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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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slide1

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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
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
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
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
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
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
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
nc implementation guidelines s lee u lee k w lee m gerla secon 2008

generation

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

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

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)
slide27
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)
mobsim architecture

Applications

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

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

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 Screenshot

MobSim: Simulation Results
  • Average search time with varying number of agents and number of nodes
future work
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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