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6 PERVASIVE computing Published by the IEEE CS n 1536-1268/16/$33.00 © 2016 IEEE
SmartphonesEditor: Nayeem Islam n Qualcomm n [email protected]
E nergy efficiency remains a high priority for current smartphone
operating systems and is increasingly a priority for applications. Consequently, modern smartphones currently incor-porate several mechanisms to optimize battery consumption. On the operating system level, on-demand resource-opti-mization strategies are used to reduce battery consumption.1 However, the effectiveness of these policies is highly dependent on the user context, and there often are complex interdepen-dencies that make it difficult to deter-mine the optimal policy for a given situation. Ensuring the effectiveness of these policies requires fine-grained models to characterize how different contexts and device features influence power consumption.
The alternative to automated poli-cies is to give users control over spe-cific system settings, such as whether to prefer Wi-Fi or cellular networks or when to turn off the screen after inac-tivity. Indeed, contemporary smart-phones have interfaces that allow these kinds of operations with little effort. With an increasing number of user-controllable system settings, keeping track of each setting’s energy impact becomes unmanageable. To illustrate this problem, Figure 1 depicts some of the system settings available on the Samsung Galaxy S4. Over 20 different settings are visible, most of which have a significant effect on energy. To fully
understand the impact of all of the set-tings would require a considerable amount of learning.
Furthermore, the total energy con-sumption of the smartphone is not simply the sum of the energy impacts of enabled system settings. Some subsystems, such as Bluetooth and Wi-Fi or accelerome-ter and gyroscope, are integrated on the same chip and can be enabled simultane-ously resulting in much less than the sum of their combined individually measured energy impacts. Enabling users to make optimal decisions would thus require fine-grained information about how different settings influence the overall battery consumption of the device in a given setting.
Here, we summarize our recent work2,3 in developing a novel solution for characterizing the energy consump-tion of system settings and other con-textual factors using crowdsourced bat-tery discharge measurements.
EnErgy ProfIlIng of MobIlE DEvICESMobile energy profiling refers to the process of characterizing the energy consumption of a mobile device, includ-ing installed applications, hardware, and other subsystem components. Energy profiling is typically carried out by constructing a statistical model that can correlate specific system states with energy consumption patterns. The mea-surements for constructing these mod-
els can be gathered either using special-ized hardware, such as the Monsoon power monitor or BattOr,4 or through the battery interface of the device.5,6
Energy profiling of mobile devices can be categorized into two different approaches based on the target of the modeling process.
Sensor-Level ModelsIn sensor-level models, the goal is to characterize the energy consumption of a set of sensors. Overall energy con-sumption can then be estimated by combining the model with usage statis-tics of different sensors.
One of the first examples of a sensor-level model is the work of Andrew Rice and Simon Hay,7 who examine fine-grained hardware power measurements and their causes and attribute energy drain to the networking stack version, packet size, and Wi-Fi handshake behav-ior. Immanuel Koenig, Abdul Qudoos Memon, and Klaus David measure power consumption of different sensors using a hardware power monitor.8 Mik-kel Baun Kjærgaard and his colleagues use conditional functions, manually con-structed from empirical power measure-ments, to represent power consumption of different sensors.9
Contrary to our work, these approaches can’t capture interdepen-dencies in the energy consumption of different components, resulting in over-estimates of overall consumption.
Where Has My Battery Gone?A novel Crowdsourced Solution for Characterizing Energy ConsumptionElla Peltonen, Eemil Lagerspetz, Petteri Nurmi, and Sasu Tarkoma, University of Helsinki
JANUARY–MARCH 2016 PERVASIVE computing 7
Device-Level ModelsInstead of focusing on individual sub-systems, device-level models character-ize the overall battery consumption of a device. These models consider how application usage patterns, workload, and other system-level parameters, such as screen brightness and data transfer rate, influence battery discharge. The estimated discharge rate of the device can then be used to predict the remain-ing lifetime of the device’s battery.
Ye Wen, Rich Wolski, and Chandra Krintz propose constructing a refer-ence curve of the battery consumption under different workloads and learning a regression model that can be used to compare current discharge with esti-mated consumption.10 Joon-Myung Kang and his colleagues predict dis-charge behavior from application usage patterns.11 Other examples include the works of Xia Zhao and her colleagues12 and Nishkam Ravi and his colleagues.13 Hossein Falaki and his colleagues14 analyze smartphone usage patterns,
revealing significant variation across users and that personalized application usage models are essential for accurate prediction of battery drain.
In contrast to our approach, which can capture how changes in the device state influence battery discharge, these approaches can only provide aggregate-level information of power usage.
CrowDSourCED EnErgy MoDElSWe consider crowdsourced measure-ments collected using the Carat appli-cation, which is available from both the Apple App Store (https://itunes.apple.com/us/app/carat/id504771500) and Google Play (https://play.google.com/store/apps/details?id=edu.berkeley.cs.amplab.carat.android). At the time of writing, we had measurements from over 725,000 distinct devices (54 per-cent iOS, 46 percent Android). Carat collects measurements of the time it takes for the battery level to change (1 percent for Android, 5 percent for iOS)
and records which settings were active during each drop in battery level.
