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

Ahmed Helmy, USC 6

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

Ahmed Helmy, USC 7

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

Ahmed Helmy, USC 8

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

Ahmed Helmy, USC 11

Intra-domain M&M for Micro Mobility

AR1

AR2

BR

Internet

AP

M&M

BR: Border RouterAR: Access RouterAP: Access Point

Ahmed Helmy, USC 12

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]

Ahmed Helmy, USC 14

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.

Ahmed Helmy, USC 15

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.

Ahmed Helmy, USC 16

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

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

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.

Ahmed Helmy, USC 22

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

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

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

Ahmed Helmy, USC 25

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

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

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.

Ahmed Helmy, USC 28

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

Ahmed Helmy, USC 29

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

Ahmed Helmy, USC 30

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

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