=Paper=
{{Paper
|id=Vol-2691/paper10
|storemode=property
|title=Energy Consumption Measurement Frameworks for Android OS: A Systematic Literature Review
|pdfUrl=https://ceur-ws.org/Vol-2691/paper10.pdf
|volume=Vol-2691
|authors=Vladislav Myasnikov,Stanislav Sartasov,Ilya Slesarev,Pavel Gessen
}}
==Energy Consumption Measurement Frameworks for Android OS: A Systematic Literature Review==
https://ceur-ws.org/Vol-2691/paper10.pdf
Energy Consumption Measurement Frameworks for
Android OS: A Systematic Literature Review
Vladislav Myasnikov∗ , Stanislav Sartasov† , Ilya Slesarev‡ and Pavel Gessen§
∗ Saint Petersburg State University
vladislav.myasnikov@bk.ru
† Saint Petersburg State University
stanislav.sartasov@spbu.ru
‡ Saint Petersburg State University
slesarev.pr@gmail.com
§ Saint Petersburg Polytechnical University
pashagess2@mail.ru
Abstract—In a modern world smartphones became a com- one should be able to compare application or module energy
monly used electronic devices performing numerous day-to- consumption before and after refactoring. Therefore a tool
day tasks and much more. Quick battery discharge degrades for conducting such experiments is required. Such a tool
user experience, and computationally intensive or badly written
programs are responsible for it. It is not always evident which may come as one-time testbed for a specific project or a
tool to use and how to set up an experiment to estimate energy more generic and reusable framework or utility. It should be
consumption of a specific application. For this we conducted a noted that there’s little uniformity among such tools. They
Systematic Literature Review (SLR) to list existing frameworks to differ not only in metering methodologies but in measurement
measure application power metering for Android OS, to classify results as well, i.e. some frameworks measure battery charge
the approaches used to create them and also to assess their
accuracy and experimental methodology. Our findings indicate percentage change, other calculate consumed power in watts,
that although there is a considerable amount of studies in this while another group operates in abstract units of measure.
field with various approaches, there is still a vacant place for a In order to help practitioners and engineers better under-
readily available tool, and it is difficult to compare accuracy of stand current state-of-the-art approaches to energy consump-
different frameworks. However there is a solid set of practices tion measurement and to select proper approach, technique or
and techniques for experimental setup in application energy
measurement. tool for a particular experiment, we conduct a systematic liter-
ature review (SLR) on the energy consumption measurement
I. I NTRODUCTION frameworks for mobile devices using Android OS. We selected
It is impossible to imagine a modern world without smart- this mobile platform as it is the most presented platform on
phones and other mobile devices. Their compact size com- the market compared to iOS and Windows Phone [3], and it
bined with significant computational capabilities grant them a is also open-sourced, meaning that some types of frameworks
firm position as a day-to-day informational and recreational (for example those that modify OS kernel) might be absent on
tool. At the end of 2019 a number of smartphone users is other platforms.
expected to be 3.2 billion with Android OS as a leading mobile This paper is organized as follows. In Section 2 our method-
operating system [1]. ology for SLR is described and research questions (RQs) are
As smartphones and tablets are mobile electronic devices, formulated. Section 3 contains answers for RQs. Limitations
its user experience is substantially defined by battery lifetime. of this study are reported in Section 4. A side question of
While hardware components are constantly improving with relating our proposed framework classification to a number of
power-saving electronics and more capacitous batteries being commercial profilers is addressed in Section 5. Conclusions
available to the market, inefficiently written software causes are drawn and future work is outlined in Section 6.
degradation of user experience due to elevated charge drain.
II. M ETHOD
Different applications and even different versions of the
same application consume energy differently. A school of We followed SLR guidelines by Kitchenham and Charters
thought called ”green software development” advocates a need [4] with a number of differences:
to consider energy consumption as well as performance met- • Instead of manual search process we addressed online
rics during application development [2]. A common practice is search engine. While this decision certainly affects the
energy-efficient refactoring — such a change in software code selection of studies compared to manual search, we used
that doesn’t change its end user functionality but decreases its a large number of papers to make a preliminary list and
energy footprint. introduced additional phases to study selection process,
Was a particular energy refactoring effective? How much so we think the impact of this change on SLR quality is
energy did we save by applying it? To answer these questions negligible.
Copyright© 2020 for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
• Quality assessment was done along with the data ex- the experiment itself and its conclusions are the centerpiece
traction process and in some sense — as a part of of the corresponding article. If, however, this artifact is the
it. However Kitchenham and Charters [4] allow such main discussion point for the article, we consider it to be a
methodology, noting that quality information can be used framework.
either to assist primary study selection by constructing Usually a testbed is only vaguely described, while frame-
detailed inclusion/exclusion criteria prior to the main data works are documented in detail, up to the point of open
collection activity or to assist data analysis and synthesis, source repositories, so this separation worked well. There
so it is collected along with the main data. was a handful of notable exceptions however where a study
focused on a specific energy-efficiency question also contained
A. Research Questions a detailed description of its testing environment along with the
A following list of research questions (RQs) was compiled experimental methodology and software architecture. In such
during initial literature assessment: cases a question was asked if this testbed is described in such
• RQ1: What approaches exist to estimate code fragments, a detail that it can be a subject of a framework-centered article.
methods or application energy consumption under An- In the case of positive answer a study was added to short-list.
droid OS? Additionally we do not include articles regarding smart
• RQ2: Are there open source code repositories or other watches and similar devices as its functionality is rather lim-
programming artifacts for corresponding frameworks? ited compared to the smartphone, therefore only mainstream
• RQ3: What devices are used in measurement experi- Android OS is considered as a focus of this SLR. We also
ments? exclude articles focused on Windows Phone and iOS.
