self-tuning wireless network power management

Self-Tuning Wireless Network Power Management

Manish Anand

Edmund B. Nightingale

Jason Flinn

Department of Electrical Engineering and Computer Science

University of Michigan

Manish Anand2MobiCom 2003

Motivation

Wireless connectivity is vital to mobile computing•But, taxes limited battery capacity of a mobile device

Power management can extend battery lifetime -However, it can negatively impact performance


802.11 Network Power Management

Network interface may be continuously-active (CAM)– Large power cost (~1.5 Watts)– May halve battery lifetime of a handheld

Alternatively, can use power-saving mode (PSM)– If no packets at access point, client interface sleeps– Wakes up periodically (beacon every 100 ms)– Reduces network power usage 70-80%


Effect of Power Management on NFS

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Tim

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PSM-static

PSM-adaptive

CAM

PSM-static:• 16-32x slower• 17x more energy

PSM-adaptive:• up to 26x slower• 12x more energy

Time to list a directory on handheld with Cisco 350 card


What’s Going On?

NFS issues RPCs one at a time …..

NFS Server

Access Point

Mobile Client

50ms 100ms 100msBeacons

RPC requests RPC responses

Each RPC delayed 100ms – cumulative delay is large– Affects apps with sequential request/response pairs– Examples: file systems, remote X, CORBA, Java RMI…


Outline

• Motivation• Self Tuning Power Management

– Design Principles– Implementation– Evaluation

• Related Work and Summary


Know Application Intent

Application: NFS File access

Best Policy: Use CAM during activity period

CAMPSM

Beacon Period

• Not enough Not enough network traffic to network traffic to switch to CAMswitch to CAM

•Data rate is Data rate is dependent on the dependent on the power mgt.power mgt.


Know Application Intent

Application: Stock Ticker that is receiving 10 packets per second

Best policy: Use PSM

• Data rate is not Data rate is not dependent on dependent on power mgmt.power mgmt.

STPM allows applications to disclose hints about:- When data transfer are occurring - How much data will be transferred (optional) - Max delay on incoming packets

PSM

Beacon Period

CAM


Be Proactive

Transition cost of changing power mode: 200-600 ms.

Large transfers: use a reactive strategy

- If transfer large enough, should switch to CAM

- Break-even point depends on card characteristics

- STPM calculates this dynamically

Many applications (like NFS) only make short transfers: be proactive

- Benefit of being in CAM small for each transfer

- But if many transfers, can amortize transition cost

- STPM builds empirical distribution of network transfers

- Switches to CAM when it predicts many transfers likely in future


Respect the Critical Path

Many applications are latency sensitive

- NFS file accesses

- Interactive applications

- Performance and Energy critical

Other applications are less sensitive to latency

- Prefetching, asynchronous write-back (Coda DFS)

- Multimedia applications (with client buffering)

- Only energy conservation critical

Applications disclose the nature of transfer: foreground or background


Embrace Performance/Energy Tradeoff

Inherent tradeoff exists between

performance and energy

conservation

STPM lets user specify relative priorities using a tunable knob

E N E R G Y

T IM E

b attery life is n o t a con sid era tion

lon ger b a ttery life is n eed ed


Adapt to the operating environment

Must consider base power of the mobile computer

Consider mode that reduces network power from 2W to 1W- Delays interactive application by 10%

On handheld with base power of 2 Watts:- Reduces power 25% (from 4W to 3W)- Energy reduced 17.5% (still pretty good)

On laptop with base power of 15 Watts:- Reduces power by only 5.9%- Increases energy usage by 3.5%- Battery lasts longer, user gets less work done


Outline





STPM Architecture

STPMModule

Applications

NetworkDevice Driver

DeviceCharacteristics

ModeTransitions

Energy-AwareOS

Hints

BasePower

Energy/Perf.Tradeoff

Operating System

User or Energy Aware OS


Transition to CAM

STPM switches from PSM to CAM when:

1. Application specifies max delay < beacon period

2. Disclosed transfer size > break-even size

3. Many forthcoming transfers are likely

To predict forthcoming transfers STPM generates an empirical distribution of run lengths

>150 ms >150 ms>150 ms

Transfers

Run Run Run Run


Intuition: Using the Run-Length History

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Run Length

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ber o

f Run

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A good time to switch

Switch when expected # of transfers remaining in run is high


Expected Time to complete a Run

)(

)(

)(

1024

1

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nr

irL

irL

TC

ni

CAM

n

i

PSMn

Expected time to execute transfers in PSM mode

Expected to execute rest of the transfers in CAM

mode

Time penalty for making a PSM to CAM switch


Expected Energy to complete a Run

)())((

)()(

)()(

1024

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1

1

)(

nrP

irPPL

irPPL

baseTCTCni

baseidleCAMCAM

n

i

baseidlePSMPSMn

• Energy calculation includes base power


Performance and Energy Tradeoff

Calculate expected time and energy to switch after each # of transfers– What if these goals conflict?– Refer to knob value for relative priority of each goal!

