Transcript Model-Driven Energy-Aware Rate Adaptation M. Owais Khan, Vacha Dave, Yi-Chao Chen Oliver Jensen, Lili Qiu, Apurv Bhartia Swati Rallapalli The University of Texas at Austin MobiHoc.

Model-Driven Energy-Aware
Rate Adaptation
M. Owais Khan, Vacha Dave, Yi-Chao Chen
Oliver Jensen, Lili Qiu, Apurv Bhartia
Swati Rallapalli
The University of Texas at Austin
MobiHoc 2013, Bangalore, India
1
Motivation
• Multi-antenna devices are becoming common
• Offer diverse rate choices
– # of antennas, modulation, coding, # of streams
• Rate adaptation – beaten to death problem?
• Large capacity gain, but significantly more energy!
Mode
Intel TX
Intel Rx
Single Antenna
1.28 W
0.94 W
Two Antennas
1.99 W
1.27 W
RateThree
adaptation
needs
Antennas
2.10 W to energy-aware!
1.60 W
What’s the big deal?
• Fixed antenna systems are fairly simple
𝑒𝑛𝑒𝑟𝑔𝑦𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑑 ∝ 𝐸𝑇𝑇
• Energy-aware rate adaptation becomes simple
Highest rate
Lowest ETT
Minimum
energy!
• Can this be applied to MIMO as well?
𝑒𝑛𝑒𝑟𝑔𝑦𝑚𝑢𝑙𝑡𝑖−𝑎𝑛𝑡𝑒𝑛𝑛𝑎𝑠 ≫ 𝑒𝑛𝑒𝑟𝑔𝑦𝑠𝑖𝑛𝑔𝑙𝑒−𝑎𝑛𝑡𝑒𝑛𝑛𝑎
– Additional hardware and RF chains
– But multiple data streams reduces transmission time!
Energy vs. Tx time: the trade-off
% decrease required
in tx time
Reduce time
by 68%! 70
3x3 Ant
2x2 Ant
65
60
55
Reduce time
by 50%!
50
45
40
35
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Single antenna tx time (ms)
• Exact rate and # of antennas depend on multiple factors
– Channel condition, wireless card and frame size
1. No single setting to minimize energy
2. Single antenna ≠ minimum energy
Hence, our work!
Understand energy consumption in
these devices
Design an energy-aware rate
adaptation scheme
Contributions
• Extensive power measurements for multiple 802.11n
wireless adapters
• Derive energy model based on power measurements
• Propose an energy-aware rate adaptation scheme
• Evaluate using simulation and testbed experiments
Why Model-Driven?
• Why not probing?
– Slow given the large search space w/ MIMO
– Hard to accurately measure the power of probe frames
• Model-driven
– Estimate power consumption for each rate under the
current channel condition
– Directly select the one w/ lowest power
Power Measurement Setup
• Multiple wireless cards
– Intel 5300N series
– Atheros 11n
– Windows mobile smartphone
• Monsoon power monitor
– One reading/μs
– Maximum power value every 200μs
Power Measurement Methodology
• Measurements at both transmitter and receiver
• Different configurations
– Frame size (250-1500 bytes)