Whenever Carat is launched, all sam-ples are sent to a cloud analytics compo-nent. The system-settings information we gather includes screen brightness, battery information (such as battery level, voltage, health, and temperature), and network settings (such as type, sig-nal strength, and mobile technology). In addition to modifiable settings, we also gather subsystem variables, such as CPU load, information from memory usage, and distance (motion or station-ary). We refer to the collection of system settings and subsystem variables as con-text factors, and we refer to the com-bined value of each factor as the device’s system state.
Given our data, we constructed bat-tery models by measuring the strength of statistical association between con-text factors and battery discharge rates. We considered two complementary metrics for measuring the strength of statistical association:
Figure 1. The number of system settings available on current smartphones can be overwhelming.
8 PERVASIVE computing www.computer.org/pervasive
SmartPhoneS
SmartPhoneS
• gain in battery life, denoted BL Gain, which measures how changes in con-text factors influence the lifetime of a device on average; and
• the conditional mutual information (CMI) between context factors and energy rates.
To assess the influence of a single context factor X and energy rate Z, the CMI is equivalent to the mutual infor-mation (MI) given by
MI X Z
p xp x z
p x p zx X
( , )
( , z) log( , )
( ) ( )
=
⋅⋅
∈∑ ..
z Z∈∑
For higher order combinations contain-ing two or more context factors (denoted X and Y), CMI is defined as follows:
CMI X Y Z
p xp z x y zp x z p
( , )
( , y,z) log( ) ( , , )( , ) (
=
⋅ ⋅⋅ yy zx Xy Yz Z , )
.
∈∈∈∑∑∑
More details of our methodology appear elsewhere.2
SyStEM StAtE EnErgy uSAgETo demonstrate the benefits of our approach, Table 1 illustrates the influence of selected system-setting com-binations on battery consumption.2 The context factor combinations have been chosen to illustrate the complexity of the patterns that can affect battery life.
We consider the following context factors: CPU use, temperature, and dis-tance (motion or stationary) from sub-system variables, and screen brightness
from system settings. In all examples, we used mobile data for the network con-nection. We estimated battery life using the time to drain the battery from 100 to 0 percent, while actively using a smart-phone with the given system state. With different values of CPU use, battery tem-perature, movement, and screen bright-ness, the estimated battery life ranged from 3.45 hours up to 9.12 hours.
From Table 1, we can observe the rela-tionship between context factors and battery consumption to be highly com-plex. For example, previous research has suggested CPU and screen brightness to be the main culprits for battery drain. In practice, however, their effects are cor-related with other factors that can have an equally significant impact.
For example, battery temperature resulted in a loss of up to 50 percent in battery life across all CPU levels. This result is consistent across CPU usage levels, so the temperature variations result from factors other than CPU, such as higher ambient temperature, battery misbehavior or a battery bug, or because the smartphone is exposed to direct sunlight. By aggregating over a large pool of devices and usage contexts, our approach can identify these kinds of relationships and provide a detailed view of the expected lifetime of a device.
Information about the system state can be used to decide which system set-tings to change to improve battery life. For example, while moving and play-ing a game, CPU usage is often high. If the phone can be kept relatively cool, 78 percent longer battery life can be expected compared to having a higher battery temperature (increase from 4.08 hours to 7.27 hours). Further savings can be obtained by switching screen brightness to automatic.
With respect to the worst possible configuration, moving to a cooler place and changing screen brightness without changing CPU use can prolong battery life by 68 percent (increase from 3.45 hours to 5.78 hours). Cooling the device or changing movement might not always be possible for regular users, but network
TaBLe 1 Battery life in hours for selected system-setting combinations.2
battery temperature
Distance traveled CPu use
Screen brightness
Estimated battery life (hours)
Under 30° C Moving Low Automatic 8.83–9.12
Moving Low Manual 8.49–8.82
Moving High Automatic 8.09–8.24
Moving Medium Automatic 7.65–7.89
Moving Medium Manual 7.34–7.60
Moving High Manual 7.27–7.41
Stationary Medium Automatic 6.57–6.64
Stationary Low Automatic 6.28–6.35
Stationary Medium Manual 6.13–6.20
Stationary Low Manual 5.88–5.96
Stationary High Automatic 5.78–5.82
Stationary High Manual 5.00–5.04
Over 30° C Moving Low Automatic 5.08–5.22
Moving Low Manual 4.73–4.88
Moving High Automatic 4.62–4.69
Moving Medium Automatic 4.59–4.70
Moving Medium Manual 4.28–4.39
Stationary Medium Automatic 4.25–4.29
Moving High Manual 4.08–4.14
None Medium Manual 4.06–4.09
Stationary Low Automatic 4.02–4.06
Stationary High Automatic 3.91–3.94
Stationary Low Manual 3.74–3.78
Stationary High Manual 3.45–3.46
JANUARY–MARCH 2016 PERVASIVE computing 9
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connectivity and screen brightness could be seen as popular ways to reconfigure the system state. However, our results indicate that as long as the phone can observe average Wi-Fi signal strength, improving the connection will not pro-vide significant savings unless CPU use is very high. In terms of screen brightness, the main effect results from switching to automatic brightness. This effect is most pronounced for moderate CPU use and, during high use, other factors can pro-vide more pronounced changes.
