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### Multicast Applications:ProbeCast and RelayCastMario Gerla, Uichin Lee, Soon Oh, SeungHoon LeeCSD, UCLAwww.cs.ucla.edu/NRL

MURI-DAWN Project review

UCSC, Oct 14 2008

Progress in 2007-2008

- Data dissemination (DTN scenarios)
- RelayCast: a scalable DTN multicast protocol (ICNP 2008)
- Impact of correlated motion on unicast DTN routing (work in progress)
- 2 phase inter-contact time distribution: power law head with exponential tail
- Capacity/delay of DTN unicast routing
- ProbeCast: multicast admission control (Q2SWINET 2008)
- Resource probing + pruning, neighborhood proportional drop (NPROD) for fair share of a channel
- Network coding configuration/implementation
- Communication, disk I/O, encoding overhead analysis (using measurement based models)
- MobSim: an interactive vehicular motion simulator

Disrupted node

Disrupted node

R4

R1

R1

F

R4

R1

R2

S

mobility

R2

R2

mobility

S

R3

R3

F

S

S

Tree

Mesh

Ferry/mule

Dissemination

DTN Multicast Routing- Provides reliable data dissemination (e.g., situation awareness data) even in disrupted environments
- DTN multicast routing strategies
- Tree, mesh, ferry/mule, epidemic dissemination
- Use mobility-assist routing to deal with disruptions

Scaling Properties of DTN Multicast

- Questions:
- Achievable DTN multicast throughput; average delay
- Compare with existing capacity/delay bounds of ad hoc wireless networks (Gupta&Kumar)
- Trade-offs:
- Infinite buffers: throughput/delay trade-offs
- Finite buffers: throughput/buffer tradeoffs
- Modeling approach:
- Inter-contact time models
- Queueing models (for throughput/delay/buffer analysis)

Relay

Destination

Review: 2 Hop Relay- 2-hop relay:

1. Source sends a packet to a relay node

2. Relay node delivers a packet to the corresponding receiver

2-hop Relay by Grossglauser and Tse

D3

D1

Source

Relay

Destinations

RelayCast: DTN Multicast Routing- 2-hop relay based multicast:

1. Source sends a packet to a relay node

2. Relay node delivers the packet to ALL multicast receivers

RelayCast: 2-hop relay based multicast

Two-Hop Relay Review

Intuition: average throughput is determined by aggregate encounter rate (src relay and relay destinations)

How often does a destination node encounter any of the relay nodes? Answer: n*λ

Two-hop relay per node throughput : Θ(nλ)

Aggregate meeting rate at a destination: nλ

Grossglauser and Tse’s results: Θ(nλ)=Θ(1)

Recall: λ = 1/n (i.e., speed 1/√n, radio range 1/√n)

RelayCast: Throughput Analysis

- Multicast traffic pattern:
- ns sources, each of which is associated with nd random destinations
- Different sources may choose the same node as one of their receivers
- Fraction of sources per receiver : nx =nsnd/n
- A source chooses a node as dest with prob. nd/n
- Fraction of aggregate packets per source = 1/nx
- RelayCast throughput: Θ(nλ/nx)=Θ(n2λ/nsnd)
- i.e. = (#of nodes) x rate x frct of packets per source

2λ

λ

3

2

1

0

RelayCast: Delay Analysis- One relay node delivers packets to all receivers
- RelayCast delay: Θ(log nd /λ)
- Unlike conventional multicast, delay is proportional to the number of receivers

R2

R1

X2

R3

X1

Markov Chain for delivery status:

Average delay = average time to absorb

= 1/3λ + 1/2λ+1/λ (memoryless!)

R

X3

relaynode

D=max(X1, X2, X3)

Assumptions; n fixed; r = √logn/n G&K; r=√1/n for 2-hop relay

Throughput scaling comparison with ns= Θ(n)

nd: # receivers, n: total # nodes

RelayCast is better than conventional multi-hop multicast (r= √logn/n)

Grossglauser & Tse, INFOCOM’01Delay Tolerant Apps

RelayCast: Delay Tolerant Apps

Gupta & Kumar, TOIT’00

Shakkottai et al., Mobihoc’07Li et al., Mobicom’07

Tavli, IEEE Com. Letter’06Keshavarz-Haddad et al., Mobicom’06

Comparison with Previous ResultsPer node throughput with ns= Θ(n)

