1. Dynamic agent-based hierarchical multicast for wireless mesh networks Yinan Li, Ing Ray Chen Presented by Kruthika Rathinavel

2. Agenda ❖ Objective ❖ Introduction ❖ DAHM - System model and Assumptions ❖ Network Events Handling ❖ Stochastic Petri Net (SPN) model ❖ Performance Evaluation ❖ Practicability and Implementation ❖ Conclusion 2

3. Objective  Propose and analyze a multicast algorithm for wireless mesh networks  Algorithm that minimizes overall network cost incurred  Dynamically handle  Member mobility  Multicast service management  Key issue  Mobility of multicast group members 3

4. Introduction  Wireless Mesh Networks (WMNs)  Cost-effective solution for providing last-mile community based broadband services  Wireless Mesh Routers (MRs)  Wireless Mesh Clients (MCs)  Wireless Mesh Routers (MRs)  Wireless mesh backbone for MCs  Wireless Mesh Clients (MCs)  End-user devices with wireless access capability  Usually mobile; may change their locations frequently 4

5. Wireless Mesh Networks (WMNs)  Seamlessly connected to the internet  Uses gateway functionality of the MRs  MRs – integrate a WMN to an existing wireless network (wireless ad-hoc networks, wireless sensor networks)  Multicasting – common paradigm in WMNs  Broadcasting nature of wireless communications 5

6. DAHM  System model and Assumptions  System Overview  Member Join and Leave  Mobility Management and Tree Maintenance  Multicast Packet Delivery 6

7. System Model and Assumptions Two level hierarchical multicast algorithm  Backbone multicast routing (based on shortest-path tree (SPT))  Local unicast routing Assumptions:  Single internet gateway  Current and future wireless MRs are powerful enough to host multicast agent software for integrated mobility and multicast service management  WMN capable of performing integrated mobility and multicast service management  Source is assumed to be static  Multicast group is dynamic w.r.t. group member locations and group membership 7

8. System Model and Assumptions  Multicast source – internet / one of the MCs  Internet – backbone multicast tree routed at gateway  One of the MCs - backbone multicast tree routed at source  Group member join and leave events modeled as Poisson processes with rates λand μ, respectively  Inter-arrival and inter-departure times are exponentially distributed with averages 1/λand 1/μ, respectively  Assumed that λand μ have about the same value (multicast group size remains stable over time)  Upper level tree – based on Shortest Path Tree (SPT) 8

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10. Why Unicast Routing @ Lower Level?  Optimal service region size of an MA that minimizes the overall communication cost  H optimal – Diverse for different group members depending on their mobility and service characteristics  Using unicast routing eliminates the need for multicast tree maintenance at the lower level  Simplifies mobility management 10

11. Member Join Event 11 MCNew MRGateway JOIN_REQ JOIN_ACK Message Exchange Sequence

12. Member Leave Event 12 MCMAGateway LEAVE_REQ LEAVE_ACK Message Exchange Sequence

13. New MR ‘H’ hops away from member’s current MA 13 MCOldMANew MR 1. ASSO_REQ 7. LEAVE_REQ 2. ASSO_ACK 8. LEAVE_ACK Gateway 3. JOIN_REQ 4. JOIN_ACK 5. DEASSOC_REQ 6. DEASSOC_ACK Message Exchange Sequence

14. New MR is already an MA 14 MCOldMANew MA 1. ASSO_REQ 5. LEAVE_REQ 2. ASSO_ACK 6. LEAVE_ACK Gateway 3. DEASSOC_REQ 4. DEASSOC_ACK Message Exchange Sequence

15. Multicast Packet Delivery - Encapsulation 15 Encapsulated packets sent to MAs New Packet – Multicast payload encapsulated with multicast destination address Gateway (Virtual source) Source (Internet) Sent through backbone multicast tree using multicast routing mode Multicast packets sent

16. Multicast Packet Delivery- MA to MCs 16 MA decapsulates packet received Encapsulates the payload with address of the serving MR of each member in destination field MR receives packet Decapsulates packet and delivers the packet to the designated member Unicast routing; Address of MR found in MA’s location database

17. Performance Model  Two dimensional n x n mesh  Wrap-around on the boundaries such that each MR has exactly four neighbors  Each MR can communicate directly with any of its four neighbors that are within its communication range  A member can change from its current serving MR to any of the MR’s four neighbors with equal probabilities of ¼.  Total number of MRs in the network  N = n 2 17

