a practical approach to qos routing for wireless networks teresa tung, zhanfeng jia, jean walrand...
Post on 18-Dec-2015
216 views
TRANSCRIPT
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/[email protected]