roadmap-based end-to-end traffic engineering for multi-hop wireless networks mustafa o. kilavuz...
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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