Presentation on theme: "Model-Driven Energy-Aware Rate Adaptation M. Owais Khan, Vacha Dave, Yi-Chao Chen Oliver Jensen, Lili Qiu, Apurv Bhartia Swati Rallapalli 1 MobiHoc 2013,"— Presentation transcript:
Model-Driven Energy-Aware Rate Adaptation M. Owais Khan, Vacha Dave, Yi-Chao Chen Oliver Jensen, Lili Qiu, Apurv Bhartia Swati Rallapalli 1 MobiHoc 2013, Bangalore, India The University of Texas at Austin
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! ModeIntel TXIntel Rx Single Antenna1.28 W0.94 W Two Antennas1.99 W1.27 W Three Antennas2.10 W1.60 W Rate adaptation needs to energy-aware!
What’s the big deal? Highest rateLowest ETT Minimum energy!
Energy vs. Tx time: the trade-off Reduce time by 68%! Reduce time by 50%! 1.No single setting to minimize energy 2.Single antenna ≠ minimum energy Exact rate and # of antennas depend on multiple factors – Channel condition, wireless card and frame size
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 Monsoon power monitor – One reading/μs – Maximum power value every 200μs Multiple wireless cards – Intel 5300N series – Atheros 11n – Windows mobile smartphone
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 Atheros Wi-Fi transmitterAtheros Wi-Fi receiver
Intel Energy Measurements Intel Wi-Fi transmitterIntel Wi-Fi receiver
Compute loss rate Map pp-SNR to un-coded BER using known relationship Convert un-coded BER to coded BER Calculate frame error rate (FER) 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 CSICalculate pp-SNRCalculate estimated loss rateCompute ETTSelect 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 EnergyThroughput MinEng consumes 14-24% less energy than MaxTput
Intel Receiver EnergyThroughput MinEng consumes 25-35% less energy than MaxTput
Intel Receiver with PPR Energy 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 EnergyThroughput MinEng consumes 19% less energy for transmitter and 28% for receiver
Mobile Channel Energy savings do not degrade with the channel!
Related Work 27 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 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 Power Saving Mode Optimization [Napman10, Sleepwell11, E-mili11] Complementary to our work Energy measurement and Models Rate Adaptation Power Savings
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
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 iwl5300 Power Monitor gnd 56 mΏ
Phone Energy Measurements Smartphone transmitter Smartphone receiver