Roadmap-Based End-to-End Traffic Engineeringfor Multi-hop Wireless Networks

Mustafa O. KilavuzAhmet SoranMurat Yuksel

University of Nevada Reno

Outline

• Introduction• Framework• Simulation• Results• Conclusion and future work

Introduction

Motivation

• Why load balance the traffic (i.e., traffic engineering) in multi-hop wireless networks?– Mitigate hotspots– Attain higher throughput (aggregate throughput is

maxed)– Lifetime of the network (load on nodes/routers is

evenly distributed)

Desired Properties

• Flexible: End-to-end route selection capability (like MPLS)– Source application can control paths the traffic

takes• Scalable: Do not want to store – Global topology information– Flow state

• Can we achieve both by in a feasible and scalable manner?

Flexibility: Source-Based E2E Trajectories• Defining E2E paths require topology info – hard to get• Idea: Decouple the E2E path from the underlying topology,

control plane costs could be reduced significantly!

Ideal Trajectory

Approximate Trajectory

Actual Trajectory

Void Area

Void AreaVoid Area

Trajectory-Based Forwarding (TBF)

Source

Destination

Data

D. Niculescu B. Nath

Scalability: Roadmaps

• Need to summarize the congestion state of the global network – hard to gather

• Idea: Use the adaptive roadmaps concept from robotics

S. Bhattacharya, et al

Current Schemes

• Mostly shortest path– Greedy– Not suitable for load balancing– E.g. GPSR

• Mostly topology dependent– Not scalable against network changes/dynamics– E.g. DSR

Overall Framework

Routing Framework with Roadmap

Roadmap Trajectory Approximator

Application-Specific Constraint(e.g., path accuracy, max delay)

send(dest, data, constraint)

Network

Packets with approximate trajectory to the network

send(dest, apprx_traj, data)

Congestion indications as link weight updates

to the roadmap

Shortest path on the roadmap as ideal trajectory

Path

Sel

ectio

n fo

r E2E

TE

at R

outin

g La

yer

Void Area

Void AreaVoid Area

Building the Roadmap

Generating Ideal Trajectory

Void Area

Void AreaVoid Area

SourceDestination

Feedback: Void Areas

Void Area

Void AreaVoid Area

SourceDestination

Data Feedback

Feedback: Congested Areas• Congestion causes

packet drops• Broadcast feedback– High priority– Small size

• 50% probability to reroute

Load Balancing• Roadmap edge weights are increased as they

are being used.• Unused edges’ weights are gradually

decreased.• Change trajectory after sending n packets

over it.

Simulation

Simulation Setup

• Goal: Maximum throughput• TBR vs. Greedy Perimeter Stateless Routing (GPSR)• Why GPSR?– Similar properties with TBR

• Geographic• Scalable• Topology-independent

– Good reference for benchmarking• Shortest path• No end-to-end

Void Area

Void AreaVoid Area

Greedy Perimeter Stateless Routing (GPSR)

Source

Destination

Greedy Forwarding

Perimeter Forwarding

Greedy Forwarding

B. Karp, H. Kung

Simulation Setup

• Field size 1500 x 1500 pixel2

• Wireless node range 150 pixels• Runtime 20s• Traffic rate 160 Kbps

• Network density 10, 15, 20, 25– Number of nodes 114, 171, 229, 286

• Number of traffic flows 3, 5, 10• Packet queue size 5, 10, …, 50

• Reruns 16

Simulation: Trajectories

Source

Source

Source

Destination

Destination

Destination

Simulation: Roadmap

Simulation: Roadmap

Results

Work Load Heat Map

GPSR Roadmap based TBR

Throughput5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50

10 15 20 25 10 15 20 25 10 15 20 253 5 10

0

10

20

30

40

50

60

70

80

90

100GPSRTBR

Ave

rage

Pac

ket D

eliv

ery

Rate

(%)

Q

D

F

Q – Packet queue size of nodesD – Network density (Average number of neighbors)F – Number of traffic flows (Source – destination pairs)

TBR has higher throughput overall

GPSR has good throughput on sparse networks

High number of flows increases congestion, reduces throughput

High queue size increases throughput

Hop Count

Q

D

F

5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50

10 15 20 25 10 15 20 25 10 15 20 253 5 10

0

2

4

6

8

10

12

14

16

18GPSR

TBR

Hop

Cou

nt

Q – Packet queue size of nodesD – Network density (Average number of neighbors)F – Number of traffic flows (Source – destination pairs)

TBR has longer routes to avoid congestion and to do load balancing

Network density is not a major factor but causes GPSR spikes because of

perimeter mode

Packet Delay

Q

D

F

5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50

10 15 20 25 10 15 20 25 10 15 20 253 5 10

0

2000000

4000000

6000000

8000000

10000000

12000000

14000000

16000000GPSRTBR

A

vera

ge P

acke

t Del

iver

y Ti

me

(s)

Q – Packet queue size of nodesD – Network density (Average number of neighbors)F – Number of traffic flows (Source – destination pairs)

