Transcript Self-Tuning Wireless Network Power Management Manish Anand Edmund B. Nightingale Jason Flinn Department of Electrical Engineering and Computer Science University of Michigan.

Self-Tuning Wireless Network Power
Management
Manish Anand
Edmund B. Nightingale
Jason Flinn
Department of Electrical Engineering and Computer
Science
University of Michigan
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
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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%
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Effect of Power Management on NFS
Time to list a directory on handheld with Cisco 350 card
20
18
PSM-static:
• 16-32x slower
• 17x more energy
PSM-static
PSM-adaptive
CAM
16
Time (seconds)
14
12
10
PSM-adaptive:
• up to 26x slower
• 12x more energy
8
6
4
2
0
0
40
80
120
Number of Directory Entries
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160
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What’s Going On?
NFS issues RPCs one at a time …..
RPC requests
RPC responses
NFS Server
Access Point
Mobile Client
Beacons
50ms 100ms 100ms
Each RPC delayed 100ms – cumulative delay is large
– Affects apps with sequential request/response pairs
– Examples: file systems, remote X, CORBA, Java RMI…
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Outline
• Motivation
• Self Tuning Power Management
– Design Principles
– Implementation
– Evaluation
• Related Work and Summary
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Know Application Intent
Application: NFS File access
• Not enough
network traffic to
switch to CAM
Beacon
Period
•Data rate is
dependent on the
power mgt.
PSM
CAM
Best Policy: Use CAM during activity period
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Know Application Intent
Application: Stock Ticker that is receiving 10 packets per second
• Data rate is not
dependent on
power mgmt.
Beacon
Period
PSM
CAM
Best policy: Use PSM
STPM allows applications to disclose hints about:
- When data transfer are occurring
- How much data will be transferred (optional)
- Max delay on incoming packets
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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
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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
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Embrace Performance/Energy Tradeoff
battery life is not a
consideration
Inherent tradeoff
exists between
performance and
energy
conservation
ENERGY
longer battery life
is needed
TIME
STPM lets user specify relative priorities using a tunable knob
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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
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Outline
• Motivation
• Self Tuning Power Management
– Design Principles
– Implementation
– Evaluation
• Related Work and Summary
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STPM Architecture
Applications
Hints
Device
Characteristics
Network
Device Driver
Mode
Transitions
Base
Power
STPM
Module
Energy/Perf.
Tradeoff
User or
Energy-Aware
Energy
AwareOS
OS
Operating System
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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
Transfers
>150 ms
Run
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>150 ms
Run
15
>150 ms
Run
Run
Manish Anand
Intuition: Using the Run-Length History
40
Number of Runs
35
30
25
20
A good time to switch
15
10
5
0
1
7
13 19 25 31 37 43 49 55 61 67 73 79 85 91 97
Run Length
Switch when expected # of transfers remaining in run is high
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Expected Time to complete a Run
n 1
n   LPSM  (r  i )
i 1
Expected time to execute
transfers in PSM mode
Expected to execute rest
of the transfers in CAM
mode
1024
  LCAM (r  i )
i n
 TC  (r  n)
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Time penalty for making a
PSM to CAM switch
Manish Anand
Expected Energy to complete a Run
n1
n   LPSM ( PPSM (idle)  Pbase )(r  i )
i1
1024
  LCAM ( PCAM (idle)  Pbase )(r  i )
i n
TC
 (  (TC  Pbase ))(r  n)
• Energy calculation includes base power
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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!
Cn  (n / mean )  knob (n / mean )  (100 knob)
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Outline
• Motivation
• Self Tuning Power Management
– Design Principles
– Implementation
– Evaluation
• Related Work and Summary
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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
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Results for Coda Distributed File System
Workload: 45 minute interactive software development activity
9000
70
Energy (Joules)
8000
Time (Minutes)
60
7000
50
6000
40
5000
4000
30
3000
20
2000
1000
10
0
0
CAM
PSM-static PSM-adaptive
STPM
CAM
PSM-static
PSM-adaptive
STPM
STPM: 21% less energy, 80% less time than 802.11b power mgmt.
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Results for Coda on IBM T20 Laptop
Same workload as before: effect of base power on power mgmt strategies
70000
70
Energy (Joules)
60000
60
50000
50
40000
40
30000
30
20000
20
10000
10
0
0
CAM
PSM-static PSM-adaptive
STPM
Time (Minutes)
CAM
PSM-static
PSM-adaptive
PSM-Static and PSM-Adaptive use more energy than CAM!
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STPM
Results for XMMS Streaming Audio
Workload: 128Kb/s streaming MP3 audio from an Internet server
Effect of knowing application intent
Power (Watts)
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
XMMS buffers data on client:
• App not latency sensitive
• PSM uses least power
CAM
PSM-static
PSM-adaptive
STPM
STPM: 2% more power usage than PSM-static – no dropped pkts
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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)
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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
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Manish Anand
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
n 1
n   LPSM  (r  i )
i 1
Expected time to execute
transfers in PSM mode
Expected to execute rest
of the transfers in CAM
mode
1024
  LCAM (r  i )
i n
Time penalty for making a
PSM to CAM switch
 TC  (r  n)
Consider the case of switching before the 3rd transfer:
3  LPSM  (1  2)  LCAM  (3  4  ...)  TC  3
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Results for tuning performance/energy
Energy (Joules)
Same workload as before: effect of tuning relative priorities
8500
8000
7500
7000
6500
CAM
knob=100
• Decreasing the knob value
never yields increased
energy usage
PSM-static
knob=95
knob=90 PSM-adaptive
knob=80
knob=0-70
6000
5500
5000
4500
42
45
48
51
54
57
• Increasing the knob value
never yields reduced
performance
60
Time (minutes)
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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
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Manish Anand
Results for Non Hinting Applications
Running Mummert’s purcell trace on Coda
70
9000
Energy (Joules)
8000
Time (Minutes)
60
7000
50
6000
5000
40
4000
30
3000
20
2000
10
1000
0
0
CAM
PSM-static
PSMadaptive
STPM-nonhint
STPM
CAM
PSM-static
PSM-adaptive STPM-non-hint
STPM without hints: 16% less energy, 72% less time than 802.11b
Power Management
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STPM
Results for executing a web trace
Result of executing a 45 minute BU web trace
TIME
42.8
8000
7000
6000
5000
4000
3000
2000
1000
0
42.6
Time (minutes)
Energy(Joules)
ENERGY
42.4
42.2
42
41.8
41.6
41.4
CAM
PSM-static
PSMadaptive
STPM
CAM
PSM-static
PSMadaptive
STPM
• 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
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Results for Remote X (No Think Time)
160
70
Energy (Joules)
140
60
120
50
100
Time (Minutes)
40
80
30
60
20
40
20
10
0
0
CAM
PSM-static
PSM-adaptive
STPM
CAM
PSM-static
PSM-adaptive
STPM uses less energy than CAM if think time > 6.5 seconds
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STPM
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?
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Expected Cost Calculation
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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
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