U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science R3: Robust Replication...

UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS, A, AMHERST • MHERST • Department of Computer Science Department of Computer Science

R3: Robust Replication Routing in Wireless Networks with Diverse Connectivity

Characteristics

Xiaozheng Tie, Arun Venkataramani, Aruna Balasubramanian

University of Massachusetts AmherstUniversity of Washington

UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS A AMHERST • MHERST • Department of Computer Science Department of Computer Science 2

Wireless routing compartmentalized

Protocols designed for well-connected meshes

OLSR, ETT, ETX, EDR, …

Protocols designed for intermittently-connected MANETs

AODV, DSDV, DSR, …

Protocols designed for sparsely-connected DTNs

DTLSR, RAPID, Prophet, Maxprop, EBR, Random, …

Research question: Can we design a simple routing protocol that ensures robust performance across networks with diverse connectivity characteristics all the way from well-connected meshes to mostly-disconnected DTNs and everything in between?

Research question: Can we design a simple routing protocol that ensures robust performance across networks with diverse connectivity characteristics all the way from well-connected meshes to mostly-disconnected DTNs and everything in between?


Outline

Compartmentalized design harmfulQuantifying replication gainR3 design and implementationEvaluationConclusion


Fragile performance

Protocols perform poorly outside target environment

DTN protocols perform poorly in mesh

Replication wasteful

Mesh protocols perform poorly in DTNs

No contemporaneous path

Mesh testbed DTN testbed

2.1x

Norm

aliz

ed

dela

y

2.2x

Norm

aliz

ed

dela

y


Spatial connectivity diversity

DieselNet-HybridVehicular DTN + Wifi Mesh20 buses in Vehicular DTN4 open AP WiFi mesh clusters

< 100 contacts

100 – 200 contacts

> 200 contacts


Temporal connectivity diversity

HaggleMobile ad hoc network8 mobile and 1 stationary imotes9 hour trace in Intel Cambridge Lab

Fract

ion o

f co

nnect

ed n

odes


Compartmentalized design harmful

1. Fragile performance under spatio-temporal diversity

2. Makes interconnection of diverse networks difficult

Makes management difficultConflates cross-layer concernsStifles long-term innovation


Outline



Replication: Key difference

DTN MeshMANET

Sparsely connected Well connectedIntermittently connected

Replication Forwarding

Key question: Under what conditions and by how much replication improves performance?Key question: Under what conditions and by how much replication improves performance?

UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS A AMHERST • MHERST • Department of Computer Science Department of Computer Science

Model to quantify replication gain

10

Replication gain

€

€

μ =min E[X1],E[X2],...,E[Xn ]{ }

Src

€

X1

Dst

€

X2

€

Xn

€

μ(1) = E[min{X1,X2,...,Xn}]

• Expected delay of forwarding

• Expected delay of replication

€

μμ(1)

€

X i

Random variable denoting the delay of path i


Example of replication gain

11

Replication gain

€

€

μ =min E[X1],E[X2]{ } =min 1,3{ } =1

Src

€

X1Dst

€

X2

€

μ(1) = E[min{X1,X2}] = 0.2

• Expected delay of forwarding

• Expected delay of replication

€

μμ(1)

= 5

€

P(X1 = 0.1) = 90%

P(X1 =10) =10%

E(X1) =1

€

P(X2 = 0.3) = 90%

P(X2 = 30) =10%

E(X2) = 3

Replication gain depends on path delay distributions, not just expected value Replication gain depends on path delay distributions, not just expected value

5x delay improvement


Trace-driven analysis on DieselNet-DTN and Haggle

Replication gain vs. number of paths

Vehicular DTN in DieselNet Haggle

Two paths suffice to capture much of the gainTwo paths suffice to capture much of the gain



Outline


R3 design overview

Link-stateEstimate per-link delay distribution

ReplicationSelect replication paths using modelAdapt replication to be load-aware

Source routing along selected path(s)

€

Y1

€

Y2

€

Y3

Src Dst


Link delay

Estimate link delay distribution

T=0

T=0: probe 0 unacked

T=1

T=1: probe 1 unacked

T=2

T=2: probe 2 acked at T=2.1 Delay = 2.1-2 = 0.1Delay = 2.1-1 = 1.1Delay = 2.1-0 = 2.1

Delay samples = {2.1, 1.1, 0.1}

Delay to successfully transfer packetLink availability delay

Node 1 Node 2


R3 design overview

Src Dst





First pathPath s.t. is smallestSelected using Dijkstra’s shortest path algorithm

Second pathPath s.t. is smallestSelected using delay distributions and model

Path selection using model

€

E[X i]

€

E[min{X i,X j}]

€

X1

€

X2

€

Xn€

X iSrc Dst

€

i

€

j


ProblemReplication hurts performance under high load

SolutionLoad aware replication

Adapting replication to load

ForwardingForwardingReplicationReplicationStart

actual_delay > t * model_estimated_delay

actual_delay ≤ t * model_estimated_delay


R3 design overview




Src Dst


Compartmentalized design harmfulQuantifying replication gainR3 design and implementationEvaluation

Deployment on a DTN and mesh testbedSimulation based on real tracesEmulation using mesh testbed

Conclusion

Outline


DieselNet DTN testbed20 buses in a 150 sq. mile area

Mesh testbed16 nodes in one floor

Simulator validation using DieselNet deployment

< 10% of deployment result

R3 Deployment


Experimental settingsTemporal diversity inherent in HaggleSpatial diversity inherent in DieselNet-HybridVarying load

Compared protocolsReplication: RAPID, ProbabilisticForwarding: DTLSR, AODV, OLSRMulti-configuration: SWITCH (RAPID+OLSR)

R3 Trace-driven simulation


Robustness to temporal diversity

Simulation based on Haggle trace

23

R3 reduces delay by up to 60%R3 reduces delay by up to 60%R3 increases goodput by up to 30%R3 increases goodput by up to 30%

HourHour

Dela

y (

min

)

Goodput

(pkt

/min

)


Robustness to spatial diversity

Simulation based on DieselNet-Hybrid trace

24

R3 improves median delay by 2.1xR3 improves median delay by 2.1x

1

2

34

5

6

7

8

9

Grid

Dela

y (

min

)


Emulating intermediate connectivityMesh-based emulation approach

Brings link up and down to vary connectivityEmulates connectivity diversity (but not mobility)

25

R3 reduces delay by up to 2.2xR3 reduces delay by up to 2.2x

Hour

Dela

y (

sec)


Compartmentalized design harmful

R3 ensures robust performance across diverse connectivity characteristics

Unified link metric based on delay distributionsReplication based on delay uncertainty modelAdaptive replication based on network load

Conclusion

Thank you!


Robustness to varying load

Simulation based on DieselNet-Hybrid trace

28

R3 reduces delay by up to 2.2x over SWITCHR3 reduces delay by up to 2.2x over SWITCH


More paths in DieselNet-Hybrid

29

Average delay when R3 uses k=2, 3, 4, 5 replication paths.

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