TBR packet delay increases within acceptable amounts

Large queue size causes more delay

Conclusion and Future Work

Conclusion

• Mobile scenarios• Algorithms optimization• Improvements to roadmaps– Construction (regular patterns)– Better methods for ideal trajectory– Local vs. global

Questions & Answers

Backup Slides

Void Area

Void AreaVoid Area

Trajectory-Based Routing (TBR)

Data

Source

Destination

M. Yuksel et al.

IdealTrajectory

ApproximateTrajectory

Special IntermediateNode (SIN)

Work Load distribution

Q

D

F

5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50

10 15 20 25 10 15 20 25 10 15 20 253 5 10

0

50

100

150

200

250

300GPSRTBR

W

ork

Load

(Sta

ndar

d D

evia

tion)

Q – Packet queue size of nodesD – Network density (Average number of neighbors)F – Number of traffic flows (Source – destination pairs)

TBR distributes load better

Load is more balanced in dense networks

High number of flows puts more load on central nodes

Contributions

• The concept of minimizing routing state under application-based constraints.

• Formulation of the trajectory approximation problem minimizing the routing state.

• Proof that the trajectory approximation problem is NP-hard.

• Solutions to solve the trajectory approximation problem.• Customized the trajectory approximation problem for

power-scarce networks.• A roadmap-based mechanism for end-to-end traffic

engineering for multi-hop wireless networks.

Roadmap Simulations

Source Node

Destination Node

Void Area

Data Packet

Approximate Trajectory

Roadmap Simulations

Roadmap Edges

Ideal Trajectory

Roadmap Vertices

Source Node

Destination Node

Approximate Trajectory

Roadmap Simulations

Roadmap Simulations

Routing Protocols• Destination-Sequenced Distance Vector (DSDV)• Ad hoc On Demand Distance Vector (AODV)• Greedy Perimeter Stateless Routing (GPSR)• Distance Routing Effect Algorithm for Mobility (DREAM)• Dynamic Source Routing (DSR)• Trajectory-Based Forwarding (TBF)• Trajectory-Based Routing (TBR)• Roadmaps in robotics

Destination-Sequenced Distance Vector (DSDV)

2 23 34 35 36 37 3

1

2

3

4

6

7

51 42 43 44 46 47 7

1 62 63 64 66 65 5

1 42 43 44 45 47 7

1 32 23 35 56 67 5

1 13 44 45 46 47 4

1 12 43 35 46 47 4

Destination

Next hop

2 23 34 35 36 37 3

1 12 43 35 46 47 4

1 32 23 35 56 67 5

1 42 43 44 46 47 7

Source

Destination

Routing table

Ad hoc On Demand Distance Vector (AODV)

1

2

3

4

6

7

5RREQ

RREQ

RREQ

RREQ

RREQ

RREQ RREQ

RREQSource

Destination

RREP

RREP

RREP

5 2

5 4

5 5

Distance Routing Effect Algorithm for Mobility (DREAM)

Source

Destination

Dynamic Source Routing (DSR)

1

2

3

4

6

7

51

1

1 | 2

1 | 3

1 | 2 | 4

1 | 2 | 4 1 | 2 | 4 | 6

1 | 2 | 4 | 6 | 7Source

Destination

1 | 2 | 4 | 51 | 2 | 4 | 5

1 | 2 | 4 | 51 | 2 | 4 | Data

1 | 2 | 4 | Data1 | 2 | 4 | Data1 | 2 | 4 | 6 | 7 | 5

1 | 2 | 4 | 6 | 7 | 5

1 | 2 | 4 | 6 | 7 | 5

Comparison

DSDV AODV GPSR DREAM DSR TBR

Flexibility

Scalability (State)

Scalability (Messaging)

Reachability

Computation

Type Proactive Reactive Reactive Reactive Reactive Reactive

Cost Comparison

Exhaustive Search Genetic Algorithm Heuristic 1 Heuristic 2

10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 1800

200

400

600

800

1000

1200

1400

1600

1800

Complexity of the Trajectory (Degrees)

Aggr

egat

e Co

st (B

ytes

)Error tolerance: 5%

GA performs pretty close to the exhaustive search

Longest representation heuristic is not bad

Exhaustive Search

Equal error heuristic did not do well

Equal Error Longest Representation

M. Kilavuz et. al. Minimizing multi-hop wireless routing state under application-based accuracy constraints, MASS 2008

Exhaustive Search Genetic Algorithm Heuristic 1 Heuristic 2Equal Error Longest Representation

Time Comparison

10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 1800.001

0.01

0.1

1

10

100

1000

10000

100000

Complexity of the Trajectory (Degrees)

Com

puta

tion

Tim

e (S

econ

ds)

Equal Error heuristic runs in no time

Exhaustive search takes too much time

These run in reasonable amount of time

Error tolerance: 5%

M. Kilavuz et. al. Minimizing multi-hop wireless routing state under application-based accuracy constraints, MASS 2008