• RQ4: How many frameworks specify or suggest experi- To summarize, our final criteria to include a study into a
mental methodology? short-list was as follows:
• RQ5: What is the measurement precision and what is the • Studies describing technical artifacts for other operating
base scenario to compare to? systems than mainstream Android OS are excluded.
However after preliminary data extraction we also added • Studies focused on a framework as an end result of a
the following questions due to the freqently occuring gaps in research are included.
methodology in the reviewed studies: • Studies focused on other topics but containing enough
information on a testbed or a method to make one for it
• RQ6: What units of measurements are used in experi-
to be considered alienable as a standalone framework are
ments with a particular framework and what is measured?
included.
• RQ7: How do frameworks deal with metering hardware
• All other articles are exlcuded.
frequency being considerably lower than CPU frequency?
At first stage Myasnikov, Sartasov and Gessen processed
RQ6 is answered to classify all the different ways report every article independently. An article was assigned ”+” if
measurement results. RQ7 came into attention due to the a reviewer considered it to be worthy of inclusion, ”-” if a
possibility of a child thread be entirely executed between review was negative and ”∼” if in doubt. Three pluses or two
multimeter synchronization impulses therefore its contribution pluses and a tilde meant inclusion to short-list, while an article
to the energy consumption would not be properly recorded. with three minuses was excluded from further reading. Other
B. Search Process review combinations signified review conflicts. About 45% of
studies were marked conflicts at the end of this phase.
A preliminary list of 931 studies was formed with a Google
Secondly, studies with conflicting reviews were additionally
Scholar1 resource. The query was ”android ”energy-efficiency”
reviewed by Slesarev, and each article was read in greater
framework”, therefore ”energy-efficiency” was required to
detail and discussed until a consensus was formed among the
appear as a phrase in a study text.
researchers. At the end of this phase 51 articles were added
C. Study Selection to short list.
At first inclusion criteria were rather simple — include Some studies in the result list deserve specific mention.
studies about energy measuring frameworks, exclude all others Although WattsOn framework [5] targets Windows Phone, it
— but it was quickly became apparent they were not sufficient. was included because authors claim their approach is portable
Indeed some studies were discussing a software and hardware to Android OS. 3 additional articles were added to the short
complex or purely software tool for measuring energy expen- list manually as they were not a part of original study list:
• Yoon, Kim et al. [6]
diture which was generic enough to be called a framework.
• Zhang, Tiwana et al. [7]
However many articles contained a description of a testbed —
• Li and Gallagher [8]
a testing environment that measures specific program energy
consumption in one way or another. In a considerable amount Those studies became known either due to past literature
of cases the distinction between a testbed and a framework was reviews in similar topics or during review process when they
blurry. We consider a programming artifact to be a testbed if were referenced in multiple other articles. They were also
reviewed independently by three researchers and got 3 pluses
1 https://scholar.google.com for review.
Third stage of study selection was done along with data 4) How is raw metering data downloaded or otherwise
extraction. 6 studies in fact were written about a different obtained for processing?
subject while looking like a framework article at a top level, 5) What are the raw data transformations used to
so our short-list contained 48 articles. present experiment outcome?
However at this time we’ve found out that in some cases 6) Is there an open source code for a framework?
multiple studies were describing the same framework under • Experimental methodology questions:
different angles or at different points of development cycles. 1) What are the preliminary actions to undertake on a
It is a common situation for an ongoing research project. As test device before starting measurements? Examples
the focus of our SLR is the frameworks and not the articles include turning off Wi-Fi, stopping applications,
per se, if multiple studies were written by the same or similar charging up to 100%, controlling device temperature
collective of authors from the same institution and described etc.
a similarly named frameworks based on a similar principles, 2) Is measurement accuracy estimated? If yes, what is
after discussion and consensus between researchers they were the base of comparison?
considered to be written about a single framework, and data 3) Does framework experimental methodology con-
extraction results were merged for these studies. In particular sider frequency of metering tools? What is the
we grouped the articles concerning the following frameworks: frequency of a tools used by authors?
• JouleUnit by Wilke et al. [9], [10] If an article didn’t contain an information required to answer
• Greendroid by Couto et al. [11], [12], [13] the question or if a question was not applicable to a specific
• PETrA by Di Nucci, Palomba et al. [14], [15] article, it was also noted.
