1. QoS Routing using Clustering with Interference Considerations Admission Control Motivation Simulation  We study QoS Routing using clustering with interference considerations.  We focus on the cost of decoupling the computation to clusters. Future Work  Analysis for the cost of decoupling the routing to per cluster computations  Intercluster Routing  OSPF  What information to forward to the next cluster  Timing and mobility effects in simulation  Introduces inconsistency which makes global calculation infeasible  Rerouting  Measurement  To refine/estimate clique constraints  For admission control Intercluster Routing  Limit 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 hop  Each cluster calculates the intercluster route using OSPF and its current view of the intercluster network topology  Intracluster routing to reach the next cluster  Forward the request to the next cluster. Current Cluster More Frequent Less Frequent Intracluster Routing Routing Strategies  OSPF: Weight on link j is 1/C+ max{  U i } where  C is the speed of link j  U i is the utilization of link i  Link j belongs to a set of cliques for which each has constraint  U i of which max{  U i } is the largest  Integer Linear Program: Uses clique constraints Full Clique Constraint U 12 +U 21 +U 13 +U 31 +…+U 56 +U 65  1 Even Decomposition U 12 +U 13 +U 31 +U 32 +U 34 +U 35 +U 53 +U 54 +U 56  1/3 U 21 +U 23 +U 24 +U 42 +U 43 +U 45 +U 46  1/3 U 64 +U 65  1/3 Proportional Decomposition U 12 +U 13 +U 31 +U 32 +U 34 +U 35 +U 53 +U 54 +U 56  1/2 U 21 +U 23 +U 24 +U 42 +U 43 +U 45 +U 46  7/18 U 64 +U 65  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 constraints  Measurement Run trial flow with same characteristics for T seconds Trial packets served with low priority Accept flow if all links able to serve trial packets Admitted Trial high Admission Network Utilization Medium/1High/1Medium/2High/2 OSPF Global53.347.367.3 Per cluster global54.040.054.747.3 Even Decomposition50.046.748.749.3 Proportional Decomposition48.740.050.0 Integer Linear Program Global57.349.372.758.7 Per cluster global57.353.571.358.7 Even Decomposition28.7 32.727.7 Proportional Decomposition35.326.034.032.0 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 @eecs.berkeley.edu University of California at Berkeley 1. Clustering 2. Intercluster next hop 3. Intracluster Routing 4. Reservation and Forward Source Dest Links Gateway Node 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 Frequently  Actual Clustering: Updated from Prospective Clustering when better used for Routing  Initially, 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 heads Wait a random time