– # of antennas
– 802.11n compliant data rates
Atheros Energy Measurements
1.2
0.6
0.5
0.8
Energy (J)
Energy (J)
1
0.6
0.4
3 Ant
2 Ant
1 Ant
0.2
0
0
0.5
1
1.5
0.4
0.3
0.2
3 Ant
2 Ant
1 Ant
0.1
0
2
2.5
0
0.2
0.4
0.6
0.8
1
Transmission Time (S)
Transmission Time (S)
Atheros Wi-Fi transmitter
Atheros Wi-Fi receiver
𝑬𝒏𝒆𝒓𝒈𝒚𝒄𝒐𝒏𝒔𝒖𝒎𝒆𝒅 ∝ 𝑬𝑻𝑻
Slope of the line depends on # of antennas
3
3.5
2.5
3
2
2.5
Energy (J)
Energy (J)
Intel Energy Measurements
1.5
1
0
0
0.5
1
1.5
1.5
3 Ant,1 Streams
3 Ant,2 Streams
3 Ant,3 Streams
2 Ant,1 Streams
2 Ant,2 Streams
1 Ant,1 Streams
1
3 Ant,3 Streams
2 Ant,2 Streams
1 Ant,1 Streams
0.5
2
0.5
2
2.5
Transmission Time (S)
Intel Wi-Fi transmitter
0
0
0.5
1
1.5
2
Transmission Time (S)
Intel Wi-Fi receiver
𝑬𝒏𝒆𝒓𝒈𝒚𝒄𝒐𝒏𝒔𝒖𝒎𝒆𝒅 ∝ 𝑬𝑻𝑻
Slope of the line depends on # of antennas
2.5
Measurement-Driven Energy Model
• Use least-square fitting to develop energy models
𝐸𝑡𝑥 = 𝐴 × 𝐸𝑇𝑇 + 𝐵
𝐸𝑟𝑥 = 𝐶 × 𝐸𝑇𝑇 + 𝐷
where 𝐴, 𝐵, 𝐶, 𝐷 vary for different wireless cards
Intel
Atheros
𝐴 0.24 × 𝑛𝑡𝑥 + 0.425 × 𝑀𝐼𝑀𝑂 + 1.02
0.38 × 𝑛𝑡𝑥 + 0.108
𝐵
0.045 × 𝑛𝑡𝑥 + 0.108
0.040 × 𝑛𝑡𝑥 + 0.062
𝐶
0.30 × 𝑛𝑟𝑥 + 0.61
0.142 × 𝑛𝑟𝑥 + 0.30
𝐷
0.064 × 𝑛𝑟𝑥 + 0.167
0.048 × 𝑛𝑟𝑥 + 0.106
Validating the model
𝑥 − 𝑥′
𝑀𝐴𝑃𝐸 = 𝑚𝑒𝑎𝑛(
)
𝑥
𝑥 : actual energy consumption
𝑥 ′ : estimated energy consumption
Card
Atheros
Intel
Phone
Transmission
3.4%
0.65%
4.9%
Reception
1.3%
1.4%
3.6%
Error is consistently below 5%!
Energy Aware Rate Adaptation
Select rate for next transmission
that minimized energy!
Channel State Information (CSI)
• Specifies amplitude and phase between tx-rx pair
– Measured for all subcarriers using preamble
– Reported once per received frame
• pp-SNR can be calculated as:
𝑀𝑀𝑆𝐸
𝑆𝑁𝑅𝑚
𝐸𝑠
=
𝑁𝑡 𝑁0
[𝐻𝐻 𝐻 +
1
𝐸𝑠
𝑁𝑡 𝑁0
−1
𝐼]−1
Compute loss rate
• Map pp-SNR to un-coded BER using known relationship
BERuncoded £ c1 ´Q
(
c2 ´ SNR
• Convert un-coded BER to coded BER
BERcoded ( r ) =
¥
å
a d ×Pd ( r )
d=d free
• Calculate frame error rate (FER)
FER =1- (1- BERcoded )L
• Partial packet recovery (PPR) support
– Only the ETT calculation changes (ref. paper)
)
Estimate energy consumption
• AP or back-end server keeps table of energy models
– Account for most commonly used Wi-Fi cards
• Get the make/model of the Wi-Fi card
– Explicit feedback or passive detection
• Compute ETT based on frame loss rate (FER)
• Get all MCS that can give 90% or more delivery rate
– Select the one with minimum energy
Putting it all together
Measure CSI
Calculate pp-SNR
Calculate estimated loss rate
Compute ETT
Select rate minimizes energy!
