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State Analysis and Aggregation for Multicast-based Micro Mobility

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. Outline. Motivation M ulticast-based M obility (M&M) Intra-domain M&M for micro-mobility

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State Analysis and Aggregation for Multicast-based Micro Mobility

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  1. 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 Ahmed Helmy, USC

  2. 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 Ahmed Helmy, USC

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

  4. Multicast-based Mobility (M&M): Architectural Concept (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. [A. Helmy, “A Multicast-based Protocol for IP Mobility Support”, ACM NGC ‘00] Ahmed Helmy, USC

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

  6. 35000 A+B, Random 30000 A+B, Neighbor 25000 A+B, Cluster 20000 Number of links Average 15000 10000 5000 0 AS r 50 r 100 r 150 r 200 r 250 ARPA ts 50 ts 100 ts 150 ts 200 ts 250 ts 300 ti 1000 ti 5000 ts 1000 Mbone_1 Mbone_2 ts 1008_1 ts 1008_2 ts 1008_3 Topology 16000 C, Random 14000 C, Neighbor 12000 C, Cluster 10000 Number of links Average 8000 6000 4000 2000 0 AS r 50 r 100 r 150 r 200 r 250 ARPA ts 50 ts 100 ts 150 ts 200 ts 250 ts 300 ti 1000 ti 5000 ts 1000 Mbone_1 Mbone_2 ts 1008_1 ts 1008_2 ts 1008_3 Topology Overall Network Overhead Total links traversed. (A + B) /  C = 1.8 Ahmed Helmy, USC

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

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

  9. 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 Ahmed Helmy, USC

  10. 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 Ahmed Helmy, USC

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

  12. Mobility-proxy Based Architecture (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 Event sequence as the mobile node moves into a domain Ahmed Helmy, USC

  13. 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] Ahmed Helmy, USC

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

  15. M&M vs. Seamless Handoff Previous location, or Seamless handoff (SH) SH/L for various topologies and movements 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. Ahmed Helmy, USC

  16. 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. Ahmed Helmy, USC

  17. 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 Ahmed Helmy, USC

  18. 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 Ahmed Helmy, USC

  19. 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. Ahmed Helmy, USC

  20. 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 Ahmed Helmy, USC

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

  22. Prefix Prefix vs. Bit-wise Aggregation 100 10 Aggregation Ratio Bitwise 1 0 100 200 300 400 500 600 700 800 900 Number of MNs Aggregation ratio for random numbers. Bit-wise aggregation outperforms prefix aggregation up to 80% of the number population. Ahmed Helmy, USC

  23. 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 Ahmed Helmy, USC

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

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

  26. 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 Ahmed Helmy, USC

  27. 1000 100 State 10 Time 1 0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 Node ID State distribution without 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. State distribution with lossy aggregation Ahmed Helmy, USC

  28. 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 Number of states: Overall average and 90th percentile (w/o agg: without aggregation, w/ agg: with aggregation) • How does aggregation change with # BRs and network routers Ahmed Helmy, USC

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

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

  31. 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 Ahmed Helmy, USC

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