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Ahmed Helmy, USC 1
State Analysis and Aggregation for Multicast-based Micro Mobility
Ahmed Helmy
Electrical Engineering Department
University of Southern Californiahelmy@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
Home Agent (HA)
CorrespondentNode (CN)
Mobile Node (MN)
Mobile IP - Triangle Routing
A B
C
Ahmed Helmy, USC 4
CN
Wireless link
Mobile Node
CN
CN
CN
(a) (b) (c) (d)
(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]
Ahmed Helmy, USC 5
Join/Prune dynamics to modify distribution
CNCN: Correspondent node (sender)
Wireless link
Mobile Node
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0
5000
10000
15000
20000
25000
30000
35000
r 50
r 100
r 150
r 200
r 250
ts 50
ts 10
0
ts 15
0
ts 20
0
ts 25
0
ts 30
0
ts 10
00
ts 10
08_1
ts 10
08_2
ts 10
08_3
ti 100
0
ti 500
0ARPA
Mbo
ne_1
Mbo
ne_2 AS
Topology
Nu
mb
er
of
link
s
A+B, Random
A+B, Neighbor
A+B, Cluster
Average
0
2000
4000
6000
8000
10000
12000
14000
16000
r 50
r 100
r 150
r 200
r 250
ts 50
ts 10
0
ts 15
0
ts 20
0
ts 25
0
ts 30
0
ts 10
00
ts 10
08_1
ts 10
08_2
ts 10
08_3
ti 100
0
ti 500
0ARPA
Mbo
ne_1
Mbo
ne_2 AS
Topology
Nu
mb
er o
f lin
ks
C, Random
C, Neighbor
C, Cluster
Average
Total links traversed. (A + B) / C = 1.8
Overall Network Overhead
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1
1.5
2
2.5
3
3.5
4
4.5
5
Topologies(b) Neighbor movement
Random Transit-stub Tiers Arpa Mbone AS1
1.5
2
2.5
3
3.5
4
4.5
5
Topologies(a) Random movement
Rat
io r
=(A
+B
)/C
Mean
90th percentile
Random Transit-stub Tiers Arpa Mbone AS
Mean
90th percentile
1
1.5
2
2.5
3
3.5
4
4.5
5
Topologies(c) Cluster movement
Rat
io 'r
'
Mean
90th percentile
Random Transit-stubTiers Arpa Mbone AS
Ratio ‘r = (A+B)/C’. Average ‘r = 2.11’.
End-to-end Delay
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0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Random Transit-stub
Tiers ARPA Mbone AS
Topology
B/L
ra
tio
Random
Neighbor
Cluster
0
2
4
6
8
10
12
14
Random Transit-stub Tiers Arpa Mbone AS
Topology
C/L
Ra
tio
Random
Neighbor
Cluster
00.2
0.40.6
0.81
1.2
1.41.6
1.82
Random Transit-stub Tiers Arpa Mbone AS
Topology
P/L
Rat
io
Random
Neighbor
Cluster
Average B/L, C/L and P/L ratios
Handoff Latency Ratios
B/L ratio C/L ratio P/L ratiomin max avg w/o r min max avg w/o r min max avg w/o r1.04 4.32 2.31 2.7 1.05 13.3 3.38 4.11 0.6 1.77 1.28 1.4
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
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Intra-domain M&M for Micro Mobility
AR1
AR2
BR
Internet
AP
M&M
BR: Border RouterAR: Access RouterAP: Access Point
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Mobility-proxy Based Architecture
2.
3.b
MP
MN
AR1
3.a
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
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]
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Micro Mobility Performance Evaluation and Comparison
name nodes links av deg name nodes links av degR-50 50 217 8.68 TS-100 100 185 3.7R-100 100 950 19 TS-150 150 276 3.71R-150 150 2186 29.15 TS-200 200 372 3.72R-200 200 3993 39.93 TS-250 250 463 3.72R-250 250 6210 49.68 TS-300 300 559 3.73TS-50 50 89 3.63 ARPA 47 68 2.89
Topologies:
0
0.5
1
1.5
2
2.5
3
3.5
R-50
R-100
R-150
R-200
R-250
TS-50
TS-100
TS-150
TS-200
TS-250
TS-300
ARPA
Topology
Av
era
ge
Ad
de
d L
ink
s 'L
'
Neighbor
Cluster
Random
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.
