GEODES
Global Energy Optimisation for Distributed heterogeneous Embedded Systems
Tutorial
Exploitation of power management techniques in
Wireless Sensor Networks
Sébastien Bilavarn, Cécile Belleudy
University of Nice Sophia Antipolis - CNRS LEAT
Como, Italy
Feb. 2011
WUPS tutorial / GEODES – LEAT Univ. Nice / CNRS
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Outline
• Power management: state of the art
• Energy and Power Aware Scheduling
• Power management: Howto
• Power Management
• Operating System
• Power Strategies
• Application study
• Low power video surveillance application
• Conclusion
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Exploitation of power management
techniques in Wireless Sensor Networks
Exploitation of power management techniques in Wireless Sensor
Networks
POWER MANAGEMENT:
STATE OF THE ART
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Energy and Power aware Scheduling
Application + constraints
Features of the platform
Issue : schedule in order to meet time constraint with lowest energy :
 determine a global frequency based on the worst case execution time
of a task set,
 tune the processor frequency according to the workload,
 low power mode in case of no (or low) workload.
Global frequency
Local frequency
Low power mode
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Dynamic Voltage and Frequency Scaling
Deadline
Power
P1
Task ti
P2 < P1
E2 < E1
f2
Global frequency (for all
task) : based on the
schedulability test
according to the worst
case execution time of a
task set
V2 < V1
f2 < f1
f1
Task ti
Hors ligne
Slow down factor:
S = f1 / f2
Local frequency (for each job) :
based on the schedulability test,
The worst case execution time
is substituted by the actual
execution time
=> Lot of switch
Lot of works : LP EDF, CC-RM,
Power savings ?
Unrealistic hypothesis : Tswitch ?
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Low power modes (DPS)

2 to 4 low power modes :
Example : Idle : data are saved in the memory cache,
Sleep : data are lost.

Switch in low power mode
Time requirement
T1
T2
T1
T3
Data in the cache
memory ?
Real time system : Off line or
online policy Based on the
schedulabitity test
…
T > Tsleep + Twakeup,
Soft system : Probability based
estimations of inactivity time,
period, duration
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DVFS or DPS
Deadline
F1,V1
Task1
Processeu
r : nop
E = Etask1,F1,V1 + Enop
Deadline
F2<F1,V2<V1
Task1
E’ = Etask1,F2,V2 + EswitchVF
E’ < Etask1,F1,V1 < E
Decrease of E and Ppeak.
Deadline
F1,V1
Task1
Processor in
low power
mode
E’’ = Etask1,F1,V1 + Eswitch + Elp mode repos
E’’ > Etask1,F1,V1(>E’) < E
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Global methodology

Power mode modelling : power characterization, time (switch delay)