• Aggarwal et al. [16] and Feghi [17]
A pool of works was distributed along Myasnikov, Sle-
• Li and Gallagher [18], [8]
sarev and Sartasov, and data from each study was extracted
• Ahmad et al. [19], [20]
independently. Quality control was applied selectively if there
In the end, out of 931 studies aggregated automatically and was a misunderstanding between team members regarding
3 papers added manually our short-list contained 48 articles particular data extraction result for a specific study. In this
summarized into 41 frameworks. case data extraction was repeated collectively until consensus
was formed.
D. Data Extraction Data extraction results were aggregated in an online docu-
To answer the stated RQs we answered the following data ment2 . We were able to extract relevant information for each
extraction questions for each included study or study group: question for each framework in the list.
• Conceptual questions: E. Quality assessment
1) What is the method of reading current battery charge Generally SLRs are accompanied with a quality assessment
level or energy expenditure? Examples include ex- procedure concerning every included article. As we’re more
ternal multimeter, internal sensor, Android OS API interested in frameworks than in individual articles themselves,
etc. we decided to modify this process by assessing the quality
2) How large is the part of an application to be of individual studies if they were not grouped with other
profiled? For example, one may measure energy publications, otherewise we assessed a group of articles as a
consumption of a single line of code, a code block, whole. Therefore if a framework is described in two articles,
a method, a unit-test, an application or all currently we assess the quality from both of them simultaneously.
running applications. To assess the quality we formulate the following questions:
3) What is the end result of a measurement experi-
ment? 1) Is there an open-source code for the framework?
4) What are the units of measurement utilized in a 2) Is framework measurement approach described?
framework (i.e. watts, joules, relative units)? 3) Is experimental methodology described?
4) Is framework accuracy assessed?
• Technical questions:
These questions intentionally overlap with our RQs, as
1) Is the application code executed on a smartphone
this information is in our opinion essential if a reader wants
or special test device and what is its Android OS
to understand a proposed framework. They were scored as
version?
follows:
2) What kind of code instrumentation is used if any?
• Question 1: yes (Y) if a repository can be found, partly
In this context we define code instrumentation as
addition of framework-specific subprograms (i.e. for (P) if a program using a framework can be found, but not
method start and end logging) to the target source a source code, no (N) otherwise. Even if an article states
code. 2 https://docs.google.com/spreadsheets/d/
3) How is energy consumption measurement started 17D1ArPavFQaFPGnU-OI3r8x1QoboWOoEa-rHR-9U98I/edit?
from a technical standpoint? usp=sharing
a number of lines of code for a described framework or are generated — power readings and application execu-
gives code fragments or algorithms, it is still a no. tion trace as in Couto et al. [11]. In this case system
• Question 2: yes (Y) if a framework approach is described clocks on smartphone and multimeter or controlling PC
in great detail, and entire thought process from principles should be synchronized before the start of experiments.
to implementation can be tracked, partly (P) if only a Power readings can then be aligned with execution trace
general description of framework principles is present, and provide an insight which method or piece of code
no (N) otherwise. is responsible for high energy usage. As instrumentation
• Question 3: yes (Y) if a methodology is described in great introduces additional code to be executed, its energy
detail and limits and bounds are stated, partly (P) if only a overhead should also be estimated and subtracted from
broad description of measurement methodology is given, final readings.
no (N) otherwise. • Internal meter: This approach utilizes internal power
• Question 4: yes (Y) if an experiment and a base case meters installed on the smartphone by manufacturer and
are described, partly (P) if some consideration is given Android OS API to access them. While generally such
to accuracy, but not a thorough one, no (N) otherwise. a tool can get a good power consumption estimate for
The scoring procedure was Y = 1, P = 0.5, N = 0. Article a smartphone as a whole, under specific experimental
quality was obtained as a sum of individual question scores. conditions it can be trimmed down to a level of a
Results are given in Table I. single application in question. Because smartphone and
power sensor are using the same system clock, there are
III. D ISCUSSION OF RESEARCH QUESTIONS no issues with clock synchronization. Power readings
requests may be integrated into instrumentation code.
This sections contains our findings in relation to the specific
Energy consumption is estimated in the same way as
research questions as stated above.
before.
A. RQ1: What approaches exist to estimate code fragments, Indirect measurement approach (or model-based approach)
methods or application energy consumption under Android means that profiling software is aggregating some information
OS? regarding code execution and relates it to energy consumption
using some mathematical model. Frameworks utilizing this
We can classify different approaches for software energy
approach operate in two phases: model calibration and energy
consumption estimation into two bins: if power measurement
estimation. At first stage model coefficients are determined
is direct or indirect.
or tuned with preliminary experiments or reference data. It is
Direct measurement approach means that some metering
an important step not only for different smartphone models,
agent is directly measuring voltage, current or even power.
but for different devices of a same model as well [21]. After
Subclasses of this approach are as follows:
a model is tailored for a device, actual energy metering
• External meter: External digital multimeter is con- experiments may be commenced.