Number of m-cast receivers per source

Simulation Results

- Comparison with Conventional Multicast Protocol
- Connected topology
- RelayCast is scalable; ODMRP’s throughput decreases significantly, as # sources increases

* QualNet v3.9.5

* Mobility: random waypoint (speed = 20, 30m/s)

* Network area size: 1000m*1000m

* 100 Nodes, 250m TX range

5 destinations

Two-phase Inter-contact Time

- Two-phase distribution: power-law head and exponential tail

Chaintreau06

Karagiannis MobiCom 07

Infocom 06

Levy walk: Rhee Infocom 08

- Association times with AP (UCSD) or cell tower (MIT cell)
- Direct contact traces: Infocom, cambridge (imotes), MIT-bt

Two-phase Inter-contact Time

- Why two-phase distribution?
- One possible cause: flight distance of each random trip [Cai08]
- The shorter the flight distance, the higher the correlation heavier power tail
- Examples of correlations:
- Manhattan sightseers: In Time Square, sightseers tend to bump into each other; and then depart for other sights
- Levy flight of human walks [Rhee08]: short flights + occasional long flights
- Vehicular mobility: Constrained by road traffic (+traffic jam)
- High correlation among vehicles in close proximity
- After leaving locality, vehicles meet like “ships in the night”
- Power-law head while in the local contention area, vs. exponential tail for future encounters

*Cai08: Han Cai and Do Young Eun, Toward Stochastic Anatomy of Inter-meeting Time Distribution under General Mobility Models, MobiHoc’08*Rhee08: Injong Rhee, Minsu Shin, Seongik Hong, Kyunghan Lee and Song Chong, On the Levy-walk Nature of Human Mobility, INFOCOM’08

Two-hop Relay Unicast under Correlated Motion Patterns

- Impact of correlated motion patterns on throughput/delay performance?
- Under the average flight distance of Ω(r); i.e., minimum travel distance ~ one’s radio range
- Increase correlation by decreasing flight distance
- Preliminary analytic results :
- Throughput: Independent of node speed and degree of correlation (ie, flight distance)
- Average delay is within [1/λ, logn/λ]; i.e., random direction (to wall) and random walk respectively
- Delay monotonically increases with the degree of correlation
- Buffer requirement also increases
- Using Little’s results: [Θ(nr/v), Θ(nrlogn/v)]
- Simulation results:
- Correlation increases burstiness of traffic in and out of relays

Simulation: Throughput

- Degree of correlation via average flight distance L
- 5000m*5000m area
- L=R=250m high correlation power law head + exponential tail
- L=1000m low correlation almost exponential
- Throughput is independent of the degree of correlations

L=250m

Exp

L=1000m

L=250m

log-linear plot

CCDF of inter-contact time (20m/s)(Log-log plot)

Average throughput per node as a function of # relay nodes

Simulation: Buffer Utilization

- Burstiness increases with the degree of correlation

Cumulative distribution of the number of consecutiveencounters

Buffer utilization over time (speed=30m/s)

Summary: DTN Routing under Correlated Motion Patterns

- Per-node throughput is not affected by the degree of correlation
- However, correlation causes increases in:
- Variance in the inter-contact time
- Average delay
- Buffer requirements
- Burstiness of inbound/outbound

ProbeCastS. Oh, G. Marfia, M. Gerla, Q2SWINET 2008

The Problem:

- Resource reservation/allocation schemes are ineffective in inelastic multicast in ad hoc nets
- Bookkeeping is very cumbersome (as # of destinations increases);
- Also, mobility requires continuous re-adjustments
- Without QoS support, quality will collapse

Flow 1 has 9 receivers with 200Kbps and flow 2 has 3 receivers with 40Kbps

- Goal:
- Achieving reliable QoS support in inelastic multicast flows (e.g., video and audio stream)

ProbeCast: key insights

- Insight #1: Resource Probing
- No a priori resource allocation
- Rather “probe” for resources
- Insight #2: Pruning via Back-pressure
- Back-pressure (“prune”) toward the source when resource is unavailable
- Re-route or reject the inelastic flow
- Insight #3: Neighborhood Proportional Drop (NPROD)
- Local rate balancing using proportional dropping
- Enforces fair channel sharing “fair back-pressure”
- Main Outcome:
- Inelastic flows to acquire resources in fair manner without reservation, yet preserving reliable QoS

ProbeCast Approach

- Assumptions:
- End-to-End FEC – e.g. erasure coding – always ON
- Each flow has packet drop threshold
- Probing
- Each node measures resource overload – e.g. packet drop rate
- Broadcast to one hop neighbors own drop rate via piggybacking on packets
- Proportional Drop (N-PROD)
- Overhearing neighbors’ drop rates
- Enforcing equal drop rates among flows competing in the same contention domain – packet drop
- Nodes in the same contention domain sharing channel fairly

ProbeCast Approach (Cont.)

