1. Ahmed Helmy, USC1 State Analysis and Aggregation for Multicast-based Micro Mobility Ahmed Helmy Electrical Engineering Department University of Southern California helmy@usc.edu http://ceng.usc.edu/~helmy

2. Ahmed Helmy, USC2 Outline Motivation –Multicast-based Mobility (M&M) Intra-domain M&M for micro-mobility Scalability Issues and State Aggregation Approaches to State Aggregation –prefix vs. bit-wise –perfect vs. leaky Performance Analysis Conclusions

3. Ahmed Helmy, USC3 Home Agent (HA) Correspondent Node (CN) Mobile Node (MN) Mobile IP - Triangle Routing A B C

4. Ahmed Helmy, USC4 (a) All locations visited by the mobile are part of the distribution tree (at some point) (b) When a mobile moves, only the new location becomes part of the tree - When the mobile moves to a new location, as in (c) and (d) the distribution tree changes to deliver packets to the new location. Multicast-based Mobility (M&M): Architectural Concept [A. Helmy, “A Multicast-based Protocol for IP Mobility Support”, ACM NGC ‘00]

5. Ahmed Helmy, USC5 Join/Prune dynamics to modify distribution CN CN: Correspondent node (sender) Wireless link Mobile Node

6. Ahmed Helmy, USC6 Total links traversed.  (A + B) /  C = 1.8 Overall Network Overhead

7. Ahmed Helmy, USC7 1 1.5 2 2.5 3 3.5 4 4.5 5 Topologies (b)Neighbor movement RandomTransit-stubTiersArpaMboneAS 1 1.5 2 2.5 3 3.5 4 4.5 5 Topologies (a) Random movement Ratio r=(A+B)/C Mean 90th percentile RandomTransit-stubTiersArpaMboneAS Mean 90th percentile 1 1.5 2 2.5 3 3.5 4 4.5 5 Topologies (c)Cluster movement Ratio 'r' Mean 90th percentile RandomTransit-stubTiersArpaMboneAS Ratio ‘r = (A+B)/C’. Average ‘r = 2.11’. End-to-end Delay

8. Ahmed Helmy, USC8 Average B/L, C/L and P/L ratios Handoff Latency Ratios

9. Ahmed Helmy, USC9 Conclusion M&M re-uses many existing multicast mechanisms (simple join/prune) Extensive simulations show that on average –M&M incurs ~1/2 network overhead as MIP –M&M incurs 1/2 end-to-end delay as MIP –M&M incurs less than 1/2 handoff delay as MIP M&M outperforms MIP, RO, Seamless HO

10. Ahmed Helmy, USC10 Problems with Inter-domain M&M Requires deployment of inter-domain multicast Needs global multicast address allocation State overhead of the multicast tree Need a new, more practical, approach –M&M for intra-domain micro-mobility

11. Ahmed Helmy, USC11 Intra-domain M&M for Micro Mobility M&M BR: Border Router AR: Access Router AP: Access Point

12. Ahmed Helmy, USC12 Mobility-proxy Based Architecture Event sequence as the mobile node moves into a domain (1) Mobile contacts access router (AR) (2) AR sends request to mobility proxy (MP) (3.a) MP performs inter-domain mobility handoff (3.b) MP sends reply to AR with the assigned multicast address

13. Ahmed Helmy, USC13 Mobility Proxy Mechanisms MP is dynamically elected and updated (similar to the PIM-SM RP bootstrap problem) MP keeps mapping for each visiting MN Another approach is to use algorithmic mapping [on-going work]

14. Ahmed Helmy, USC14 Micro Mobility Performance Evaluation and Comparison Topologies: L for various topologies and movements Average # added links: - 2.48; Random Mov - 1.28; Nbr Mov - 1.91; Cluster Mov - 1.89; Overall Av.

15. Ahmed Helmy, USC15 M&M vs. Seamless Handoff SH/L for various topologies and movements Previous location, or Seamless handoff (SH) Average SH/L ratio (all topos): - 1.47; Random Mov - 0.84; Nbr Mov - 1.38; Cluster Mov - 1.23; Overall Av. Average SH/L ratio (w/o rand topos): - 1.77; Random Mov - 1.01; Nbr Mov - 1.62; Cluster Mov - 1.47; Overall Av.

