Energy-Aware Adaptation for
Mobile Applications
Aida Vosoughi
Britt Antley
Conserving Energy in Mobile Devices
• There has always been a desire to conserve
energy in devices reliant on their battery
• First approaches dealt with purely hardware
solutions
• Soon realized that this would not be enough
• OS needed to interact with hardware to achieve
more efficient energy usage
• Needed to consider higher level approach and thus
software solutions began to be explored
Adaptive Disk Spin-Down
• Monitors the spin-down threshold and adjusts it to keep
a balance between energy consumption and
unacceptable spin-ups.
Wireless Communication Suspension
• Transport Layer Protocol (TLP) that conserves power by
choosing short periods of time to suspend communications
• Collaboration between TLP and mobile host to enable
queuing of data when communication is suspended
• Up to 83% communication power savings, resulting in
overall savings of 6-9% in laptops and 40% mobile device
Energy-Aware Adaptation for Mobile
Applications
• The idea is to modify the applications'
behavior dynamically to conserve energy.
• According to the energy supply and demand a
decision is made:
o
o
Use less energy if energy is scarce
Use more energy otherwise (better user experience)
• OS is monitoring the energy and guide the
applications to yield a battery-life of interest
Odyssey
• Introduced in 1997 by B. D. Noble et al.
• A software platform which supports adaptation for a broad
range of mobile applications.
• Integrated into Linux as a new file system, along with a set of
API extensions.
• Monitors resources such as bandwidth, CPU cycles, battery
power, etc… & makes tradeoffs
• Adaptation is achieved by trading of data quality for resource
consumption.
• Fidelity: the degree to which data presented at a client matches
the reference copy at a server.
o Fidelity is type-specific
o Odyssey allows each application to specify the fidelity levels
it supports.
Odyssey Architecture
• Viceroy: Responsible for
monitoring the availability of
resources and managing their
use.
• Wardens: Encapsulate typespecific functionality. There is
one warden for each data type.
[1]
[1] J. Flinn and M. Satyanarayanan. Energy-aware adaptation for mobile applications.
PowerScope
• Introduced in 1999 by J. Flinn et al.
• A tool for profiling energy usage by
applications.
• A sample energy profile:
[2]
[2] J. Flinn and M. Satyanarayanan,
"PowerScope: a tool for profiling the energy
usage of mobile applications”
[2]
Experiments
• Question: Does lowering data fidelity lead
to better energy savings?
QuickTime Video
35% reduction through
hardware management,
compression, and
reduced window size
Speech
Recognition
69-80% reduction
mainly through lower
fidelity and turning
off screen & network
Map Viewer
46-70% reduction
by hardware
filtering (most
efficient by
removing minor
and secondary
roads) & cropping
Web Browser
29-34% reduction by
hardware fidelity
reduction
Zoned Backlighting
• Selectively control lighting on parts of screen
• Break screen into multiple "zones" which can be lit or not
based on need
• Theoretical power savings of up to 50%
Goal-Directed Energy
Adaptation
• A mobile user has an estimate
of how long a battery needs to
last.
• Goals:
o To ensure that Odyssey
meets the specified time
duration.
o To provide the best user
experience possible.
Two requirements:
1. Applications should offer as high
a fidelity as possible at all times
2. The user should not be jarred by
frequent adaptations.
Determine the
residual energy
Predict future
energy demand
Decide if
applications should
change fidelity
Odyssey:
Predicting future energy demand
• Exponential smoothing function: new = (1-α)(this sample)+(α)(old).
• History-based prediction: Empirical approach to predict energy
consumption of a specific application as a function of fidelity
o
o
Application-specific logging/training: randomly sample the
fidelity space and recording energy consumption at each
sample point
Offline/Online learning: using machine learning algorithms
Simplest such predictor is linear.
(E= c0 + c1S+ c2fS, where S is a
constant = e.g. size of image and
f is fidelity)
Odyssey:
Triggering Adaptation
• Predicted demand > Residual energy: Up-calls so that
applications can adapt to reduce energy usage.
