Multi-Variate Analysis of Mobility Models for Network Protocol Performance Evaluation

1
Multi-Variate Analysis of Mobility Models for
Network Protocol Performance Evaluation

• Carey Williamson
• Nayden Markatchev
• carey,nayden_at_cpsc.ucalgary.ca
• University of Calgary

2
Preamble and Motivation

• Consider mobile host movement in an arbitrary
  internetwork
• Can disconnect from one network at any time, move
  to another location, and reconnect, while
  maintaining same identity
• See IETF Mobile IP

3
B
C
A
Example Three different home networks, each
with their own (stationary) router or base
station (A, B, C).
Small circles and triangles represent mobile
hosts. Triangles belong to multicast group G,
while circles do not.
4
B
C
A
Observation 1 Mobile hosts can move anywhere
anytime.
5
B
C
A
6
B
C
A
7
B
C
A
8
B
C
A
Mobile Host (MH) registers with Foreign Agent
(FA) at the visited network, and with its Home
Agent (HA) as well to enable packet forwarding
(via tunneling).
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Packet from CH to MH
B
C
A
10
B
C
A
Packet from CH to MH
Packet from HA to FA
Basics of IETF Mobile IP packet forwarding
11
B
C
A
Observation 2 Similar rules apply for mobile
hosts that are members of multicast groups.
12
B
C
A
13
B
C
A
14
Packet from MS to G
B
C
A
15
B
C
A
Packet from MS to MH
Packet from HA to FA
Can be done using unicast bidirectional
tunneling.
16
B
C
A
Observation 3 This can be inefficient if
multiple group members are away at the same
location.
17
B
C
A
18
B
C
A
19
Packet from MS to G
B
C
A
20
B
C
A
Packet from MS to MH
Packet from HA to FA
21
B
C
A
Packet from MS to G
Packet from HA to FA
More efficient solution is to tunnel the
multicast itself.
22
B
C
A
Observation 4 Inefficiency still exists if
multiple HAs have group members away at the same
location.
23
B
C
A
24
B
C
A
25
B
C
A
26
Packet from MS to G
B
C
A
27
B
B
C
A
Packet from MS to G
Packet from HA to FA
This is called the tunnel convergence problem.
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B
C
A
Packet from MS to G
Packet from HA to FA
The solution in the MoM (Mobile Multicast)
protocol is to select a Designated Multicast
Service Provider (DMSP) to forward multicast
packets to G at a certain network.
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Observation 5 The general case can be very
messy! The performance of MoM (or any other
protocol) depends on group size and on MOBILITY
PATTERNS.
Multicast group DMSP (HA) Mobile Host
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Multi-Variate Analysis of Mobility Models for
Network Protocol Performance Evaluation

• Carey Williamson
• Nayden Markatchev
• carey,nayden_at_cpsc.ucalgary.ca
• University of Calgary

31
Motivation

• The performance of a mobility support protocol
  is highly sensitive to user mobility patterns.
• Very little is known about mobile user behaviors
  in operational networks.
• Most simulation studies evaluating protocol
  performance use simple models of user mobility.
  (e.g., random walk)

32
Overview of this Research

• Proposes a more general suite of mobility models
• Models are classified along two orthogonal axes
  degree of correlation (I/C) and degree of
  skewness (U/N)
• Independent Uniform (IU)
• Independent Non-Uniform (IN)
• Correlated Uniform (CU)
• Correlated Non-Uniform (CN)
• Uses the MoM protocol as a case study for the
  models.
• Impacts of mobility model parameters assessed
  using the Analysis of Variance (ANOVA)
  statistical technique.

33
Background and Related Work

• Mobile Computing and Mobile IP.
• IETF Mobile IP protocol
• Mobile Host (MH)
• Foreign Agent (FA)
• Home Agent (HA)
• The model works but multicast support is
  inefficient. (tunnel convergence problem)
• Therefore

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Background and Related Work(2)

• New protocols, such as the MoM (Mobile Multicast)
  protocol, are proposed to deal with this issue.
• MoM uses the Home Agent for delivery of multicast
  datagrams to mobile users, and achieves
  scalability through a Designated Multicast
  Service Provider (DMSP) for each multicast group
  on a foreign network.

