Commit 2bae81a539d0d69f789d4a44a5245048cdd4d60c

Authored by Geoffrey Challen
1 parent 6ccfdb28

New.

figures/survey.pdf
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figures/survey.py
@@ -61,6 +61,6 @@ ax.set_xlabel('\\textbf{%d Responses}' % (len(scores)), labelpad=6) @@ -61,6 +61,6 @@ ax.set_xlabel('\\textbf{%d Responses}' % (len(scores)), labelpad=6)
61 ax.set_ylabel('\\textbf{Score}') 61 ax.set_ylabel('\\textbf{Score}')
62 fig.subplots_adjust(right=0.99,top=0.98,left=0.07,bottom=0.10) 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 fig.savefig('survey.pdf') 66 fig.savefig('survey.pdf')
paper.tex
1 \input{./include/start.tex} 1 \input{./include/start.tex}
2 2
3 \def\theconference{HotMobile'15} 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 Apps} 5 Apps}
6 \def\theauthors{Anudipa Maiti and Geoffrey Challen} 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 @@ -12,45 +12,24 @@ access to a representative group of participants balanced between genders and
12 across a wide variety of age brackets, making our results more 12 across a wide variety of age brackets, making our results more
13 representative. 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 \texttt{SurfaceFlinger} and \texttt{AudioFlinger} Android platform components 22 \texttt{SurfaceFlinger} and \texttt{AudioFlinger} Android platform components
28 to record usage of the screen and audio, and altered the Activity Services 23 to record usage of the screen and audio, and altered the Activity Services
29 package to record energy consumption at each app transition, allowing energy 24 package to record energy consumption at each app transition, allowing energy
30 consumption by components such as the screen to be accurately attributed to 25 consumption by components such as the screen to be accurately attributed to
31 the foreground app, a feature that Android's internal battery monitoring 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 \subsection{Total Consumption} 34 \subsection{Total Consumption}
56 35