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M&M vs. Seamless Handoff
00.20.40.60.8
11.21.41.61.8
2
Topology
Ave
rag
e ra
tio
S
H/L
Random
Neighbor
Cluster
SH/L for various topologies and movements
ARnewARold
Previous location, orSeamless 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.
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M&M vs. Hierarchical MIPHA
FAdomain
FA2
FA1
Hierarchical MIP ofForeign Agents (FA)
0
1
2
3
4
5
6
R-50
R-100
R-150
R-200
R-250
TS-50
TS-100
TS-150
TS-200
TS-250
TS-300
ARPA
Topology
Av
era
ge
ra
tio
FA
/L
Random
Neighbor
Cluster
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
FA/L SH/LAvg Avg w/o r Avg Avg w/o r
2.24 3.07 1.23 1.47
w/o r: without random topologies
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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.
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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
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Multicast State Aggregation for M&M
• Prefix vs. bit-wise
1
10
100
1000
0 100 200 300 400 500 600 700 800 900
Number of Mobile Nodes (MNs)
Ag
gre
gat
ion
Rat
io
Aggregation ratio for in-sequence numbers.
Identical gain for bit-wise and prefix aggregation.
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Prefix vs. Bit-wise Aggregation
1
10
100
0 100 200 300 400 500 600 700 800 900
Number of MNs
Ag
gre
gat
ion
Rat
io
Bitwise
Prefix
Aggregation ratio for random numbers. Bit-wise aggregation outperforms prefix aggregation up to 80% of the number population.
Av. prefix Av. bit-wise Av. bit-wise/prefix
80% population 1.40 1.84 1.32
100% population 2.48 1.98 1.19
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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
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State Distribution Across Topology: Number of states indexed by the node ID after 250k MNs
1E+1
1E+2
1E+3
1E+4
1E+5
1E+6
0 10 20 30 40 50 60 70 80 90
Node ID
Sta
tes
BR
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name nodes links av deg name nodes links av degR-50 50 217 8.68 TS-100 100 185 3.7R-100 100 950 19 TS-150 150 276 3.71R-150 150 2186 29.15 TS-200 200 372 3.72R-200 200 3993 39.93 TS-250 250 463 3.72R-250 250 6210 49.68 TS-300 300 559 3.73TS-50 50 89 3.63 ARPA 47 68 2.89
Simulated 12 topologies: random, transit-stub, and real networks
Obtained consistent results and trends in all simulations
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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
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10 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48
Node ID
10
100
1000
Sta
te
Tim
e
State distribution without aggregation
1
10
100
1000
0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48Node ID
Sta
tes
Tim
e
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.
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Number of states: Overall average and 90th percentile
(w/o agg: without aggregation, w/ agg: with aggregation)
0
10
20
30
40
50
60
70
80
90
Time
Nu
mb
er
of
Sta
tes
90th w/o agg
90th w/ agg
Avg. w/o agg
Avg. w/ agg
•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
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Perfect Bit-wise Aggregation
1
1.1
1.2
1.3
1.4
1.5
12
34 50 100 150 200 250 300
Number of Nodes
Ag
gre
ga
tion
Ra
tio
MPs
Aggregation ratio for perfect aggregation with various topologies
and multiple BRs.
BRs
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Lossy Bit-wise Aggregation
1
1.2
1.4
1.6
1.8
2
12
3450 100 150 200 250 300
Number of Nodes
MPs
Ag
gre
ga
tion
Ra
tio
Aggregation ratio for lossy aggregation with various topologies and multiple BRs
BRs
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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