nected to battery contacts of smart device. Sometimes Subclasses are formed based on the type of aggregated
to compensate voltage drop in Li-ion batteries during information used in the model:
discharge and therefore normalize power readings an
• Working time model: For this group information regard-
external power generator working as a constant voltage
ing working time of various smartphone systems is aggre-
source is connected to testing device instead of default
gated. Various devices may consume energy differently
battery. Framework launches an application in question or
under different modes of operation. For example, Wi-Fi
a unit test alongside power measurement. In the end total
module consumes different amounts of energy when idle,
power consumption is estimated by linear interpolation
when looking for a network and when transferring data
as
over a network. Therefore energy consumption value is
X
Nread −1
Ii+1 + Ii calculated as
E= Ui × × (ti+1 − ti ) (1)
i=1
2 X X
Ndev N Pi
E= Pij × tij (2)
where E is total energy, Nread is number of power
i=1 j=1
readings, Ui is ith voltage read, Ii is ith current read,
ti is time of ith read. Some multimeters write the time where E is total energy, Ndev is a number of tracked
of a read directly, while for other ti+1 − ti is an inverse devices on a smartphone, N Pi is a number of different
of a multimeter frequency. Step interpolation is also used power characteristics of ith device, Pij is a jth power
in some works. characteristic of ith device, tij is active time for ith device
If a more detailed report is needed, for example at a operating under Pij power characteristic.
level of methods or code blocks, then the source code Calibration phase consists of experiments for determining
is instrumented with additional logging instructions for power profiles for individual components using one of the
method or code beginning and end, and two data traces direct measurement approaches: both external and inter-
TABLE I
S TUDIES QUALITY
Q1 Q2 Q3 Q4 Quality
Studies Repository Approach Methodology Experiment score
Zhang et al. [7] Y Y Y Y 4
Hindle et al. [21] Y Y Y P 3,5
Di Nucci, Palomba et al. [14], [15] P Y Y Y 3,5
Tuysuz, Uçan and Trestian [22] P Y Y Y 3,5
Wilke et al. [9], [10] Y Y P P 3
Hao et al. [23] N Y Y Y 3
Couto et al. [11], [12], [13] Y Y Y N 3
Bareth [24] N Y Y Y 3
Kamiyama, Inamura and Ohta [25] N Y Y Y 3
Saksonov [26] N Y Y Y 3
Sahar, Bangash and Beg[27] Y Y P P 3
Ahmad et al. [19], [20] N Y Y Y 3
Pandiyan and Wu [28] N Y Y Y 3
Huang et al.[29] N Y Y Y 3
Oliveira et al.[30] N Y Y P 2,5
Aggarwal et al.[16], Feghi [17] N Y P Y 2,5
Westfield and Gopalan [31] P Y N Y 2,5
Dolezal and Becvar [32] N Y Y P 2,5
Hu et al. [33] N Y P Y 2,5
Yoon, Kim et al. [6] N Y P Y 2,5
Larsson and Stigelid [34] N Y P Y 2,5
Fischer, Brisolara and Mattos [35] N Y P Y 2,5
Lee, Yoon and Cha [36] N Y P P 2
Mittal, Kansal and Chandra [5] N Y N Y 2
Chen and Zong [37] N Y P P 2
Hung et al. [38] N Y N Y 2
Carette et al. [39] N Y Y N 2
Kapetanakis and Panagiotakis [40] N Y P P 2
Dong, Lan and Zhong [41] N Y P P 2
Lee et al. [42] N P P Y 2
Chung, Lin and King [43] N Y P N 1,5
Shin et al. [44] N P N Y 1,5
Jung, Kim and Cha [45] N Y P N 1,5
Tsao et al. [46] N Y N P 1,5
Metri [47] N Y P N 1,5
Gao et al.[48] N P P P 1,5
Banerjee et al. [49] N Y N P 1,5
Li and Gallagher [18], [8] N Y P N 1,5
Li et al. [50] N Y P N 1,5
Walcott-Justice [51] N Y N N 1
Kim, Kyong and Lim[52] N Y N N 1
nal meters are conceptually suitable. Linear regression method calls, floating-point operations etc. Energy con-
is used to extract power coefficients from experimental sumption is calculated as
data. As an alternative some frameworks utilize Android
power profile data [53]. X
Ninstr
Energy estimation is assessed by measuring active device
E= Pi × n i (3)
time during experimental code execution. Different com-
ponents have different ways for measuring their active i=1
time, i.e. CPU stores information about its time in differ-
ent power states in proc folder, while Wi-Fi generates where E is total energy, Ninstr is number of different
system events when it transitions from one power state instruction types in a model, Pi is power consumption of
to another. a single instruction of ith type, ni is number of ith type
Additional estimations may also be incorporated into such instructions in the code.
model, for example, corrections for battery discharge rate Model calibration is done by measuring power consump-
[44]. tion of each instruction type in a synthetic tests using
• Instruction energy model: This group of frameworks direct approach.
considers energy consumptions of various code instruc- Total energy is calculated from instruction statistics of a
tion types — conditional statements, loop controls, specific code execution trace. It should be noted that it
is not required to launch test code under Android OS if
no specific Android API is invoked. Instruction statistics and GreenDroid (January 2019), so we may call them semi-
may be aggregated in any suitable environment. abandoned. In our experience lack of recent commits usually
• Method/API call energy model: This approach is similar indicates either a research project being finished or it was
to the previous one, but instead of a power profile for a abandoned for some reason. Additionally, none of the projects
single instruction energy consumption of a system or API can be built out of the box, and build instructions are not
call, or framework method is calculated. Models under provided in details. As stated before, PowerTutor was used in
this approach are created under assumption that most a number of other studies when it was supported, but it is not
of the time and energy is spent outside of application surprising that a significant amount of more recent testbeds
code, and therefore good estimation of application energy we’ve seen in studies during study selection phase are based
consumption can be obtained by analyzing its API usage. on industrial grade Monsoon power monitor [54]3 .
