1. Energy-Delay Tradeoffs in Smartphone Applications
Moo-Ryong Ra
Jeongyeup Paek, Abhishek B. Sharma Ramesh Govindan, Martin H. Krieger, Michael J. Neely
Mobisys 2010

2. Introduction

3. Battery lifetime
Urban Tomography system users reported that battery lifetime is a critical usability issue

4. Energy-delay tradeoff

5. Algorithm
They design a control algorithm, called SALSA (Stable and Adaptive Link Selection Algorithm): use the Lyapunov optimization framework - minimizes the total energy expenditure subject to keeping the average queue length finite
This algorithm considers link selection problem

6. Problem statement, model and objective

7. Notations A[t]: the size of video data in bits
P[t]: the power consumption ,
μ[t]: the amount of data transferred
U[t]: queue backlog
L[t]: set of links visible to a smartphone
[t]: the quality of the wireless link
I[t]: indicator random variable 0, 1
I[t] == 1 >> smartphone decides to transmit data
I[t] == 0 >> otherwise

8. Model and objective μ[t] ≜ C(I[t], l, [t], U[t], P[t])
U[t+1] = U[t] - μ[t] + A[t]
Stability
Minimizes the time average transmit power expenditure

9. The link selection algorithm

10. SALSA’s control decision
SALSA decides, every timeslot t, whether to transmit data from its queue, and which of its available links to use
The performance of this algorithm critically depends upon the choice of V
SALSA’s control decision using the Lyapunov optimization

11. Constraints Power consumption satisfying: Queue backlog satisfying:
Trade-off between power consumption and delay depend on the parameter V: [O(1/V), O(V)]
P* is a theoretical lower bound on the time average power consumption
B is an upper bound on the sum of the variances of A[t] and μ[t]

12. Choosing a good V V controls the energy-delay tradeoff
(α is the slope of )
Adapt V to the instantaneous delay
D[t] denotes the instantaneous delay in data transfer
SALSA computes B based on all the A[t] and μ[t] values observed over some large time window
It updates its value whenever the estimate for B is updated
Instead of using a different parameter, they chose to use α in order to have only one free parameter in SALSA

13. Evaluation

14. Overview
They use trace-driven simulation - arrival traces: derived from users of their urban tomography system in real-world settings - link availability traces: generated empirically by carrying a smartphone on a walk across different environments
They compare SALSA against two baseline algorithms: - minimize delay and always uses WiFi

15. Arrival Patterns
They use a total of 42 arrival patterns consisting of a total of 935 videos

16. CDF link availability with failure probability
CDF of the average transfer rate per 20-second window
USC campus
A large shopping mall near Los Angeles (Glendale Galleria)
Los Angeles International Airport (LAX)

17. Comparison Minimum-delay algorithm WiFi-only algorithm
Static-delay algorithm
Know-WiFi algorithm
Minimum-delay algorithm: always transfers data when an AP is available (High energy)
WiFi-only algorithm: uses only WiFi APs (Unbounded delay)
Static-delay algorithm: it has not seen any WiFi AP in the past T timeslots, it uses the first link that becomes available (Not take link quality into account)
Know-WiFi algorithm: assumes information about the availability of WiFi APs in the future (Not consider queue backlog)

18. Performance metrics The average energy consumed per byte -
The average delay per byte -
Dispersion

19. Minimum-delay vs WiFi-only vs SALSA

20. SALSA’s performance

21. Comparison with threshold-based algorithms
관점이 달라서 관점을 하나로 통일 >> the most aggressive value, the least aggressive value

22. Sensitivity to the scanning interval
Four additional scanning intervals: 60s, 120s, 180s, and 240s
The sweet spot for the scanning interval appears to be 60 seconds
They simulated HD traffic in a single collision domain under varying densities and different bitrates
Back2F provides gains are in the range of 15% to 30%

23. Experimental results

24. Environment
They implement SALSA in a video transfer application developed in Symbian C++ for the Nokia N95 smartphone
One volunteer carried five phones each configured with different values of α, and conducted 5 walks

25. Experimental result
At the USC Campus
At Shopping Mall

26. Summary

27. Summary
Adaptive algorithm for energy/delay trade off - Extensive evaluation with real world scenarios - Validation with real implementation - Provable performance bound