1. Adaptive Mobile Applications
4/21/2009
Richard Yang

2. Admin.
Project

3. Challenges in Developing Mobile, Wireless Applications
Need to adapt to heterogeneous, dynamic device/network status
Need to optimize resource usage given that resources are often limited

4. Motivation for Service Adaptation
Service delivered should depend on
device status (e.g., display capability, input method, memory size, remaining battery level)
network status
in-building
campus-area
packet radio
metro-area
regional-area

5. Adaptation: Who and How?
request
client
service
result
Adaptation need support of
the client,
the server, and/or
a third party (proxy or gateway)
Server and third party adaptation is also called content adaptation

6. Adaptation: Client Responsibility
Client informs server its device capability and connection status
Client makes best use of
available resources and
available result data

7. Content Adaptation
Server/third party adapts service/content according to client capability

8. Client Adaptation

9. Client Capability Negotiation and Monitoring
Rudimentary capability already exists, but far from complete

10. Two Examples of Client Makes Best Use of Resources/Results
Adaptive CPU scheduling
Adaptive playout

11. Example 1: Adaptive CPU Power Management

12. Big Picture: Power Management

13. Power Model

14. Some Techniques of Power Management

15. Dynamic Voltage Scaling
Dynamic Voltage Scaling (DVS) allows voltage to be dynamically adjusted
DVS-enabled processors, e.g.,
StrongARM SA-2 (500mW at 600MHz; 40mW at 150MHz)
AMD (PowerNow!)
Intel (SpeedStep, XScale)
Texas Instrument
Transmata

16. CPU Power Consumption
When the supply voltage V is lower, charging/discharging time is longer; thus frequency should be lower

17. CPU Power Consumption Model
The power consumption rate P of a CMOS processor satisfies
where k is a constant, C the capacitance of the circuit, f the CPU frequency, and V the voltage
-> P ~ O(V3)

18. Voltage Scaling
throughput
Discussion: what voltage to operate on?

19. Dynamic Voltage Scaling
Basic idea: determining voltage according to program response time requirement
For normal applications, give reasonable response time
For multimedia applications, use the deadline to determine voltage

20. multimedia applications
Architecture
multimedia applications
monitoring
requirements
scheduling
scheduler
demand distribution
profiler
time constraint
speed adaptor
speed scaling
CPU

21. Demand Prediction
Online profiling and estimation: count number of cycles used by each job 1
CDF
F(x) = P [X  x]
cumulative
probability
Cmin=b0 b1 b2
br-1
br=Cmax

22. Demand distribution is stable or changes slowly
Observations
Demand distribution is stable or changes slowly

23. CPU Resource Allocation
How many cycles to allocate to a multimedia job?
Application should meet  percent of deadlines
 each job meets deadline with probability 
 allocate C cycles, such that F (C ) =P [X  C ]   b1 b2 b0 br 1
br-1
cumulative
probability
F(x)  C

24. How Fast to Run the CPU?
Assume the strategy is to run job i at a fix (also called uniform) speed Si
Assume it needs Ci cycles during a time duration of Pi
Fact: since power is a convex function of frequency, if a job needs C cycles in a period P, then the optimal frequency is C/P, namely the lowest constant frequency.

25. Why Not Uniform Speed? Intuitively, uniform speed achieves
- minimum energy if use the allocated exactly
However, jobs use cycles statistically
- often complete before using up the allocated
- potential to save more energy
 stochastic DVS

26. Stochastic DVS For each job such that
find speed Sx for each allocated cycle x
time is 1/Sx and energy is (1 - F(x))S3x
such that

27. Observation: speed up the processor with increasing clock cycles
Example Speed Schedule
cycle:
speed:
100 MHz
200 MHz
400 MHz
Job 1
2.5x106 cycles
speed (MHz)
100
400
200
Job 2
1.2x106 cycles
Observation: speed up the processor with increasing clock cycles

28. DVS A1 B1 A1 A1 A2 context switch Store speed for switched-out
New speed for switched-in
speed up
within job
switch back A1 B1 A1
speed A1 A2
execution

29. Extension to Linux kernel 2.4.18
Implementation
Hardware: HP N5470 laptop
Athlon CPU (300, 500, 600, 700, 800, 1000MHz)
round speed schedule to upper bound
system
call
process
control
block
DVS modules
PowerNow speed scaling
Soft real-time scheduling
standard
Linux
scheduler
Extension to Linux kernel
716 lines of C code

30. Evaluation: Normalized Energy
Reduces power consumption
However, limited due to few speed options

31. Example 2: Deal with Variable Delay

32. Example: Deal with Variable Delay
Consider multimedia applications
Packets arrive with variable delay (why?)
Variable delay does not work well for multimedia application

33. Client Playout Buffer
Basic idea: delay playing out packets to accommodate delay jitter
Question: how to determine the playout delay?
packets number
time
packets
generated p p'
delay 1 r
received

34. Odyssey: An Example Client Architecture
Application indicates resource capabilities in its request to service
Operating system maintains/monitors available resources
no need to have each application re-implement the monitoring
An application registers a resource descriptor and an upcall event handler with the OS
OS notifies the application upon detecting resource changes
Application adjusts requests to the server

35. Client System Model

36. Content Adaptation

37. Objective: automating adaptation
Content Adaptation: Examples
Objective: automating adaptation

38. Example: Adapting Audio Content2 3
Send a lower resolution stream as the redundant information, e.g. nominal stream at 64 kbps and redundant stream at 13 kbps (such as GSM)

39. Example: Adapting Fidelity of Video/Image Content?
Potentially many dimensions
frame rate (for video)
image size
quality of image
Usage: e.g., data acceleration offered by many carriers

40. Frame Encoding: Block Transform Encoding
DCT
Zig-zag
Quantize
Huffman Code
Run-length Code

41. Discrete Cosine Transform
4C(u)C(v)
n-1 n-1
(2j+1)up
(2k+1)vp
å å
f(j,k) cos
F[u,v] =
cos n2 2n 2n
j=0 k=0
where 1
for w=0
Ö 2
C(w) =
for w=1,2,…,n-1 1
DCT is better at reducing redundancy than Discrete Fourier Transform but it is more computationally expensive

42. Basis Functions of DCT An image is a superposition of basis functions
DCT computes the contribution of each basis function
- F[u,v]: for the basis
function at position [u, v]
more detail

43. Example: MPEG Block Encoding
DC component
Quantize
DCT
original image
AC components
zigzag
run-length and Huffman
encoding of the stream
coded bitstream < 10 bits (0.55 bits/pixel)
Discussion: how to generate different encoding rates?

44. Examples Uncompressed (262 KB) Compressed (22 KB, 12:1)