- Pruning
- Drop-Threshold (DT) for flows
- traffic class and flow age dependent
- Piggybacking DT on the packet Forwarders know Drop Threshold of flows
- Typically, incoming flow has lower threshold than incumbent
- When drop rate is > threshold, a flow is back-pressured no explicit control packets to source
- Source action:
- re-route if there is alternative route;
- otherwise reject the flow

Probe/Prune + N-PROD at Work

(A) 3 flows in the same contention domain. Lower graphs shows packet delivery ratios, presented by percentages. (B) Flow 3 starts transmitting and other flows’ rates decrease (N-PROD). (C) Since flow 3 drop rate exceeds the threshold, backpressure starts.

Simulation - Fairness

- Qualnet Simulation: 50 nodes uniformly distributed 1000 mby 1000 m field
- Flow 1 has 9 receivers with 200Kbps and Flow 2 has 3 receivers with 40Kbps
- N-PROD eliminates capture problem increasing FAIRNESS

X1

X2

X3

e3

e1

e2

+

[e1,e2,e3]

e1X1+e2X2+e3X3

NC Implementation GuidelinesS. Lee, U. Lee, K-W. Lee, M. Gerla SECON 2008- Goal: show that NC can be implemented in military scenarios
- Develop configuration guidelines based on measured data
- We start with Network Coding processing O/H analysis
- Linearly proportional to the number of packets in a generation (= generation size)
- Generation size must be carefully chosen: max node encoding rate > (available) wireless bandwidth

Nokia N800: TI OMAP 2420 (330Mhz) + 802.11b

N1

Orinoco8471 WD

IBM Thinkpad R52

N3

Scenario: k contendersin domain (k=1/2/3)

NC Throughput Measurement- Validation through measurements using portable devices

G10 = 10 packets in generation

- N/A: No network coding

Number of contenders in a domain

NC Throughput Measurement- large generation => high CPU O/H => low pkt tx
- As the number of contenders increases, pkt tx rate must decrease can support a larger generation size
- For small unit operations, optimal Gen Size < 50 (from experiments)
- Well suited for network coding based streaming (i.e., CodeCast)

MobSim: An Interactive Simulator for Urban MobilityC. Li, M. Bansal, U. Lee, K.-W. Lee, M. GerlaACITA Demo Session

- Limitations of current simulators
- Non-realistic urban mobility models
- Non-interactive simulations
- MobSim design goals:
- Programmable mobility model
- Interactive simulation environment
- Built-in appl modules (eg dissemination)

Mobility Generator: Tiger map + IDM

Real-time Visualization Module

Data DisseminationProcessing Module

InteractiveSimulation UI

MobSim

MobSim Architecture- Mobility Generator:
- Tiger map of target urban area
- Underlying vehicle motion pattern (eg, commuting, shopping, etc)
- Application:
- E.g., Data Dissemination Processing Module
- Target selection; Agent vehicles, etc
- Real-time Visualization Module
- Interactive Simulation UI

Start

Road-constrained Motion Model- For each car, pick random start/end points and speed
- Construct the shortest path
- Travel at variable speed on each segment

Move to Last Encounter Point

Agent Tracks Target using Last Encounter Routing- Agent moves in direction hinted by cars that last encountered the target
- While moving, agent continuously looks for fresher encounter information

MobSim: Simulation Results

- Average search time with varying number of agents and number of nodes

Future Work

- Impact of different vehicle and agent motion patterns
- Impact of density (e.g., intermittent connectivity)
- Bio-inspired multiple agent collaboration algorithm (i.e., Lévy jump based searching + datataxis)
- Investigate realistic urban warfare scenario (e.g., hints about enemy movements)

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