package to record energy consumption at each app transition, allowing energy |
| 30 | 25 | consumption by components such as the screen to be accurately attributed to |
| 31 | 26 | the foreground app, a feature that Android's internal battery monitoring |
| 32 | -component (the Fuel Gauge) lacks. The dataset of 67~GB of compressed log | |
| 33 | -files represents \num{6806} user days during which \num{1328}~apps were | |
| 34 | -started \num{277785} times and used for a total of \num{15224} hours of | |
| 35 | -active use. | |
| 36 | - | |
| 37 | -At \PhoneLab{} based on the analysis of data collected about foreground and | |
| 38 | -background energy consumption by applications running on the participants' | |
| 39 | -smartphones, we tried to formulate design for requirements for an energy | |
| 40 | -efficiency metric to apply to smartphone apps. First, it must scale with | |
| 41 | -usage, respecting the differences in baseline consumption between users and | |
| 42 | -the temporal variation of apps. Second, it must try to avoid targeting top | |
| 43 | -apps, even if they tend to consume a great deal of energy, as these may not | |
| 44 | -be apps that users are willing to uninstall. Finally, we use the analysis of | |
| 45 | -background energy consumption as a hint about where to start, given that | |
| 46 | -background energy consumption should match foreground usage in most cases. | |
| 47 | - | |
| 48 | -We tried characterizing app energy consumption in multiple ways: via the | |
| 49 | -total amount, by consumption rate, and scaled against foreground energy | |
| 50 | -consumption and a new content-delivery metric we design that incorporates use | |
| 51 | -of both the display and the audio device. In each case we examine the app | |
| 52 | -consumption data generated by our usage monitoring study and use each metric | |
| 53 | -to shed light on app energy consumption. | |
| 27 | +component (the Fuel Gauge) lacks. Changes were distributed to \PhoneLab{} | |
| 28 | +participants in November, 2013, via an over-the-air (OTA) platform update. | |
| 29 | +The resulting 2~month dataset of 67~GB of compressed log files represents | |
| 30 | +\num{6806} user days during which \num{1328}~apps were started \num{277785} | |
| 31 | +times and used for a total of \num{15224} hours of active use by | |
| 32 | +107~\PhoneLab{} participants. | |
| 54 | 33 | |
| 55 | 34 | \subsection{Total Consumption} |
| 56 | 35 | ... | ... |