)100()/()/( knobknobC meannmeannn


Outline





Evaluation

Client: iPAQ handheld with Cisco 350 wireless card

Evaluate STPM vs. CAM, PSM-static, and PSM-adaptive:– NFS distributed file system– Coda distributed file system– XMMS streaming audio– Remote X (thin-client display)

Run DFS workload to generate access stats for STPM– Use Mummert’s file system trace (SOSP ’95)– File system operations (e.g. create, open, close)– Captures interactive software development


Results for Coda Distributed File System

STPM: 21% less energy, 80% less time than 802.11b power mgmt.

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CAM PSM-static PSM-adaptive STPM

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Energy (Joules) Time (Minutes)

Workload: 45 minute interactive software development activity


Results for Coda on IBM T20 Laptop

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PSM-Static and PSM-Adaptive use more energy than CAM!

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Same workload as before: effect of base power on power mgmt strategies


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Results for XMMS Streaming Audio

STPM: 2% more power usage than PSM-static – no dropped pkts

XMMS buffers data on client:• App not latency sensitive• PSM uses least power

Power (Watts)

Workload: 128Kb/s streaming MP3 audio from an Internet server

Effect of knowing application intent


Related Work– Lu, Y.H., Benini, L., AND Micheli, G.D. Power-aware operating

systems for interactive systems. IEEE Trans. on VLSI (April 2002)

– Simunic, T., Benini, L., Glynn, P. and Micheli, G.D. Dynamic

Power Management for Portable Systems. Mobile Computing and Networking (2000)

– Kravets, R., and Krishnan, P. Application-driven power management for mobile communication. ACM Wireless Nets. (2000)

– Shih’s Wake on wireless: (MOBICOM '02)

– Krashinsky’s BSD Protocol: (MOBICOM '02)


Summary

STPM adapts to:

– Base power of mobile computer

– Application network access patterns

– Relative priority of performance and energy conservation

– Characteristics of network interface

Compared to previous power management policies, we perform better and conserve more energy

Self-Tuning Wireless Network Power Management

Manish Anand

Edmund B. Nightingale

Jason Flinn

Department of Electrical Engineering and Computer Science

University of Michigan


Expected Time to complete a Run

)(

)(

)(

1024

1

1

nr

irL

irL

TC

ni

CAM

n

i

PSMn

Expected time to execute transfers in PSM mode

Expected to execute rest of the transfers in CAM

mode

Time penalty for making a PSM to CAM switch

343213 ...)()( TCCAMPSM LLConsider the case of switching before the 3rd transfer:


Results for tuning performance/energy

• Decreasing the knob value never yields increased energy usage

• Increasing the knob value never yields reduced performance

Same workload as before: effect of tuning relative priorities

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42 45 48 51 54 57 60

Time (minutes)

En

erg

y (J

ou

les)

CAM

PSM-adaptive

PSM-static

knob=100

knob=95knob=90

knob=80knob=0-70


Self Tuning Power Management

STPM adapts to:

– Base power of mobile computer

– Application network access patterns

– Relative priority of performance and energy conservation

– Characteristics of network interface

Compared to previous power management policies, we perform better and conserve more energy


Results for Non Hinting Applications

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STPM-non-hint

STPM

Running Mummert’s purcell trace on Coda

Energy (Joules)

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CAM PSM-static PSM-adaptive STPM-non-hint STPM

Time (Minutes)

STPM without hints: 16% less energy, 72% less time than 802.11b Power Management


Results for executing a web trace

Result of executing a 45 minute BU web trace

• CAM performs only 0.8% better than PSM-static while expending 62% more energy

• STPM behaves like PSM-static when conserving energy and like CAM in presence of abundant energy

ENERGYENERGYTIMETIME

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STPM

Ene

rgy(

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STPM

Tim

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Results for Remote X (No Think Time)

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STPM uses less energy than CAM if think time > 6.5 seconds



Managing Other Devices with STPM

STPM well-suited for power management when:– Performance / energy conservation tradeoff exists– Transition costs are substantial

Consider disk power management:– Web browser, DFS, mobile DB cache data locally– Hard drive spins down for power saving– Significant transition cost to resume rot. latency– Faster, less energy to read small object from server– But, if many accesses, want to spin-up disk

For what other devices can STPM be applied?


Expected Cost Calculation


STPM as a wireless power management strategy

• Holistic solution

– Application intent through hints

– Proactive solution using run histogram

– Nature of network transfer : foreground or background

– Performance/Energy tradeoff with a tunable knob

– Operating Environment: base power

self-tuning wireless network power management

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