Energy-Aware Adaptation for Mobile Applications

Aida VosoughiBritt 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 Use less energy if energy is scarceo 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-specifico 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 type-specific functionality. There is one warden for each data type.

[1] J. Flinn and M. Satyanarayanan. Energy-aware adaptation for mobile applications.

[1]

PowerScope

• Introduced in 1999 by J. Flinn et al. • A tool for profiling energy usage by

applications.• A sample energy profile:

[2] J. Flinn and M. Satyanarayanan, "PowerScope: a tool for profiling the energy usage of mobile applications”

[2]

[2]

Experiments

• Question: Does lowering data fidelity lead to better energy savings?

QuickTime Video35% reduction through hardware management, compression, and reduced window size

Speech Recognition

69-80% reduction mainly through lower fidelity and turning off screen & network

Map Viewer46-70% reduction by hardware filtering (most efficient by removing minor and secondary roads) & cropping

Web Browser29-34% 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:• Applications should offer as high

a fidelity as possible at all times • 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 fidelityo Application-specific logging/training: randomly sample the

fidelity space and recording energy consumption at each sample point

o 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 strategyo Bias toward stability when energy is plentiful and toward agility

when it is scarce.o Odyssey caps fidelity improvements at a maximum rate of

once every 15 seconds.o When multiple applications are executing concurrently,

Odyssey must decide which to notify.

Example of Goal-Directed Adaptation

Application priorities:1. Web browser• Map viewer• Video player• 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        • Modification of HiStar OS running on ARM processor

(Android G1)

• Used to achieve 3 properties of controlo Isolationo Delegationo Subdivision

Power Guru: Implementing Smart Energy Management on the Android Platform

• Implement smart power management app on Androido Monitor all apps and their power useo 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 effectiveo Fidelity adaptationo Context-aware battery management o Application energy allotmento 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, 25-26 Feb 1999[3] Kravets, Robin; Krishnan, P., "Power Management Techniques for Mobile Communication," MobiCom 1998. pp. 157-168.[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."