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\section{Introduction}
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Measuring app energy consumption\footnote{\small To avoid confusion between
app and energy usage, we use \textit{consumption} exclusively when referring
to energy usage and \textit{usage} exclusively when referring to user
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interaction with apps.} on mobile devices is nearly a solved problem. This is due to
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great strides made in both generating and validating energy models that
deliver accurate runtime energy consumption
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estimates~\cite{mansdi,vedge-nsdi13,pathak2011,pathak2012,yoon} and in
accurately attributing energy consumption, even for asynchronous and shared
resources~\cite{cinder-eurosys11,osdi08-quanto}. Accurate energy models bring
us closer to the goal of effective energy management on battery-constrained
devices.
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But accurate energy measurement alone is not enough, because even
perfectly-accurate measurements of energy consumption are insufficient to
answer critical energy-related questions faced by users and developers,
including:
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\begin{itemize}
\item Which of the following two apps is more energy efficient?
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\item Will this change to an app make it more energy efficient?
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\item Is a particular app an \textit{energy virus}?
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\item How should the limited energy resources on a given app be prioritized?
\end{itemize}
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Unifying all of these questions is one missing component: a measure of app
\textit{value}, which can be used alone or combined with energy consumption
to compute energy \textit{efficiency}:
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%
\[\frac{value}{energy} \]
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Armed with a measure of value we can return to the difficult questions posed
above. By computing efficiency users can perform apples-to-apples comparisons
of apps in order to evaluate two video conferencing tools, web browsers, or
email clients. Developers can determine whether a new feature delivers value
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more or less efficiently than the rest of their app and better understand the
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differences in energy consumption across different users. Measuring value
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allows a rigorous definition of an \textit{energy virus} as an app that
delivers little or no value per joule, and for systems to reward efficient
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apps by prioritizing limited resources based on app value or energy
efficiency. After all the progress we have made in computing the
denominator---energy consumption---we believe that the search for the missing
numerator is the most important open challenge in energy management.
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Developing such a measure, however, is difficult. To be effective it must
work across almost the entire spectrum of smartphone apps, which represent an
incredible diversity of different goals, interfaces, and interaction
patterns. It must also work across a variety of different users with
different usage patterns. It must be efficient to compute, since it should
not compete for the same limited energy resources that it is intended to help
manage. Ideally it should require little to no user input, since this will
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make it burdensome and error-prone. And to make matters worse, there is no
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obvious way to measure ground truth to compare against---even in a lab.
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Despite all these challenges, however, even a semi-accurate value measure
would greatly benefit energy management on battery-constrained smartphones.
With users continuing to report battery lifetime as their top concern with
smartphones~\cite{jdpowerbatterylife-url}, we believe this effort is
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worthwhile.
In this paper we motivate the idea of a value measure and describe an early
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failure at developing one. We begin in Section~\ref{sec-usage} by describing
how useful such a measure would be while also formulating design requirements
for the value measure itself. Section~\ref{sec-measure} presents an overview
of possible inputs into such a measure and discussion of how each could be
measured and how useful it might be. In Section~\ref{sec-results} we present
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our initial effort at formulating a value measure based on content delivered through the video
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display and audio output---an attempt that we consider a failure based on the
result of a user survey, but a failure that we hope sheds some light on this
difficult challenge.
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