High level estimation of system (application, platform): power, energy,
autonomy

Power manager : strategy to reduce the power consumption, autonomy, …
Modelling
Experiment
Power saving
Estimation
Optimisation :
Exploration de
solutions HW, SW
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Exploitation of power management
techniques in Wireless Sensor Networks
Exploitation of power management techniques in Wireless Sensor
Networks
POWER MANAGEMENT:
HOWTO
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Power Management (1)
• What is power management ?
• We refer in the following to CPU level techniques
able to operate dynamically (on the fly).
• Hardware capabilities to exploit sleep modes, and/or
to change the operating frequency.
• A set of:
• Power management mechanisms
• Dynamic Voltage and Frequency Scaling (DVFS)
• Dynamic Power Switching (DPS)
• Power management policies (or strategies)
• Governors
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Power Management (2)
• Power management mechanisms
• DVFS: frequency scaling
• Ability to change the CPU(s) clock speed on the fly
• States are called operating performance points
• Example: TI OMAP3530
• DPS: power swtiching
• Ability to switch CPU(s) low-power modes
• Many idle states with different recovery time and
power
GEODES Présentation
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Power Management (3)
• Power management strategies
• Frequency selection policy
• Automatic choice among performance states
• Governors: how to apply these states?
• Examples: Ondemand, Conservative (Linux)
• Mainly workload based
• DPS: Dynamic Power Swtiching
• Automatic choice among idle states
• Governors:
• Examples: ladder, menu (Linux)
• Step wise/parameter based approach to select idle states
GEODES Présentation
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Linux PM support (1)
• Provides a platform independent interface
• To handle power management mechanisms
• To use existing power management strategies
• Driver support
• DVFS: CPUfreq
• DPS: CPUidle
• Supported by a large variety of platforms
• Basic power management policies
• Governors
• Open source community support
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Linux PM support (2)
• Demo
• OMAP-PM Distribution (Kevin Hilman)
• http://elinux.org/OMAP_Power_Management
• Suspend/resume
• echo mem > /sys/power/state
• mount -t debugfs nodev /sys/kernel/debug/
• echo 4 > /debug/pm_debug/wakeup_timer_seconds
• DVFS
• echo 125000 > /sys/devices/system/cpu/cpu0/cpufreq/scaling_setspeed
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Power strategies (1)
• Definition
• A power strategy is a module which functionality is to
apply power management techniques on the
resources of the system in a way to save power
and/or energy.
• The power strategies are applied at runtime to
support current and future resource requirements
• Example
• So far, very basic power strategies are actually used
• Linux CPUfreq governors
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Power strategies (2)
Existing power strategies: Linux CPUfreq governors
Performance governor: Highest frequency
The performance governor statically sets the processor to the highest frequency available.
Powersave governor: Lowest frequency
On the flip side, the powersave governor statically sets the processor to the lowest
available frequency.
Userspace governor: Manual frequencies
Next there is the userspace governor, which allows you to select and set a frequency
manually.
Ondemand governor: Frequency change based on processor use
The ondemand governor checks the processor utilization and if it exceeds the threshold,
the governor will set the frequency to the highest available.
Conservative governor: A more gradual ondemand
The conservative governor checks the processor utilization and if it is above or below the
utilization thresholds, the governor steps up or down the frequency to the next
available instead of just jumping to the highest frequency as ondemand does.
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Power strategies (3)
• Limits of existing strategies
• Typical strategies are based on processor workload,
but the workload is not always the driving parameter
for frequency scaling oportunities (actual execution
time, latency, bit rate, etc).
• There is room for more efficient strategies by defining
strategies that are specific to the application domain.
• Task scheduling
• Video processing
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Power strategies (4)
• Limits of existing strategies
• Example: video processing
• Video processing is almost in all cases a 100% CPU load
application.
• Using an Ondemand governor will result in setting the CPU to
its maximum frequency.
• The driving parameter in a video application is the frame rate.
• Adapting the CPU frequency to the framerate required (e.g. 30
fps) has potential to decrease the operating frequency (thus
power consumption).
So how to develop a power strategy suited to an
application domain ?
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Exploitation of power management
techniques in Wireless Sensor Networks
Exploitation of power management techniques in Wireless Sensor
Networks
APPLICATION STUDY: VIDEO
OVER IEEE 802.15.4
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Design of a power strategy
• Objectives
• Design of an ad hoc power strategy suited to WSN constraints
• Illustration: low power video surveillance application
• Development approach
• Application development
• Performance analysis
• Power strategy definition
• Power savings measurement/estimations
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Application overview
Very low power video surveillance application


