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Multicast Applications: ProbeCast and RelayCast Mario Gerla, Uichin Lee, Soon Oh, SeungHoon Lee CSD, UCLA www.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)

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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
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


Dtn multicast routing

R1

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
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)


Review 2 hop relay

Source

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


Relaycast dtn multicast routing

D2

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
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
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


Relaycast delay analysis

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)


Comparison with previous results

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 Results

Per node throughput with ns= Θ(n)

Number of m-cast receivers per source


Simulation results
Simulation Results relay

  • 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 Inter-contact Time relay

  • 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 time1
Two-phase Inter-contact Time relay

  • 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
Two-hop Relay Unicast under Correlated Motion Patterns relay

  • 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
Simulation: Throughput relay

  • 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
Simulation: Buffer Utilization relay

  • 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
Summary: DTN Routing under Correlated Motion Patterns relay

  • 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


Probecast s oh g marfia m gerla q2swinet 2008
ProbeCast relayS. 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
ProbeCast: key insights relay

  • 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
ProbeCast Approach relay

  • 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
ProbeCast Approach (Cont.) relay

  • 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
Probe/Prune + N-PROD at Work relay

(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
Simulation - Fairness relay

  • 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


Nc implementation guidelines s lee u lee k w lee m gerla secon 2008

generation relay

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


Nc throughput measurement

N2 relay

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


Nc throughput measurement1

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 relayfor 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)


Mobsim architecture

Applications relay

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


Road constrained m otion m odel

End relay

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


Agent t racks t arget using last encounter routing

Harvest relay

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

MobSim relay Screenshot

MobSim: Simulation Results

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


Future work
Future Work relay

  • 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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