We assign each of the listed frameworks to its approach in We conclude that studied frameworks rarely provide their
the Table II. code in open source repositories. Those who do are not easy to
launch. What’s worse, not all of the frameworks are available
TABLE II to the practinioners even in the form of proprietary software.
A PPROACHES FOR ENERGY CONSUMPTION ESTIMATION
C. RQ3: What devices are used in measurement experiments?
Direct measurement
The number of devices running Android OS is hard to
External meter [43], [9], [21], [46], [39],
[50], [52], [34], [41] reliably count: from the very first HTC Dream model line to
Internal meter [9], [45], [51], [37], [40], [34], [35] modern foldable smartphones as well as tablets. Android OS
Indirect measurement is also continues to being developed with a major release by
Working time model [44], [11], [14], [5], [47], [36], [32], Google every year. For example Android Q version released
[48], [25], [24], [49], [38], [26], [6],
[7], [27], [52], [22], [29], [42] in 2019 supports foldable smartphones and 5G-devices [55].
Instruction energy model [23], [18], [19] Additionally, Android API is not static, although generally
Method/API call energy model [30], [16], [31], [33] an application is forward compatible with a newer Android
OS version [56]. Regardless, application well-behaving on a
specific version of Android platform might be glitching on
B. RQ2: Are there open source code repositories or other another version due to the API change. With such variety of
programming artifacts for corresponding frameworks? devices and versions it is important to understand what range
This research question intentionally overlaps with the Ques- of devices and Android OS versions are covered by existing
tion 1 in Quality Assessment section. Energy consumption energy metering frameworks.
measurement frameworks are practical and generic tools by We obtained the following distribution of target devices
definition, therefore it is reasonable to expect its source code from the listed frameworks:
available for reuse. As an alternative a metering application • A special testbed is used in ∼17% studies. In this context
may also be sufficient. a testbed is a special platform running Android OS which
Among the 41 included frameworks the results are as is not a smartphone or a tablet, although it might be
follows: functionally similar. For example, Odroid-A platform has
• 5 frameworks have their source code available in repos- a set of functions similar to Samsung Galaxy S2 [44].
itory: JouleUnit [9], [10], GreenMiner [21], GreenDroid • An emulator is used in ∼12% studies. Emulator allows
[11], [12], [13], EnSights [27], PowerTutor [7] to run applications without necessity to procure a real
• 3 frameworks are openly presented as a ready application: device. However emulator is not a complete substitute
PowerTutor [7], PETrA [14], [15], ”PowerProfiler & for a smartphone or a tablet, in particular application
Energy-aware Network Selection” application in Google performance might be worse in emulated environments
Play by Uçan, Tuysuz and Trestian [22]. A web-site for thus negatively affecting measurement accuracy. Another
another framework, Orka [31], is available in Imperial limiting factor is a number of devices available for
College of London intranet. emulation, for example, only one model of the Nexus 7
• 3 studies include code samples or statistics in the text: tablet is emulated in Westfield and Gopalan [31]. Hence
Sema [35], Huang et al. [29], Kapetanakis and Panagio- a range of available emulated devices is significantly
takis [40]. limited compared to commercially available smartphone
Note that PowerTutor [7] is marked as having both a source and tablet ranges, albeit this approach is significantly
code and an application available. Non-mentioned studies cheaper. WattsOn framework [5] is also included into this
don’t refer to the source code. 3 Monsoon power monitor is a high frequency multimeter to be connected
It should be noted separately that all of the frameworks between smart device battery and electronics. It is capable to upload mea-
with their code available are not kept up to date. PowerTutor surement results to a PC. While it was designed with smart device energy
and JouleUnit were abandoned over 5 years ago. At the measurements in mind, it is just that - a high-quality multimeter, which can
be used in a variety of scenarios not limited to Android power profiling.
time of writing (January 2020) most recent commits are Therefore we do not consider Monsoon to be a metering framework by itself,
found in GreenMiner (May 2018), EnSights (August 2018) but it can lie in a foundation of one.
category as its authors claim it to be portable to Android D. RQ4: How many frameworks specify or suggest experi-
emulators. mental methodology?
