Download Energy Management Issues in Wireless Computing and Networking

Energy Management Issues in
Wireless Computing and
Networking
Srijan Chakraborty
Objective

Reducing energy consumption of battery
powered devices, e.g., Laptops and
Handhelds, in wireless networks.


Efficient runtime adaptation of application
parameters.
Exploiting mobility by movement prediction in
a wireless ad hoc network.
Presentation Outline





Motivation
Predicting Energy Consumption of MPEG Video Playback on Handhelds

Introduction

Experimental setup

Experimental results

Energy consumption models

Summary
Movement prediction

Observation

Power saving strategy

System model

Heuristics

Simulation results

Summary
Conclusion
Future work
Motivation


Wireless networks are getting popular.
Increasing interest in mobile ad hoc networks





Easy and low cost deployment
Mobility
No infrastructure
Highly dynamic
Problems




Routing – nodes keep moving in and out of the network.
Security – selfish, malicious, uncooperative nodes.
Scalability.
Limited battery life.

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
Network communication – major energy drainer. For handhelds over
50% of the battery life can be consumed by network interface card!
Improvements in battery technology - lifetime has increased. However,
not to the extent to keep up with the increased energy requirement.
Needs software level energy saving strategies.
Related Work – Energy Saving
Techniques

Hardware level schemes



Low power hardware design
Dynamic voltage scaling
Switching to power saving modes





the whole device
network interface card
individual memory chips
Transmission power control
Software level schemes




Computation offloading
System redesign with energy metric
Runtime adaptation of application parameters
Energy aware routing protocols



proactive/reactive
location aware
low cost spanning tree
Predicting Energy Consumption of
MPEG Video Playback on Handhelds

Runtime adaptation of application parameters is
promising



Two extremes in adaptation 



react to low remaining energy conditions …
by gracefully degrading application quality
Complete system responsibility
Complete Application responsibility
A collaborative partnership between the operating
system and the individual applications works best.
Need to predict energy use as a function of
independent controllable parameters.
Target Application

MPEG video playback


decode and display
Controllable parameters





frame rate
display size
capture size
frame type
spatial resolution
Experimental Setup
Video Sequences
Video Sequence
Description
Video-1
A rotating torus.
Video-2
Beginning of Legendary Life.
Video-3
Beginning of Bounce.
Video-4
A person talking on a phone (from Bounce).
Video-5
Captured from Bounce.
Video-6
Captured from Legendary Life.
Video-7
Two persons talking (from Legendary Life).
Video-8
Captured from Bounce.
Current Consumption by Display
Size
Energy Use by Frame Rate
Frame rate (frames/s)
Energy rate (Joules/sample period)
Video-2
Video-3
Video-6
1.9
0.76
-
-
2
-
0.89
0.89
2.8
-
1
-
2.9
0.95
-
-
3
0.95
-
1.04
3.6
-
1.23
-
3.7
-
-
1.2
3.8
-
1.22
-
4
1.11
-
1.21
4.5
-
-
1.42
4.6
-
1.37
0
Energy Use by Frame Rate
(Cont’d)
Frame rate (frames/s)
Energy rate (Joules/sample period)
Video-2
Video-3
Video-6
4.7
-
1.37
1.42
4.8
1.2
1.38
-
5
-
-
1.74
5.2
-
-
1.70
5.3
-
-
1.70
5,6
1.32
-
-
5.7
1.33
-
-
5.8
-
1.50
-
5.9
-
1.50
-
6.3
-
1.49
-
Energy Consumption by
Capture Size
Capture size (pixel2)
Energy used (Joules)
Video-2
Video-5
Video-6
19200
436.25
391.04
-
76800
1481.09
1184.25
1706.06
84480
-
-
1836.62
100800
1830.31
-
-
115200
-
-
2105.63
168960
-
2878.39
2967.74
172800
2192.26
2773.71
3148.15
153600
2171.47
2307.48
2782.37
230400
-
3200.81
3767.95
307200
-
3811.72
4477.85
345600
3144.51
4074.90
4855.33
Energy Consumption by
Capture Bit Rate
capture bit rate
(MB/s)
energy used (Joules)
Video-4
Video-7
Video-8
0.4
-
653.58
747.57
1
1052.56
980.08
1043.91
2
-
1052.23
1166.01
2.99
-
1387.44
1635.93
4.02
1052.56
1463.74
1779.85
4.99
-
1775.91
-
6.01
-
1797.63
1994.32
7.04
-
1920.22
2066.08
8
1052.56
1996.98
2076.28
9.03
-
2052.46
2056.68
9.99
-
-
1941.19
12.04
3146.58
-
-
15
3176.30
-
-
Scatter Plot of I, P, B Energy
Usage
Energy Prediction Models



