qos routing using clustering with interference considerations admission control motivation...
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QoS Routing using Clustering with Interference Considerations
Admission Control
Motivation SimulationWe study QoS Routing using clustering with interference considerations. We focus on the cost of decoupling the computation to clusters.
Future WorkAnalysis for the cost of decoupling the routing to per cluster computationsIntercluster Routing
OSPFWhat information to forward to the next cluster
Timing and mobility effects in simulationIntroduces inconsistency which makes global calculation infeasibleRerouting
MeasurementTo refine/estimate clique constraintsFor admission control
Intercluster RoutingLimit the propagation of cross cluster information using Fisheye strategy
Nearby clusters exchange link state information more frequently. Local information is more accurate.Each cluster has its own view of the intercluster topology.
OSPF at intercluster level per cluster hopEach cluster calculates the intercluster route using OSPF and its current view of the intercluster network topologyIntracluster routing to reach the next clusterForward the request to the next cluster.
Current Cluster More Frequent Less Frequent
Intracluster Routing
Routing StrategiesOSPF: Weight on link j is 1/C+max{Ui} where
C is the speed of link jUi is the utilization of link i Link j belongs to a set of cliques for which each has constraint Ui of which max{Ui} is the largest
Integer Linear Program: Uses clique constraints
Full Clique Constraint U12+U21+U13+U31+…+U56+U65 1
Even DecompositionU12+U13+U31+U32+U34+U35+U53+U54+U56 1/3U21+U23+U24+U42+U43+U45+U46 1/3U64+U65 1/3Proportional DecompositionU12+U13+U31+U32+U34+U35+U53+U54+U56 1/2U21+U23+U24+U42+U43+U45+U46 7/18U64+U65 1/9
Decomposition
1
2
3
4
5
6
Bidirectional Links Color Clusterid
Nodeid
Network Graph all links in the same clique
Clustering
By checking clique constraintsMeasurement
Run trial flow with same characteristics for T secondsTrial packets served with low priorityAccept flow if all links able to serve trial packets
Admitted
Trial
high
Admission
Network Utilization
Medium/1
High/1 Medium/2
High/2
OSPF
Global 53.3 47.3 67.3 67.3
Per cluster global 54.0 40.0 54.7 47.3
Even Decomposition 50.0 46.7 48.7 49.3
Proportional Decomposition
48.7 40.0 50.0 50.0
Integer Linear Program
Global 57.3 49.3 72.7 58.7
Per cluster global 57.3 53.5 71.3 58.7
Even Decomposition 28.7 28.7 32.7 27.7
Proportional Decomposition
35.3 26.0 34.0 32.0
1. Dimakis, He, Musacchio, So, Tung, Walrand. “Adaptive Quality of Service for a Mobile Ad-Hoc Network” MWCN October 2003.
2. Pei, Gerla, Chen. “Fisheye State Routing in Mobile Ad-Hoc Networks” ICDCS Workshop on Wireless Networks and Mobile Computing 2000.
1
2
Medium: Average 5 flows active per timeHigh: Average 10 flows active per time
Routing Strategy
Load/Topology
Eric Chi, Antonis Dimakis, Zhangfeng Jia, Teresa Tung, Jean Walrand<echi, dimakis, jia, teresat, wlr>@eecs.berkeley.edu
University of California at Berkeley
1. Clustering1. Clustering
2. Intercluster next hop
3. Intracluster Routing
4. Reservation and Forward
Source
Dest
LinksGatewayNode
Topology 1 Topology 2
Minimize the number of interfering links outside of a cluster subject to a constraint on cluster size.Damped Clustering
Prospective Clustering: Updated FrequentlyActual Clustering: Updated from Prospective Clustering when better used for RoutingInitially, each node is its own prospective and actual cluster
Prospective Clustering Algorithm executed per node
Toss a coin heads with probability
P
Randomly assign clusterid from set
of clusterids of neighboring nodes
Node becomes a new cluster
tails
headsWait a random
time