• Real devices such as smartphones and tablets are used in It is not enough to write code, launch a framework to
the other studies. Smartphones are much more prevalent estimate its energy consumption and get results. Experimental
than tablets — the latter are experimented upon in a single methodology is as important as framework itself. By method-
study [51]. A number of devices used for experiments ology we mean a set of rules, procedures and techniques aimed
varies from 1 to 3. to decrease, eliminate or otherwise take control of external and
To evaluate a potential range of devices for a particular internal influences, which are not independent variables of the
framework one can try different methods. Firstly, this range experiment, on experimental outcome. In a way, methodology
may be estimated using target Android OS version used in the defines a context of framework usage as authors see it, and
experiments described in a selected study. Then the article is non-stringent approach to experimental setup and outcome
considered applicable for devices with the specified version interpretation leads to innacurate or outright wrong results.
and (with a caution) higher due to forward compatibility [56]. Ill-thought procedures or rules or lack of thereof in a study is
Such an assessment, however, is rather imprecise as there are an alarming signal.
no guarantee that experiments were conducted on a minimally In this regard studies in our list generally take experimental
available version. Secondly, an estimate of supported Android methodology into consideration with only 6 articles not men-
versions can be based on measurement tools being used in tioning it. We extracted this information from the others and
experiments if they are specified in a study. In general this aggregated them into following groups:
assessment is more precise than the first. Thirdly, it’s worth • Testing environment setup.
to take into account technical restrictions imposed by the • Repeated launch of tests, benchmarks etc.
framework itself if they are mentioned. • Overhead estimation.
With the listed frameworks we obtained the following Table III contains study distribution into each group.
results: A smartphone or a tablet is a complex device with numerous
settings. Using it as a testing device requires to pay attention
• In the articles where it was possible to draw conclusions to its configuration. To a lesser extent this is also true for
on Android version experiments are conducted on An- emulator. Such parameters include the following:
droid OS versions 2 to 5, so approximately 90% of all
1) Reducing activity of background processes (both system
devices are supported [57].
and non-system) [23], [51], [39], [27], [40], [28].
• Some restrictions are stated explicitly, for example, de-
2) Increasing scheduling priority of the subject application
vice power profile must be uploaded to the framework
[23].
server prior to experiments [19]. However, in some
3) Decresing screen brightness or switching it off com-
studies its restrictions are implied, for example, Android
pletely [21], [25], [39], [27], [28], [22], [29].
Power Profiler tool used for energy metering is available
4) Switching off all unnecessary modules (Wi-Fi, 3G, GPS,
only for Android 5.0+ devices [30], while Trepn Profiler
accelerometer, proximity etc.) [39], [26], [20], [40], [28].
is available for a limited range of devices for Android
5) Charging battery for the same level [24], [39], [22].
4.0+ [51], [47], [33].
6) Shutting down for cooling down the battery heat [39].
• At least 12 frameworks require root access for the device,
7) Warmup execution [30].
mainly to use additional API. It might be unacceptable
8) Time waiting before beginning a new test in order to let
for some users as rooted devices void the manufacturer’s
background processes calm down [32], [27].
warranty. Additionally there is a risk of disrupting proper
9) Application reinstalling [21].
OS functioning under root privileges compared to a non-
10) Application cache erasing [14], [27].
root user.
11) Rebooting the device [26].
• In 7 of those 12 frameworks it is not enough to just
12) Factory data reset [24], [26].
have a root access, as one must also integrate a kernel
module into the OS [45], [46], [36], [6], [42] or otherwise If left unchecked these factors introduce additional energy
modify an existing Android framework [48], [39]. Such drain and thus skew measurement results and decrease re-
preparatory actions impose a considerably higher entry peatability, therefore a proper setup is required. About 40%
barrier than simply obtaining root access rights for the of the listed frameworks take it into consideration one way or
device. another.
A framework itself might introduce additional energy con-
In the end the range of supported devices is wide despite sumption. For example, code instrumentation is required to
some of the frameworks targeting testbeds or emulators. While obtain execution trace, but as it is implemented in the form
individual restrictions make some frameworks harder to set up of additional code, it inevitably introduces energy overhead.
than others, we conclude that development tools versioning Aggregating system data while executing a test suite is another
and framework accessibility in general (see RQ2) is more reason behind elevated energy consumption during measure-
limiting for their usage than device range. ment. To alleviate this problem one should estimate this over-
head and subtract it from measurement results. In particular setup or even impossible. This accuracy estimation technique
code instrumentation is estimated by running all the tracking is used both for direct measurement and model-based.
code from a selected trace without the original test code and Another way to assess accuracy of a newer framework is to
measuring its energy footprint. Around 50% of all studies compare it with an already existing tool which is considered
address this issue, with some studies only stating measures to be a baseline. Once again accuracy is a relative difference
to overcome it [37], [35], while other actually estimate its between frameworks. Several studies match themselves against
effect [25], [6]. a PowerTutor framework [31], [22], while one of the studies
A notable number of studies — about 37% — repeat compares the proposed framework with Appscope [42]. While
their measurements multiple times to get an average or mean such comparisons allow researchers to evaluate measurement
value of consumed energy. In this way authors reduce random accuracy with respect to the few existing solutions, lack of
metering equipment jittering and OS scheduling impact on homogenity in baseline framework selection indicates that
measurement results [9], [30], [17], [29]. there is no universally accepted accuracy standard.
Chung, Lin and King [43] and Jung, Kim and Cha [45] As stated above, several studies fell into several groups at
should be mentioned separately. Those studies specify amount the same time. For example, in some studies a comparison is
of time for the experiment to last to produce adequate results. made both with real measurements and with other frameworks
However they do not specify reasoning behind selecting par- [19], [35], [42]. One interesting take on this approach is
ticular values, so we do make another group for them. described in Saksonov [26] where a derivable energy profile
Overall, we conclude that collectively studies in our list con- is compared to the default Android Power Profile4 and direct
tain enough guidelines for a practitioner to properly conduct measurements.
energy consumption estimation process. Based on this we conclude that such two-factor estimation
process provides better context of actual framework accuracy.