Need to obtain quantitative prediction
models from measurement data
Run regression as function of relevant
control parameters
Find least-square best-fit polynomial

increase polynomial degree until marginal
improvement in sum of squared error is
small
Best-fit Polynomial for Capture
Size
Best-fit Polynomial for Frame
Rate
Best-fit Polynomial for Capture
Bit Rate
Summary of Energy Models
Best fit Polynomial
condition
R
E=4735.92029r+447.3r2 -33.9r3
d = 480 x 240, b =
2.4Mb/s
0.92
E = -131.13 + 0.0323d
b = 2.4Mb/s, r varies
with d
0.99
d = 480 x 240, r varies
with b
0.95
E=627.07+359.92b31.98b2 +1.32b3
Summary


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Predictive energy models enable
informed energy management
For MPEG video playback, simple
polynomial models possible (R value >
0.92) as function of several control
parameters
Models serve as building block in
energy-aware handheld OS architecture
Presentation Outline





Motivation
Predicting Energy Consumption of MPEG Video Playback on Handhelds

Introduction

Experimental setup

Experimental results

Energy consumption models

Summary
Movement prediction

Observation

Power saving strategy

System model

Heuristics

Simulation results

Summary
Conclusion
Future work
Movement Prediction

Observation: Reduced distance between
communicating peers ⇒ Reduced
transmission power requirement ⇒
Energy saving.



Assuming network interface has
transmission power control capability.
Single hop communication – obvious
Multi hop communication – expected
Power Saving Strategy

If likely to move closer to the target,
postpone communication for a future
time.


Assuming application can tolerate some
delay k.
Needs movement prediction

Based on movement history.
Network Structure
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Mobile nodes are moving within a rectangular plane.
We divide the network into virtual grids.
Each grid has a unique grid ID.
Assumptions
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Each node knows it's position – GPS.
Each mobile host maintains a sequence of n
previous grid IDs.
Initial assumption –

target is fixed.

Every mobile node knows the target’s location.
Relax the fixed target assumption –

Both communicating peers are mobile.
Mobility Model
Defines a stochastic process which tells us how a mobile node
moves in a network.
Random waypoint mobility model


1.
2.
3.
4.
5.
Wait for pause_time seconds
Pick a random new destination
Pick a random velocity
Move steadily to the chosen destination
Upon reaching the destination, repeat the steps 1 through 4
Regular waypoint mobility model


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Introduce regularity
Home – work – home model with occasional diversions
Choose new destination – not completely randomly
Two parameters –
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Regularity r
Periodicity T
Terminology

History of node h:
Sh = {x1, x2, …, xn}

A window of size l (for i ≤ n-l+1):
W(i,i+l-1) = {xi, xi+1, …, xi+l-1}
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W(i,i+l-1) is a subsequence of Sh.

Distance between two grids i and j: d(i,j).
Binary Distance (BD) Heuristic

Calculate the probability p that a mobile node
will be in grid ID y within the next k time
units as follows:
p  Pr(W (n  1, n  k ) contains y )
 (number of windows in S h of size k containing y ) /( n  k  1)
Communicate immediately if p is less than some
probability threshold pth. Else, postpone
communication.
Problem With BD Heuristic
A
Too coarse granular idea of
distance – Counts only
when the communicating
node is in the same grid as
the target.
A
t
Binary Markov Distance
(BMD) Heuristic


Based on order-m Markov model.
Calculate the probability that a mobile node will be in
grid ID y within the next k time units as follows:
N
Pr( xn 1  y )  
i1 1
N

i2 1
N
  Pr( xn 1  y | xn 1m  i1 , xn  2m  i2 ,, xn  im )
im
Problems:
• Higher computational overhead.
• Same coarse granularity problem as BD.
Markov Distance (MD)
Heuristic
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Let R be the set of all possible routes that can be taken by the
mobile node in the next k time units
Let R1, where R1  R, contain those routes in R that have at
least one location closer to the target than the current distance.
Then, we calculate the probability that a mobile node will move
closer to the target as:
p
( probabilit y of taking the route  )


1R1
1
( probabilit y of taking the route  )


R
If p ≥ pth, then we postpone the communication, else we communicate
immediately.
• Higher computational overhead
• Distinguishes the distance between the node and the target on a finer level
MD Heuristic - Example
Consider three possible paths of
node A:
• ρ1 moves closer to the target in
the next two time steps.
• ρ2 and ρ3 do not move closer to
the target in the next two time
steps.
• If, these were the only options
and A takes any of these paths with
equal probability, then the
probability that A will move closer
to the target is: 1/3.
3
A
2
1
t
Average Distance (AD)
Heuristic

Calculate the average distance between a mobile node
and the target over all windows of size k in the
mobile node's movement history as:
avg 
n  k 1
1
k
j  k 1
  d ( x , y)
j 1
i j
i
If the current distance between the mobile node and the target
is greater than avg, then the mobile node decides to postpone
the communication, or else it communicates immediately.
• Less Computational overhead
• Takes into consideration the actual distance
Analogy With Secretary
Problem

Secretary problem: one must make an
irrevocable choice from a number of applicants whose
values are revealed only sequentially.