• Residual energy > Predicted demand: Applications are
notified to increase data fidelity.
• Level of hysteresis in Odyssey’s adaptation strategy
o
o
o
Bias toward stability when energy is plentiful and toward agility
when it is scarce.
Odyssey caps fidelity improvements at a maximum rate of once
every 15 seconds.
When multiple applications are executing concurrently,
Odyssey must decide which to notify.
Example of Goal-Directed
Adaptation
Application priorities:
1. Web browser
2. Map viewer
3. Video player
4. Speech recognizer
Modern Implementations
• Focus on uses in modern mobile OS
Energy Management in Mobile Devices
with the Cinder Operating System
• Controlling energy allocation is crucial feature for mobile
OS's
• Introduces abstraction of reserves and taps
• Used to achieve 3
properties of control
o Isolation
o Delegation
o Subdivision
• Modification of HiStar OS running on ARM processor
(Android G1)
Power Guru: Implementing Smart Energy
Management on the Android Platform
• Implement smart power management app on Android
o Monitor all apps and their power use
o Give user suggestion on which apps to kill to optimize
battery life
• Apps are given Power Rating based on their CPU utilization
and hardware usage
• User can Prioritize certain apps to not be killed and other
apps are ranked based on Power Rating
Context-aware Battery
Management for Mobile Phones
• Battery meter/ “battery low” audio signals are not enough
anymore…
• CABMAN Problem: Will the battery last until the next charging
opportunity is encountered?
Next charging opportunity? Call time requirements? “Discharge speedup factor”?
Summary
• The main goal is to converse energy without affecting
usability
• High level solutions are effective
o Fidelity adaptation
o Context-aware battery management
o Application energy allotment
o Giving user suggestions for power savings
References
[1] Jason Flinn and M. Satyanarayanan. 1999. Energy-aware adaptation for mobile applications. In Proceedings of the
seventeenth ACM symposium on Operating systems principles (SOSP '99). ACM, New York, NY, USA, 48-63.
[2] Flinn, J.; Satyanarayanan, M.; , "PowerScope: a tool for profiling the energy usage of mobile applications," Mobile
Computing Systems and Applications, 1999. Proceedings. WMCSA '99. Second IEEE Workshop on , vol., no., pp.2-10, 2526 Feb 1999
[3] Kravets, Robin; Krishnan, P., "Power Management Techniques for Mobile Communication," MobiCom 1998. pp. 157168.
[4] Arjun Roy; Stephen M. Rumble; Ryan Stutsman; Philip Levis; David Mazières; Nickolai Zeldovich; "Energy Management
in Mobile Devices with the Cinder Operating System," EuroSys '11. April 10-13 2011.
[5] Ravi, N.; Scott, J.; Lu Han; Iftode, L.; , "Context-aware Battery Management for Mobile Phones," Pervasive Computing
and Communications, 2008. PerCom 2008. Sixth Annual IEEE International Conference on , vol., no., pp.224-233, 17-21
March 2008
[6] D. Narayanan, J. Flinn, and M. Satyanarayanan. 2000. Using history to improve mobile application adaptation. In
Proceedings of the Third IEEE Workshop on Mobile Computing Systems and Applications (WMCSA'00) (WMCSA '00).
IEEE Computer Society, Washington, DC, USA
[7] Brian Noble, M. Satyanarayanan, and Morgan Price. 1995. A Programming Interface for Application-Aware Adaptation in
Mobile Computing. In Proceedings of the 2nd Symposium on Mobile and Location-Independent Computing (MLICS '95).
USENIX Association, Berkeley, CA, USA, 57-66.
[8] Brian D. Noble, M. Satyanarayanan, Dushyanth Narayanan, James Eric Tilton, Jason Flinn, and Kevin R. Walker. 1997.
"Agile application-aware adaptation for mobility". SIGOPS Oper. Syst. Rev. 31, 5 (October 1997)
[9] Unelsroed, Hans Fredrik; Roeine, Per Christian; Ghani, Fahad. "Power Guru: Implementing Smart Power Management
on the Android Platform."