TABLE III Complete lack of accuracy assessment strongly indicates a
E XPERIMENTAL METHODOLOGY GROUPS
study of a subpar quality.
Group Number of papers
Overhead estimation 20 TABLE IV
Environment setup 16 M EASUREMENT ACCURACY EVALUATION GROUPS
Relaunching 15
None 6 Comparison group Number of papers
Real measurements 18
Another tool (framework, power model) 13
E. RQ5: What is the measurement precision and what is the None 17
base scenario to compare to?
Measurement accuracy is one of the key characteristics of
F. RQ6: What units of measurements are used in experiments
energy profiling framework. Thus a question of framework
with a particular framework and what is measured?
accuracy might be expected to be answered in a corresponding
study. Our review shows that accuracy estimation of some sort Code energy consumption measure using a framework can
was carried out approximately in 61% works, which can be be expressed in different terms. For example, recall equation
divided by a type of estimation into the following groups: 2. With the direct approach total energy is the most obvious
• Comparison with direct measurement.
way to estimate consumed energy. However when voltage is
• Comparison with another tool (frameworks, power mod-
constant amount of consumed Ampere-hours is as informative
els). as total energy. Likewise, if power reads are regular their
amount and distribution contain all necessary information.
Study distribution into each group is shown in Table IV.
Similar idea is also frequently used in model-based approach
Some studies fell into several groups at the same time.
for estimating CPU power usage [53]. Moreover, the same
If a study compares framework accuracy with a baseline in
value can be presented with varying degree of accuracy. For
the form of external power or current metering device data, we
example, under some experimental setups energy may be
correspond it to comparison with direct measurement group.
better measured in µJ than in mJ or J for a more conclusive
Such a device can be an ordinary multimeter [20] or a special
results. Therefore lack of homogenity in result presentation
equipment like, for example, Monsoon [14], [5], [6]. Accuracy
among the listed frameworks can be explained.
is estimated as a relative difference between framework and
Table V aggregates the characteristics measured in the listed
equipment data. Note that baseline scenario organization for
studies and distributions of the corresponding measurement
this group is similar to the direct measurement approach in
units. Among absolute characteristics power and energy were
framework building (see RQ1). A significant limitation of
most often measured with their values presented in mW and J,
this technique is also the similar — usage of an external
respectively, current, voltage and electric charge are estimated
meter comes at a price of estimating only the total power
less often. In specific cases higher accuracy measurement was
consumption of a test device without breaking it down by
hardware components, and therefore intercomponent compar- 4 An XML file detailing power characteristics of smartphone components
ative analysis may be limited and require specific experimantal provided by its manufacturer.
used with µW for power [47], and nJ for energy [28]. However, 10 KHz. As amperemeters average variable current between
some studies are not precise in terminology, in particular synchronization impulses, power spikes of higher frequencies
difference in battery charge is also labeled as consumed energy may be smoothed out to levels of measurement errors and pass
[44]. undetected. Therefore it is important to understand if studies
It is not uncommon to estimate specific energy calculated dealing with direct measurement approach frameworks or di-
per unit of device operation. Examples include instructions rect measurement baseline comparison alter their methodology
[43], MHz of processor, screen inch or dB of speakers [32], to address this issue.
bits of transmitted traffic [29]. We’ve identified 10 frameworks that in one way or another
Relative quantities are also encountered in the listed studies, acknowledge it:
although rarely. Banerjee et al. [49] introduce a measure • Mittal et al. [5], Dolezal and Becvar [32], Saksonov [26],
reflecting the energy efficiency of the application for a certain Pandiyan and Wu [28] turn off DVFS completely or set it
time period. Dong, Lan and Zhong [41] measure energy to a more controlled mode of operation, so CPU current
consumption in percentages relative to the total energy con- draw is at least more predictable and consistent.
sumption of the system. • Hung et al. [38], Yoon et al. [6], Zhang et al. [7] include
Jung, Kim and Cha [45] organize their measuring process CPU frequency data as an additional input for total energy
using both absolute and relative quantities. The percentage consumption estimation.
of screen brightness, the percentage of processor utilization • Couto et al. [11], Li and Gallagher [18] adjust test running
and its frequency, the strength of Wi-Fi and 3G signals in time to be comparable to metering hardware frequency.
dBm were measured along with many other characteristics of • Larsson and Stigelid [34] adjust metering frequency to
a device energy behavior. test running time. We include this study with caution
Overall, about 65% of the listed studies report a single though, as authors access Android API and not an internal
quantity, in other studies two or more are reported. meter itself, therefore frequency of API calls might be
unrelated to frequency of internal meter readings.