B y aggregating over a large popula-tion of devices and usage contexts,
our approach can capture complex interdependencies between different factors and characterize their overall effect on battery drain. Results of our analysis revealed novel insights about battery consumption and quantified their effects. For example, we demon-strated that a Wi-Fi signal strength drop of one bar can result in a battery life loss of over 13 percent and that a smart-phone sitting in the sun can experience over 50 percent worse battery life than one indoors in cool conditions. The data used in our analysis has also been made available for research purposes (http://carat.cs.helsinki.fi/research).
Energy models that can accurately cap-ture the energy state of a device and that can estimate how system-state changes influence energy are beneficial for several reasons. For example, our approach can be used to bootstrap and support bat-tery management interfaces developed to support users. Instead of merely allow-ing users to switch off (or on) different settings, our approach can estimate how these changes are expected to influence the device lifetime. As another example, our approach can be used to construct device-specific resource-optimization strategies that can estimate changes in battery use more accurately. Our approach could also be used to construct empirical energy models for comparing and evaluating the energy effectiveness of different sensing strategies.
acknoWLeDGMenTS
The work of Ella Peltonen has been supported by
Doctoral School of Computer Science. This research
was partially supported by the Academy of Finland
grant 277498. The publication reflects only the
authors’ views.
ReFeRenceS
1. N. Vallina-Rodriguez and J. Crowcroft, “Energy Management Techniques in Modern Mobile Handsets,” IEEE Communications Surveys and Tutori-als, vol. 15, no. 1, 2013, pp. 179–198.
2. E. Peltonen et al., “Energy Modeling of System Settings: A Crowdsourced Approach,” Proc. 2015 IEEE Int’l Conf. Pervasive Computing and Com-munications (PerCom), 2015, pp. 37–45.
3. E. Peltonen et al., “Constella: Recom-mending System Settings the Crowd-sourced Way,” to appear in Pervasive and Mobile Computing, 2016; www.sciencedirect.com/science/article/pii/S1574119215001959.
4. A. Schulman et al., “Demo: Phone Power Monitoring with BattOr,” Proc. ACM MobiCom, 2011; www.stanford.edu/~aschulm/battor.html.
5. A.J. Oliner et al., “Carat: Collaborative Energy Diagnosis for Mobile Devices,” Proc. 11th ACM Conf. Embedded Networked Sensor Systems, 2013, pp. 10:1–10:14.
6. L. Zhang et al., “Accurate Online Power Estimation and Automatic Bat-tery Behavior Based Power Model Generation for Smartphones,” Proc. 8th IEEE/ACM/IFIP Int’l Conf. Hard-ware/Software Codesign and System Synthesis, 2010, pp. 105–114.
7. A. Rice and S. Hay, “Decompos-ing Power Measurements for Mobile Devices,” Proc. 2010 IEEE Int’l Conf. Pervasive Computing and Communica-tions (PerCom), 2010, pp. 70–78.
8. I. Koenig, A.Q. Memon, and K. David, “Energy Consumption of the Sensors of Smartphones,” Proc. 10th Int’l Symp. Wireless Communication Systems (ISWCS), 2013, pp. 1–5.
9. M.B. Kjærgaard et al., “Energy-Effi-cient Trajectory Tracking for Mobile Devices,” Proc. 9th Int’l Conf. Mobile Systems, Applications, and Services (MobiSys), 2011, pp. 307–320.
10. Y. Wen, R. Wolski, and C. Krintz, “Online Prediction of Battery Lifetime for Embedded and Mobile Devices,” Proc. 3rd Int’l Workshop on Power-Aware Computer Systems (PACS), 2005, pp. 131–138.
11. J.-M. Kang et al., “User-Centric Pre-diction for Battery Lifetime of Mobile Devices,” Proc. 11th Asia-Pacific Net-work Operations and Management Symp. (APNOMS), 2008, pp. 531–534.
12. X. Zhao et al., “A System Context-Aware Approach for Battery Lifetime Prediction in Smart Phones,” Proc. 2011 ACM Symp. Applied Computing (SAC), 2011, pp. 641–646.
13. N. Ravi et al., “Context-Aware Battery Management for Mobile Phones,” Proc. IEEE Int’l Conf. Pervasive Computing and Communications (PerCom), 2008, pp. 224–233.
14. H. Falaki et al., “Diversity in Smart-phone Usage,” Proc. 8th Int’l Conf. Mobile Systems, Applications, and Ser-vices (MobiSys), 2010, pp. 179–194.
Ella Peltonen is a graduate
student at the University of
Helsinki. Contact her at ella.
Eemil lagerspetz is a
postdoctoral researcher at
the University of Helsinki.
Contact him at eemil.lager-
Petteri nurmi is a senior
researcher at the University
of Helsinki and at the Hel-
sinki Institute for Information
Technology. Contact him at
Sasu tarkoma is a full pro-
fessor at the University of
Helsinki and at the Helsinki
Institute for Information
Technology. Contact him at