Our problem: we must choose one time step when a
node communicates and once it communicates it is
done.

Solutions to the secretary problem might help
designing solutions to our problem.
37% Rule and The Least
Distance (LD) Heuristic

Best-choice(r) Algorithm: reject the first r-1
candidates. Then accept the next candidate whose
relative rank is 1 among the candidates seen till now.


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Accepts the best candidate with probability 1/e ≈ 0.368.
Optimal solution.
Choose the time when the distance is the minimum
seen till now.
LD Heuristic: find Minimum as:
d min  Min d ( x, y)
 xS h
Postpone communication if current distance is
greater that dmin, else communicate immediately.
Single Threshold Solution

Select the first candidate whose value
exceeds a pre-specified threshold value.


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Applicable only to the full information problem.
Parameters can be estimated from partial
observation.
Average Distance heuristic – threshold is the
average seen till now.
One-bounce Rule


Keep checking values as long as they
go up. As soon as they go down we stop
postponing any more and take the
current value.
postpone as long as the distance between the
mobile host and the target is decreasing, and
communicate as soon as the distance starts
increasing.


Ignores the history other than the last value.
Use this idea along with AD heuristic.
Use of One Bounce Rule
If a node is moving away from
the target, average keeps
decreasing at each time step
and finally we choose the
worst alternative.
A
A
t
Solution: Directional Average Distance Heuristic
• Take direction of movement into consideration.
• If at any point of time, moving away from the
target, communicate immediately.
Moving Target

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Simple modifications to the heuristics proposed works
for moving target.
Assume a mobile host s with location history Ss =
{x1, x2, …, xn} wants to communicate with node r
with location history Sr = {y1, y2, …, yn}.
MD heuristic: just define R and R1 with respect to Sr
instead of y.
AD heuristic: Define average as:
avg 
n  k 1

j 1

1
k
j  k 1
 d (x , y )
i j
i
i
LD heuristic: define minimum distance as:
d min  Min d ( x, y)
 xS h
Preliminary Experiments

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Number of Grids: 3 x 3
Cost of single communication C(d) for
distance d is d2.
10000 repetitions.
Target Location: Randomly chosen for
each run.
Performance of BD Heuristic
• Poor performance.
Performance of BMD Heuristic
Performance of MD Heuristic
• With random waypoint mobility model.
Performance of MD Heuristic
• With regular waypoint mobility model.
Performance of AD Heuristic
Performance Comparison of
BD, BMD, MD and AD.
Simulation Experiments
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Network size: 1500m x 1500m
Number of Grids: 3 x 3
Number of nodes: 20
Maximum speed: 10 m/s
Simulation time: 20000 seconds
Routing protocol: DSR
Propagation model: Two-ray ground.
Target Location: fixed at the center of the
network.
Performance of MD Heuristic for
Varying Probability Threshold
Performance of MD Heuristic
for Varying Regularity
Performance of AD Heuristic
for Varying k
Performance of AD and LD
Heuristics for Varying Regularity
Performance of LD Heuristic
for Varying k
Result for Mobile Target –
Single hop Communication
Result for Mobile Target –
Multi hop Communication
Observed Delay vs. Maximum
Allowable Delay
• We get higher energy saving by setting k higher, but without
increasing the observed delay significantly.
Energy Consumption Due to
CPU Processing
Comparison Among Heuristics
Summary



Our strategy predicts a good time for
communication, when some amount of
delay is tolerable.
We postpone the communication until
that point and then communicate.
Simulation results show significant
energy saving.
Conclusion




Wireless networking is rapidly emerging as the future
communication technology
The components of an ad hoc network are mostly
battery-powered handheld devices.
Limited battery life is an important issue in wireless
networking.
We address two issues:

Predicting energy consumption as a function of adaptable
parameters.


We show that simple polynomial models can predict effectively.
Exploiting node mobility in an ad hoc network to conserve
energy.

We can save more that 50% of the communication cost.
Future Work




Dynamic adaptation of system parameters.
Location information for moving target in ad
hoc networks.
Considering transmission duration in
predicting a good time for communication.
Optimal way to divide the network into grids.
Thank you!