Evaluation
• Trace-driven simulator
– Static and mobile channel traces using Intel 5300
– Written in python (??? LOC)
• Testbed
– Uses the Intel 5300 card
– Iwlwifi driver is modified to support rate adaptation
Simulation Methodology
• Developed in Python using real CSI traces
• Different schemes are supported
–
–
–
–
–
Sample Rate with MIMO
Effective SNR
Maximum throughput
Minimum energy
Minimum Energy with throughput constraint
Intel Transmitter
EffSnr
MinEng
OracleMinEng
MaxTput
OracleMaxTput
SRate
70
Throughput (Mbps)
Energy (nJ/bit)
60
50
40
30
20
10
0
static 1
static 2
Energy
static 3
ETput80
ETput60
45
40
35
30
25
20
15
10
5
0
static 1
static 2
static 3
Throughput
MinEng consumes 14-24% less energy than MaxTput
Intel Receiver
OracleMinEng
MaxTput
90
80
70
60
50
40
30
20
10
0
OracleMaxTput
SRate
Throughput (Mbps)
Energy (nJ/bit)
EffSnr
MinEng
static 1
static 2
Energy
static 3
ETput80
ETput60
45
40
35
30
25
20
15
10
5
0
static 1
static 2
static 3
Throughput
MinEng consumes 25-35% less energy than MaxTput
Intel Receiver with PPR
MinEng
MaxTput
ETput80
60
70
Throughput (Mbps)
60
Energy (nJ/bit)
ETput60
50
40
30
20
10
50
40
30
20
10
0
0
static 1
static 2
Energy
static 3
static 1
static 2
static 3
Throughput
MinEng consumes 26-28% less energy than MaxTput
Testbed
• Implemented scheme on Intel Wi-Fi link 5300 driver
– Used tool in [Halperin10] to extract CSI from driver
• Static channel
– 200 UDP Packet of 1000 bytes each transmitted
– Results averaged over 10 runs
• Mobile channel
– Receiver moves away from transmitter at walking speed
– Results averaged over 5 runs
Static Channel
MinEng
80
70
60
50
40
30
20
10
0
ETput80
Throughput (Mbps)
Energy (nJ/bit)
MaxTput
tx
rx
ETput70
ETput60
50
45
40
35
30
25
20
15
10
5
0
tx
rx
Energy
Throughput
MinEng consumes 19% less energy for transmitter and
28% for receiver
Mobile Channel
Energy savings do not degrade with the channel!
Related Work
• Models based on data size [Carvahlo04], empirical study [Bala09]
• Neither considers effects of multiple antennas, data rates, tx power
• Study power consumption under different parameters[Halperin10]
• Do not develop energy model
Energy measurement and Models
• Extensively studied [Bicket05, Holland01, Sadeghi02, Wong06, etc.]
• None of these schemes consider minimizing energy
• Energy based rate adaptation [Li12]
• Limited effectiveness of probing-based approach
Rate Adaptation
• Power Saving Mode Optimization [Napman10, Sleepwell11, E-mili11]
• Complementary to our work
Power Savings
27
Conclusion
• Collect and analyze extensive power measurements
– Derive simple energy models for transmission/reception
• Develop model-driven energy-aware rate adaptation
scheme
• Experimentally show significant energy savings possible
– 14-37% over existing approaches
– PPR extensions can be even better
Questions ???
Thank You.
Selecting the min Energy Rate
• SNR values are used to calculate the delivery ratio
and expected transmission time
ETT = PacketSize x DeliveryRatio
transmission time
• Energy is calculated using the energy model
– Appropriate transmission parameters like number of
antennas
– Expected transmission time
– The rate which has the smallest estimated energy
consumption is selected
Variations
• Minimize energy with throughput constraint
– Selects a constraint on throughput. E.g. 80% of the
maximum throughput possible for a given channel
– Selects the rate which consumes the least amount
of energy while satisfying the constraint on
throughput
• Partial Packet Recovery Support
– Approach also works with PPR
– PPR only changes ETT calculation. The model
remains the same
Simulator
• Following schemes are implemented
– Sample Rate with MIMO
• Probing scheme. Uses loss rate as a metric to maximize
throughput
– Maximum Throughput
• Selects the rate which yields the highest throughput
irrespective of energy consumption
– Minimum Energy
• Selects the rate which consumes the least amount of energy
– Minimum Energy with Throughput Constraint
• Tries to minimize energy consumption by placing a threshold
on throughput loss
Multi-antenna Wi-Fi
• ETT vs. energy relationship does not hold!
– Highest throughput ≠ lowest energy
– Additional energy consumption by MIMO
• Single antenna does not always consume minimum
energy
• Rate minimizing energy depends on channel
condition and energy profile of Wi-Fi device
Solution: Joint Optimization of Energy and
Throughput through Rate Adaptation
Power Measurement Setup
56 mΏ
gnd
iwl5300
Power
Monitor
0.015
0.015
0.012
0.012
Energy (J)
Energy (J)
Phone Energy Measurements
0.009
0.006
0.009
0.006
0.003
0.003
1 Ant
0
0
0.002
0.004
0.006
1 Ant
0
0.008
Transmission Time (S)
Smartphone transmitter
0
0.003
0.006
0.009
0.012
Transmission Time (S)
Smartphone receiver
𝑬𝒏𝒆𝒓𝒈𝒚𝒄𝒐𝒏𝒔𝒖𝒎𝒆𝒅 ∝ 𝑬𝑻𝑻
Slope of the line depends on # of antennas