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

Multi-Variate Analysis of Mobility Models for Network Protocol Performance Evaluation. Carey Williamson Nayden Markatchev {carey,nayden}@cpsc.ucalgary.ca University of Calgary. Preamble and Motivation. Consider mobile host movement in an arbitrary internetwork

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

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  1. Multi-Variate Analysis of Mobility Models for Network Protocol Performance Evaluation Carey Williamson Nayden Markatchev {carey,nayden}@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).

  9. 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 HA to FA Packet from MS to MH 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. Packet from HA to FA Packet from MS to MH2 B C A Packet from HA to FA Packet from MS to MH

  21. B C A Packet from HA to FA Packet from MS to G More efficient solution is to tunnel the multicast itself.

  22. B C A Observation 4: Inefficiency still exists if multiple HA’s 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. Packet from HA to FA Packet from MS to G B B C A Packet from HA to FA Packet from MS to G This is called the “tunnel convergence problem”.

  28. B C A Packet from HA to FA Packet from MS to G 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.

  29. 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

  30. Multi-Variate Analysis of Mobility Models for Network Protocol Performance Evaluation Carey Williamson Nayden Markatchev {carey,nayden}@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…

  34. 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

  36. 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.

  37. 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 >= 0. • Correlation (i.e., follow the leader) • FRACTION_FOLLOWERS (% of mobile hosts) • FOLLOW_PROBABILITY (per-move by a follower)

  38. Model Validation

  39. Experimental Parameters

  40. 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

  43. 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

  45. 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.

  46. 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

  47. ANOVA Results:DMSP overhead per HA • Correlation factor - 67.0% • Skewness factor - 28.5% • Interaction - 2.25% • Error - 2.22%

  48. DMSP Handoffs(3 replications)

  49. ANOVA Results: DMSP Handoffs Correlation factor - SSA/SST = 349,515/399,980 = 87.4% Skewness factor - SSB/SST = 6.2% Interaction - SS(A+B)/SST = 0.4% Error - SSE/SST = 6.0% The P value indicates the statistical significance of each value.

  50. Average Foreign LANs Visited (per HA) (3 replications)

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