TABLE V We conclude that the issue is not widely acknowledged, but
M EASURED CHARACTERISTICS
it can be alleviated at least to an extent both by configuring
Quantity Number of papers Accuracy distribution the test device and by properly setting up experiment. We
Power 21 mW — 14, W — 6, µW — 1 suggest that testing those measures for adequacy in a real-
Energy 22 J — 12, mJ — 7, life multicore scenario with threads created and destroyed
kJ — 1, µJ — 1, nJ — 1
Current 3 A — 2, mA — 1
regularly and at high frequency is an open challenge.
Voltage 2 V — 1, mV — 1
IV. S TUDY LIMITATIONS
Electric charge 3 mAh — 2, mAms — 1
One of the main issues when conducting SLR is to find all
relevant studies to include. We used automatic search system
G. RQ7: How do frameworks deal with metering hardware (Google Scholar) and therefore our search is as efficient and
frequency being considerably lower than CPU frequency? thorough as this service is. While the query term ”energy
This RQ was included at later stages of data extraction efficiency” allowed us to find a good amount of relevant
process as a reaction to the following phenomena. studies, we admit that in some of the additionally included
Modern CPUs including systems on a chip like smartphones articles it was absent. There is a probability that we missed
or tablets operate at frequencies of several GHz. While the some other relevant studies not using this term in the text.
operating voltage for a CPU is more or less constant5 , the Additionally several months passed since our original search
current it draws can vary tremendously. When multiple cores and article publication, so there is also a possibility of some
are working, it is higher than in the case of a single core newer articles published after summer 2019 being missed in
working. Entire CPU or even each of its cores may potentially our study.
work at different frequencies with different corresponding cur- It is also possible that we missed to group some articles,
rent, changing dynamically — this process is called Dynamic although we don’t think there’s a high probability to it. We
Voltage Frequency Scaling (DVFS). Such changes in drawn assume our merge process was reliable enough as an idea to
current may appear at frequencies of at least MHz range. merge [16] and [17] into a single group came initially from
On the contrary metering hardware in the listed works their common institution of origin despite completely different
operates at considerably lower frequencies. Maximum fre- sets of authors.
quency of 100 KHz is found in Wilke et al. [9], [10], but Another limitation was a selective quality control of a
the bulk of studies uses multimeters operating way below data extraction process. In some cases particular studies were
handled by a single researcher with extracted data being well-
5 For Li-ion and Li-pol batteries commonly used in modern smartphones written and not raising questions, so other researchers relied
voltage drops slightly between 90% and 20% of charge, but it is largely on this data instead of original study. Such process could
insignificant drop. Electronic devices can in fact safely operate in a range of
voltages, so only a significant drop in voltage after 20% of charge results in introduce errors in extracted data due to misunderstanding of
functional degrade. a study.
V. C LASSIFICATION OF SOME COMMERCIAL POWER circumstances accuracy comparison between approaches is
PROFILERS currently extremely hard to undertake.
As we prepared this article, a frequently asked question On a brighter side, these frameworks are reported to be
was how to relate results obtained from SLR to a number used. To our knowledge only a PowerTutor framework [7] was
of commercially available power profilers used in practical able to evolve further and became a broadly used application,
Android development. While comparative accuracy overview while some of the frameworks were documented to be used
is out of the scope of this article, we think it is useful to apply internally as a tools for higher level research projects [21],
framework classification obtained in RQ1 to those profilers. [11].
Our goal here is to help practitioners better understand their A number of devices supported by existing frameworks is
intended use cases. The list is definitely not exhaustive, but large, so even if a practitioner would write a framework itself,
those were the most frequently mentioned profilers. test device itself is not a limiting factor. A set of method-
Trepn Power Profiler [58] is a tool developed by Qualcomm ological practices and techniques was established to improve
which is no longer supported. It is a direct measurement measurement accuracy, and even relatively low frequency of
framework using internal meter, and therefore its energy hardware equipment compared to measured device can be
consumption data is as accurate as the meter itself is. This offset by smart experimental design, although we think that
issue was acknowledged by developers and a list of accurate there are still open questions in this area.
devices was compiled [59]. We’ve also shown that our proposed classification is useful
BatteryHistorian [60] is a tool for background system in- to analyze possible use cases for commercially available power
formation aggregation in Android OS. Not only it provides profilers targeting Android OS.
power drain information but also statistics of runtime system Results of our study affected design decisions and helped to
events like Wi-Fi scans or wakelocks. Being classified as a continue developent of our own tool for energy consumption
direct measurement framework with internal meter, it displays metering for Android OS - a working time model-based
energy consumption as percentage of battery charge which is Navitas framework6 . From the beginning it was conceived as
a victim to battery degradation. A noticable drop in battery an open-sourced framework which could be used in different
charge usually requires a significant amount of computations scenarios, and one of possible applications we’re developing is
and/or peripherals work, therefore this value is not suitable for a plugin for Android Studio IDE. Its development, evaluation
short time experiments, for example, those that run only for and application for energy refactorings is a focus for future
several seconds. work.
Android Studio Energy Profiler [61] shows energy con-
sumption as a real-time graph in relative units. Regretfully, ACKNOWLEDGMENT
there is virtually no documentation on the model itself, but Authors would like to thank Lanit-Tercom company for
after analyzing the information obtained from various sources hosting our research project as part of its Summer School’19.
(such as StackOverflow [62]) and experimenting with tool
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