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A Practical Approach to QoS
Routing for Wireless Networks
Teresa Tung, Zhanfeng Jia, Jean Walrand
WiOpt 2005—Riva Del Garda
Outline
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Problem: clustering
Assumptions: routing algorithm
Analysis: simple models
Analysis: simulations
Scenario
Routing over ad-hoc wireless networks
Goal: Discover the diverse paths
• Small area, use shortest path
• Uniform demand, shortest path admits
most flows
• Demand between few s-d pairs, use
diverse paths to increase capacity
Observation on Interference
• Interference
– Area effect
– Not a link effect
• Routing choices
– Over areas
– Not over links
Tx
Intfx
Related Work
Theoretical Approach
• Gupta Kumar
• Thiran
Practical
• Fixed transmission radius
• Routing algorithms
Clustering: Motivation
Clustering makes sense for dense networks
Each node sees roughly the same info
Clustering: Motivation
Clustering makes sense for dense networks
Each node sees roughly the same info
Clustering: Motivation
Clustering makes sense for dense networks
Each node sees roughly the same info
Clustering: Motivation
Clustering makes sense for dense networks
Each node sees roughly the same info
Costs
• Cost of flat routing
– No point in all nodes reporting
– Reduction in control messages
– Limited loss of information
• Cost of clustering
– Restrict possible paths
– Use more network resources
Outline
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Problem: clustering
Assumptions: routing algorithm
Analysis: simple models
Analysis: simulations
Routing granularity
• Comparison of routing strategies over a
flat network shows little improvement
• Scheme
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Shortest path within clusters
OSPF at the cluster level
Measurement
Admission Control
Routing
Source
Dest
Routing
Routing: Measurement
Measure the available resources in a cluster
• Use a representative node per cluster
• Given the link speed
• Measure the fraction of time that the
channel is busy
– Transmitting/Receiving
– Channel busy
• The fraction of idle time x link speed gives
an upper bound on residual capacity
Routing: Admission Control
For inelastic flows require a rate F
• Trial flow of same rate F for period t
• Trial packets served with lower priority
• Admit if all trial packets received
• Otherwise busy
Admitted
Trial
high
802.11e
Routing Assumptions
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Shortest path within clusters
Resource estimates via measurements
OSPF based scheme at the cluster level
Admission control
Outline
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Problem: clustering
Assumptions: routing algorithm
Analysis: simple models
Analysis: simulations
Clustering: Analysis Model
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Continuous plane (dense network)
Compare routes over an idle network
Grid clustered
Compare
– Length
– Self interference
– Diversity
Clustering: Length
Compare # hops
Path length: grid size
Path length: grid = 2r
Clustering: Self-Interference
• Unit disk model, interference radius
• Self-interference for shortest path
Clustering: Self-Interference
Midpoint on II
– From II
– From I and III each
Decreasing in grid size
Clustering: path diversity
Cost of Flat Routing
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N nodes over area A=ar x ar where r tx radius
C=(a/g)^2 clusters of size gr x gr
Average hops between nodes L
Average hops across cluster < gsqrt2
• Flat routing LN2
• Clustered routing (gc1+c2L)C2
Outline
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Problem: clustering
Assumptions: routing algorithm
Analysis: simple models
Analysis: simulations
Outline
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Problem
Argument for clustering
Routing scheme
Simulation results
Simulations
• Matlab
Algorithms
• Global OSPF
• Event driven OSPF
• Event+clustered OSPF
100 nodes, vary density
• Mesh topology (5x5)
• Random topology
(3x3,4x4)
Clustering: Shortest Path
Simulations: Admission Ratio
Mesh over a 5x5 Grid
Random over a 3x3 Grid
Simulations: Max capacity s-d
Mesh over a 5x5 Grid
Random over a 3x3 Grid
Simulations: Average path length
Mesh over a 5x5 Grid
Random over a 3x3 Grid
Simulations: Path length for
fixed s-d pair
Simulations: Path Diversity
Simulations: ave # routes s-d
Mesh over a 5x5 Grid
Random over a 3x3 Grid
Conclusion
Cost of clustering: 20% loss in admit ratio
• Path length
• Self-interference
• Path diversity
www-inst.eecs.berkeley.edu/~teresat
[email protected]
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