35
Basic Mobility Model in MoM
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New Mobility Models

• To broaden the range of mobility patterns
  considered, we introduce two new model
  parameters
• Correlation
• The tendency for certain hosts to move in
  patterns that are related either
  geographically (i.e., location) or temporally
  (i.e., time).
• Skewness
• Some destinations are more popular than others.
• The combination of those two factors leads to
  four different mobility models CU, CN, IU, IN.

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Mobility Model Parameters

• Homing Probability - HOMING_PROB (0.5)
• Mean Residency Time (60 time units) and Mean
  Travel Time (6 time units).
• Skewness
• Degree of skewness k gt 0.
• Correlation (i.e., follow the leader)
• FRACTION_FOLLOWERS ( of mobile hosts)
• FOLLOW_PROBABILITY (per-move by a follower)

38
Model Validation
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Experimental Parameters
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Experimental Design

• Simulations are used to assess the performance
  impacts of multicast group size, network size,
  number of mobile hosts, and host mobility model.
• Simulations run for 26,000 time units, of which
  the first 6,000 time units are for warm up.
• Only one multicast group is simulated.

41
Performance Metrics

• DMSP forwarding overhead per HA.
• Number of DMSP handoffs.
• The average number of foreign networks visited by
  mobile multicast group members (per HA).

42
MoM Performance
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MoM Performance (zoom)
Line A - Average number of group members
away. Line B - Average number of different
foreign networks at which the away group
members reside. Line C- DMSP forwarding overhead.

44
Impact of Mobility Model on Number of Foreign
LANs Visited
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Analysis of Variance (ANOVA)

• ANOVA is a statistical technique to analyze
  multi-variate data and figure out which factor is
  most important.
• The method separates the total variation of the
  performance index into components associated with
  possible source of variation.
• Tabular analysis row effect vs. column effect.
• F-test values determine the level of factors
  influence.
• Multiple independent replications of experiments
  are used to identify the interaction effects
  between different factors.

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DMSP Overhead per HA(3 replications)
Note lower is better. CN is best case. IU is
worst case.
10 LANS, 10 Hosts per LAN Multicast group size
100
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ANOVA ResultsDMSP overhead per HA

• Correlation factor - 67.0
• Skewness factor - 28.5
• Interaction - 2.25
• Error - 2.22

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DMSP Handoffs(3 replications)
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ANOVA Results DMSP Handoffs
Correlation factor - SSA/SST 349,515/399,980
87.4 Skewness factor - SSB/SST
6.2 Interaction - SS(AB)/SST 0.4 Error -
SSE/SST 6.0 The P value indicates the
statistical significance of each value.
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Average Foreign LANs Visited (per HA) (3
replications)
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ANOVA ResultsForeign LANs Visited (per HA)
Number of Hosts per LAN - 58.0 Number of LANs -
31.3 Interaction - 10.7 Error - 0.003
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Effect of Correlation Parameterson LANs Visited
(3 replications)
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ANOVA ResultsImpact of Correlation Parameters

• FRACTION_FOLLOWERS accounts for 34.2 of the
  total variation.
• FOLLOW_PROBABILITY accounts for 35.9 of the
  total variation.
• Interaction effects account for 29.2.
• Errors contribute 0.7.

54
Effect of Skewness Parameterson LANs Visited (3
replications)
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ANOVA ResultsEffect of Skewness

• Correlation factor contributes 57.6 of the total
  variation.
• Skewness contributes 33.9 of total variation.
• The interaction effect accounts for 8.0.
• The effect of errors is 0.6.

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Summary and Conclusions

• The proposed suite of models (IU, IN, CU, CN)
  represents a broad set of possible behaviors for
  mobile users.
• The choice of mobility model can have a
  significant effect on protocol performance.
• The degree of correlation between mobile hosts
  has a greater impact than the degree of skewness.
• For the MoM protocol, the Independent Uniform
  (IU) model is actually the worst case stress
  test.

57
Future Work

• Extending the correlation models to include
  dynamic multicast group membership.
• Applying our mobility models to routing in ad hoc
  wireless networks
• Applying our mobility models to the evaluation of
  the rekeying protocols for secure multicast
  groups.