A Practical Approach to QoS Routing for Wireless

Networks

Teresa Tung, Zhanfeng Jia, Jean Walrand

WiOpt 2005—Riva Del Garda

Outline

• Problem: clustering• Assumptions: routing algorithm• Analysis: simple models• Analysis: simulations

Scenario

Routing over ad-hoc wireless networksGoal: 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

• Problem: clustering• Assumptions: routing algorithm• Analysis: simple models• Analysis: simulations

Routing granularity

• Comparison of routing strategies over a flat network shows little improvement

• Scheme– Shortest path within clusters– OSPF at the cluster level– Measurement– Admission Control

RoutingSource

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

802.11eAdmitted

Trial

high

Routing Assumptions

• Shortest path within clusters• Resource estimates via

measurements • OSPF based scheme at the cluster

level• Admission control

Outline

• Problem: clustering• Assumptions: routing algorithm• Analysis: simple models• Analysis: simulations

Clustering: Analysis Model

• Continuous plane (dense network)• Compare routes over an idle

network• Grid clustered• Compare

– Length– Self interference– Diversity

Compare # hops

Clustering: Length

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

• 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

• Problem: clustering• Assumptions: routing algorithm• Analysis: simple models• Analysis: simulations

Outline

• 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/~teresatteresat@eecs.berkeley.edu