Video monitoring and transmission using IEEE 802.15.4
802.15.4

802.15.4
Camera sensor



Video capture
Compression
Transmission IEEE 802.15.4

Base station/cluster head



WUPS tutorial / GEODES – LEAT Univ. Nice / CNRS
Reception IEEE 802.15.4
Decompression
Video display
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Requirements (1)
• Hardware
• Wireless radio
• IEEE 802.15.4
• low power, short range (10 – 75 meters), low bit rate (20 - 250 Kbit/s)
• Power management
• DVFS on each kind of nodes (camera sensor, display nodes)
• Monitoring capabilities
• Camera: compression is mandatory given transmission speed limitations
• Display: only on display nodes (some basestations used as control nodes)
• Video compression/decompression
• A MPEG2 standard is used for encoding complexity reasons (low
processing power of WSN nodes)
→ Suited platform
→ OS support (variety of drivers)
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Requirements (2)
• Platforms
• Crossbow IMOTE2
• PXA271 XScale® Processor at 13-416MHz
• Wireless radio:
•
integrated 802.15.4
• Power management
•
DVFS, DPS
• Monitoring capabilities
•
Extension for camera chip interface
•
No display
• Operating System
•
TinyOS: few drivers support
•
No Linux support
• No CPU power measurement
→ Not usable in practice
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Requirements (3)
• Platforms
• Beagleboard
• TI OMAP3530 processor
•
Up to 600 MHz
• Wireless radio
•
None
• Power management
•
DVFS, DPS
• Monitoring capabilities
•
USB video camera
•
DVI-D output
• Operating System
•
Many ports of Linux distributions
•
Ubuntu, Linux-omap, etc
• No CPU power measurement
→ Intended for mobile phones, but large set of devices, APIs and drivers (Linux)
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Requirements (4)
• Operating System
• Linux APIs and drivers
• Wireless radio
• XBee modules under the control of a
serial connection, using termios API
• Power management
• CPUfreq, (CPUidle)
• Monitoring capabilities
• Camera: standard USB webcam supported by
video capture API for Linux (V4L USB Video Class)
• Display: X Window, Graphical User Interface, GIMP toolkit (GTK)
• Distributions
• Linux-omap-pm: Camera sensors
• Ubuntu: Display nodes
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Demo
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Performance analysis
MPEG2 performance on OMAP3 (w/o 802.15.4)
125MHz
250MHz
500MHz
550MHz
600MHz
720MHz
Encoder
0,95 fps
1,92 fps
3,80 fps
4,16 fps
4,76 fps
5,61 fps
Decoder
23 fps
43 fps
92 fps
101 fps
111 fps
130 fps
MPEG2 performance on OMAP3 (w/ 802.15.4)
• Bit rate: 4KB/sec max (average compressed frame size: 2KB)
• Frame rate: 2.3 fps max
Application 1: DVFS strategy for video encoding
→ adapt OMAP frequency to the variations of the bit rate
2 KB/s → 125 MHz, 4 KB/s → 250 MHz, 8 KB/s → 500 MHz
1 fps → 125 MHz, 2 fps → 250 MHz, 4 fps → 500 MHz
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Power strategy definition
Bit rate KB/sec
8
500MHz
4
250MHz
2
125MHz
Time
1fps -2fps
< 1fps
1fps - 2fps
> 2fps
Bit rate < 2KB/s → useless to run CPU at more than 125 MHz
Bit rate < 4KB/s → useless to run CPU at more than 250 MHz
Bit rate < 8KB/s → useless to run CPU at more than 500 MHz
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Evaluation conditions
Energy savings and lifetime extensions are based on measures
and estimations:
• It is not possible to measure power/energy (on OMAP/beagleboard)
• Limitations on DVFS implementation (Linux CPUfreq/beagleboard)
• Transmission speed can actually be measured on a real video
capture and transmission example. DVFS - Transmit adaptation
principle:
bit rate
< 2000 Bytes/sec → 125 MHz
bit rate
< 4000 Bytes/sec → 250 MHz
(4000 Bytes/sec < bit rate
< 8000Bytes/sec → 500 MHz)
2000 Bytes/sec <
• We can combine this with real power measures (on OMAP/EVM)
OPP
125MHz
250MHz
500MHz
550MHz
600MHz
P (mW)
57
130
303
371
445
720MHz
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Energy and Lifetime analysis