16. Ahmed Helmy, USC16 M&M vs. Hierarchical MIP Hierarchical MIP of Foreign Agents (FA) FA/L for various topologies and movements Average FA/L ratio (all topos): - 1.51; Random Mov - 3.15; Nbr Mov - 2.06; Cluster Mov - 2.24; Overall Av. Average SH/L ratio (w/o rand topos): - 1.82; Random Mov - 4.61; Nbr Mov - 2.78; Cluster Mov - 3.07; Overall Av.

17. Ahmed Helmy, USC17 Comparison Summary 1080 Simulations (10 per mov/topo/protocol) In more than 94% of the scenarios M&M outperformed hierarchical and seamless handoff approaches w/o r: without random topologies

18. Ahmed Helmy, USC18 Scalability Issues Scalability of multicast state is still an issue Unlike unicast, multicast is location independent. Multicast addresses are not readily aggregatable. Aggregation may not be as intuitive as in unicast Need a deeper look into multicast aggregation in our architecture

19. Ahmed Helmy, USC19 Aggregation Techniques Prefix Aggregation: –128.125.50.2 and 128.125.50.3 can be aggregated as one entry as 128.125.50.2/31, where 31 is the mask length Bit-wise Aggregation: –128.125.0.2 and 128.125.1.2 may be aggregated as 128.12.0.2\9, where 9 is the position of the aggregated bit.

20. Ahmed Helmy, USC20 Aggregation Techs. (contd) Intuitively bit-wise aggregation gives more chances to aggregate Deeper look: –sequence of {0,4,1,2,3} leads to 3 states with bit-wise, whereas with Prefix it leads to 2 states Leaky vs Perfect aggregation –mcast state {S,G,iif, oiflist} or sparse mode {*,G, RP-iff, oiflist} –leaky does not compare the oiflist

21. Ahmed Helmy, USC21 Multicast State Aggregation for M&M Prefix vs. bit-wise Aggregation ratio for in-sequence numbers. Identical gain for bit-wise and prefix aggregation.

22. Ahmed Helmy, USC22 Prefix vs. Bit-wise Aggregation 1 10 100 0 200300400500600700800900 Number of MNs Aggregation Ratio Bitwise Prefix Aggregation ratio for random numbers. Bit-wise aggregation outperforms prefix aggregation up to 80% of the number population.

23. Ahmed Helmy, USC23 Multicast State Analysis Simulations to understand the distribution of state in the nodes and be in a better position to choose the appropriate aggregation using 2 sets of scenarios: –(1) Across space/topology: snapshot of 250k MNs randomly distributed over the topology –(2) Across time: 1000MNs moving 40k moves randomly

24. Ahmed Helmy, USC24 State Distribution Across Topology: Number of states indexed by the node ID after 250k MNs BR

25. Ahmed Helmy, USC25 Simulated 12 topologies: random, transit-stub, and real networks Obtained consistent results and trends in all simulations

26. Ahmed Helmy, USC26 Observations on state distribution across topology Very clear uneven skewed distribution Av. state in routers ~ 10k 80% of nodes had < 10k states ~ 60% of nodes have around 2.5k states (1% of the total number of MNs). Heavy concentration in a small number of nodes

27. Ahmed Helmy, USC27 1 036912151821242730333639424548 Node ID 10 100 1000 State Time State distribution without aggregation State distribution with lossy aggregation 17-20% of nodes hold more than the average number of states 40-60% hold less than 1% of the total number of MNs 66-71% hold less than 2% That is, we observed a very high concentration of states in only a small fraction of the nodes.

28. Ahmed Helmy, USC28 Number of states: Overall average and 90 th percentile (w/o agg: without aggregation, w/ agg: with aggregation) The average aggregation ratio (AR) for the highest 20% of nodes in terms of state was 10.07 (i.e, 90% reduction) AR of 2 (50% reduction) for average number of states How does aggregation change with # BRs and network routers

29. Ahmed Helmy, USC29 Perfect Bit-wise Aggregation Aggregation ratio for perfect aggregation with various topologies and multiple BRs. BRs

30. Ahmed Helmy, USC30 Lossy Bit-wise Aggregation Aggregation ratio for lossy aggregation with various topologies and multiple BRs BRs

31. Ahmed Helmy, USC31 Conclusions Aggregation increases with –decrease in number of BRs –increase in number of MNs –decrease in number of network routers We get better aggregation ratios with concentration of the multicast state The more concentration, the worse the problem, but the more effective the aggregation Bit-wise aggregation can reduce state by 90% in nodes with the highest 20% states