18. n x n Mesh Network Model 18

19. 19 ParameterNotation σThe average mobility rate of multicast group members λpλp The multicast packet rate SMRService to mobility ratio, defined as SMR=λ p /σ λThe rate of member join events μThe rate of member leave events MThe multicast group size nThe dimension of the WMN NThe number of MRs in the WMN γThe member density αThe average unicast path length of the WMN ωThe arrival rate of a single member to an arbitrary MR P MA The probability that an arbitrary MR is also an MA P0 P0 The probability that an MR is not covering any member P1P1 The probability that an MR covers exactly one member The probability that an MA services exactly one member N MA The number of MAs HThe service region size of an MA TThe multicast tree size in terms of total number of tree nodes κThe multicast scaling factor LThe expected hop distance from the source to an MA dThe average degree of inner nodes Parameters and Notations used in Performance Modeling and Analysis

20. Performance Model  σ – physical meaning:  average number of serving MR changes made by a multicast group member per time unit  Time unit : second  Average unicast path length (hop count) denoted by α : α=2n/3  Arrival and departure of M multicast members to and from an MR  M/M/∞/M queue  ω- Arrival rate of single member to an arbitrary MR ω=σ/(n 2 -1) 20

21. M/M/∞/M queuing model 21  P 0 – probability that an MR covers no member  P 1 – probability that an MR exactly one member

22. Performance Model Parameters 22 Probability that an arbitrary MR is an MA in DAHM is approximated P MA – approximate because which MR is chosen as MA for a multicast Members depends on the user’s mobility A MA services exactly one member if all the MRs within its service region totally service exactly one member. Probability that an MA services exactly one member L m -Total number of multicast links (among MRs) R – number of leaves on the multicast tree Expected hop distance L from the source to an MA – average length of all paths from source to the MAs (Average depth of all Mas on the backbone multicast tree routed at the source) d – degree of the inner node d=4 here, as each inner node has 4 neighbors T – total number of MRs(including Mas)

23. Stochastic Petri Net Model for DAHM 23

24. Performance Metrics  Average total communication cost incurred per member per time unit (second) – Metric for performance metrics and analysis  Objective – minimize this cost 24

25. Performance Evaluation 25 Cost vs. H, under different multicast group sizes in DAHM (n = 10) Cost vs. γ in DAHM γ=M/N ->Average number of members serviced by one MR H optimal vs. γ in DAHM γ= M/N -> Average number of members serviced by one MR Cost vs. H, under different network sizes in DAHM (M = 50)  Service region size – key to performance of DAHM  There exists an optimal service region size that optimizes the performance of DAHM  C DAHM decreases monotonically with increasing γ  Multicast efficiency improves as the member density increases  cost is effectively amortized by the increasing member population Service cost C s for multicast packet delivery and accordingly C DAHM decreases with decreasing H optimal.  Because average distance over which multicast packets are transmitted at the lower level decreases

26. Comparative Performance Study 26 Performance comparison: cost vs. M(n = 10) Performance comparison: cost vs. n(M = 50) Member density,γ=0.25 ; cost vs. SMR Member density, γ=1.5; Cost vs SMR

27. Simulation using SMPL (BMA- 10 batches)  Location update events  Multicast packet deliveries  Member join/leave events  Multicast tree maintenance operations  All the above are discrete events  Simulated using SMPL Batch Mean Aanalysis 27 Analytical modeling vs. simulation: cost vs. H, under different multicast group sizes in DAHM (n = 10) Analytical modeling vs. simulation: cost vs. γ in DAHM Analytical modeling vs. simulation: cost vs. M(n = 10) Analytical modeling vs. simulation: cost vs. n(M = 50)

28. End-to-end delay minimization & Throughput maximization  C DAHM – Amount of traffic incurred to the network per MC  C DAHM,i – traffic generated by MC i  Total traffic incurred to the network by all MCs = Σ i C DAHM,i  Per hop traffic delay (including queuing delay and retransmission delay because of collisions) at any MR can be minimized when the input traffic to the MR is minimized.  End-to-end multicast packet delay to the MC is also minimized  Little’s law: throughput x response time (end-to-end delay) = MC population  Network throughput is maximized when end-to-end delay is minimized  Happens when every MC operates at the optimal H optimal value 28

29. Practicability and Implementation  Powerful mobile devices with state-of-the-art processors – H optimal determined at runtime periodically  Less powerful mobile devices – table lookup of H optimal at runtime periodically  Data required to compute H optimal :  Mobility rate (σ)  Multicast packet rate (λ p )  Rates of member join/leave events (λ and μ) 29

30. Future Research Directions  investigating how the proposed multicast algorithm can be adapted to support multiple multicast groups simultaneously active in a WMN  utilizing MCs in addition to MRs serving as MAs when a group member cannot find a nearby MR and must rely on other MCs for network traffic relaying  considering the effect of lossy and heterogeneous links of WMNs to enhance multicast service performance  investigating how DAHM can be augmented and optimized to support reliable and secure multicast in WMNs  etc. 30