(1)
Analysis of energy savings
• Based on transmission speed measures and OMAP 3530 power
consumption measures at different operating points
• An estimation of energy can be comptuted from the time spent in
frequency each zone i: E = Σ(POMAP * Ti).
• Energy savings are given by the ratio δ = (Ew/o - Ew/ ) / Ew/
Analysis of lifetime extension
• From previous energy savings (δ %), we can compute an
estimation of lifetime extension Textended based on E = Paverage* T
• Since E is reduced from δ %, considering Paverage is constant
implies: Textended = T * 100 / (100 - δ)
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Energy and Lifetime analysis
(2)
Analysis of energy savings and lifetime extension over no
DVFS strategy
•
First case: node is operating at nominal frequency (500 MHz)
→ Energy gains over nominal frequency (500 MHz)
• Second case: node is set a minimum possible frequency to
process video without loss in quality level (250 MHz).
→ Energy gains over minimum static frequency (250 MHz)
Measures are performed on 3 sample tests where the actual
transmission speed is measured on 2 physical prototypes
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Sample test #1
36s video monitoring
Bytes/sec
Transmission speed variations
3000
250MHz
2500
2000
1500
125MHz
1000
500
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Frame
number
Energy savings + lifetime extension over nominal freq (500MHz)
Energy
Energy savings
Lifetime extension
500MHz
10.908 J
7.182 J
--
W/ strategy
3.726 J
65.8%
2.92x
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Sample test # 2
30s video monitoring
Bytes/sec
3000
250MHz
2500
2000
1500
125MHz
1000
500
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Frame
number
Energy savings + lifetime extension over nominal freq (500MHz)
Energy
Energy savings
Lifetime extension
500MHz
9.09 J
6.504
--
W/ strategy
2.586 J
71.5%
3.51x
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Sample test # 3
135s video monitoring
Bytes/sec
3000
250MHz
2500
2000
1500
125MHz
1000
500
1
7
13
19
25
31
37
43
49
55
61
67
73
79
85
91
97
103
109
115
121
127
133
139
145
151
157
163
169
175
181
187
193
199
205
0
Frame
number
Energy savings + lifetime extension over nominal freq (500MHz)
Energy
Energy savings
Lifetime extension
500MHz
63.63 J
45.966
--
W/ strategy
17.664 J
72.2%
3.6x
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Sample test #1
36s video monitoring
Bytes/sec
Transmission speed variations
3000
250MHz
2500
2000
1500
125MHz
1000
500
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Frame
number
Energy savings + lifetime extension over minimum static freq (250MHz)
Energy
Energy savings
Lifetime extension
250MHz
4.68 J
0.954
--
W/ strategy
3.726 J
20.4%
1.26x
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Sample test # 2
30s video monitoring
Bytes/sec
3000
250MHz
2500
2000
1500
125MHz
1000
500
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Frame
number
Energy savings + lifetime extension over minimum static freq (250MHz)
Energy
Energy savings
Lifetime extension
250MHz
3.9 J
1.314 J
--
W/ strategy
2.586 J
33.7%
1.49x
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Sample test # 3
135s video monitoring
Bytes/sec
3000
250MHz
2500
2000
1500
125MHz
1000
500
1
7
13
19
25
31
37
43
49
55
61
67
73
79
85
91
97
103
109
115
121
127
133
139
145
151
157
163
169
175
181
187
193
199
205
0
Frame
number
Energy savings + lifetime extension over minimum static freq (250MHz)
Energy
Energy savings
Lifetime extension
250MHz
27.3 J
9.636
--
W/ strategy
17.664 J
35.3%
x 1.55
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GEODES
Global Energy Optimisation for Distributed heterogeneous Embedded Systems
Tutorial
Exploitation of power management techniques in
Wireless Sensor Networks
Sébastien Bilavarn, Cécile Belleudy
University of Nice Sophia Antipolis - CNRS LEAT
Como, Italy
Feb. 2011
WUPS tutorial / GEODES – LEAT Univ. Nice / CNRS
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