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R3: R obust Replication Routing in Wireless Networks with Diverse Connectivity Characteristics. X iaozheng Tie, Arun Venkataramani, Aruna Balasubramanian U niversity of Massachusetts Amherst U niversity of Washington. W ireless routing compartmentalized. - PowerPoint PPT Presentation
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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 compartmentalizedProtocols 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?
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS A AMHERST • MHERST • Department of Computer Science Department of Computer Science 3
OutlineCompartmentalized design harmfulQuantifying replication gainR3 design and implementationEvaluationConclusion
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS A AMHERST • MHERST • Department of Computer Science Department of Computer Science 4
Fragile performanceProtocols 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
alize
d de
lay
2.2x
Norm
alize
d de
lay
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS A AMHERST • MHERST • Department of Computer Science Department of Computer Science 5
Spatial connectivity diversityDieselNet-Hybrid
Vehicular DTN + Wifi Mesh20 buses in Vehicular DTN4 open AP WiFi mesh clusters
< 100 contacts
100 – 200 contacts
> 200 contacts
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS A AMHERST • MHERST • Department of Computer Science Department of Computer Science 6
Temporal connectivity diversityHaggle
Mobile ad hoc network8 mobile and 1 stationary imotes9 hour trace in Intel Cambridge Lab
Frac
tion
of
conn
ecte
d no
des
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS A AMHERST • MHERST • Department of Computer Science Department of Computer Science 7
Compartmentalized design harmfulFragile performance under spatio-temporal diversity
Makes interconnection of diverse networks difficult
ManageabilitySeparation of concernsLong-term innovation
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS A AMHERST • MHERST • Department of Computer Science Department of Computer Science 8
OutlineCompartmentalized design harmfulQuantifying replication gainR3 design and implementationEvaluationConclusion
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS A AMHERST • MHERST • Department of Computer Science Department of Computer Science 9
Replication: Key difference
DTN MeshMANET
Sparsely connected Well connectedIntermittently connected
Replication Forwarding
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}]
• Delay of forwarding
• Delay of replication
€
μμ(1)
€
X i
Random variable denoting the delay of path i
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS A AMHERST • MHERST • Department of Computer Science Department of Computer Science 11
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
• Delay of forwarding
• 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
5x delay improvement
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS A AMHERST • MHERST • Department of Computer Science Department of Computer Science 12
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 gain
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS A AMHERST • MHERST • Department of Computer Science Department of Computer Science 13
Compartmentalized design harmfulQuantifying replication gainR3 design and implementationEvaluationConclusion
Outline
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS A AMHERST • MHERST • Department of Computer Science Department of Computer Science 14
R3 design overviewLink-state
Estimate 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
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS A AMHERST • MHERST • Department of Computer Science Department of Computer Science 15
Link delay
Estimate link delay using periodic probes
Estimate link delay distribution
Time
30s20s
10s0.1s
Link availability delay Delay to successfully transfer pkt
Delay samples = {30.1s, 20.1s, 10.1s}
Acked probe Half of round-trip delay
Unacked probe Half of time since sending probe and
receiving an ack for subsequent probe
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS A AMHERST • MHERST • Department of Computer Science Department of Computer Science 16
R3 design overview
Src Dst
Link-stateEstimate per-link delay distribution
ReplicationSelect replication paths using modelAdapt replication to be load-aware
Source routing along selected path(s)
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS A AMHERST • MHERST • Department of Computer Science Department of Computer Science 17
First pathPath s.t. it minimizes Selected using Dijkstra’s shortest path algorithm
Second pathPath s.t. it minimizesSelected using delay distributions and model
Path selection using model
€
k( ∈{1,2,...,n})
€
E[X k ]
€
E[min{X i,X k}]
€
k( ∈{1,2,...,n})
€
X1
€
X2
€
Xn€
X iSrc Dst
€
i
€
j
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS A AMHERST • MHERST • Department of Computer Science Department of Computer Science 18
ProblemReplication hurts performance under high load
SolutionLoad aware replication
Adapting replication to load
ForwardingReplicationStart
actual_delay > 2 * model_estimated_delay
actual_delay ≤ 2 * model_estimated_delay
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS A AMHERST • MHERST • Department of Computer Science Department of Computer Science 19
R3 design overviewLink-state
Estimate per-link delay distribution
ReplicationSelect replication paths using modelAdapt replication to be load-aware
Source routing along selected path(s)
Src Dst
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS A AMHERST • MHERST • Department of Computer Science Department of Computer Science 20
Compartmentalized design harmfulQuantifying replication gainR3 design and implementationEvaluation
Deployment on a DTN and mesh testbedSimulation based on real tracesEmulation using mesh testbed
Conclusion
Outline
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS A AMHERST • MHERST • Department of Computer Science Department of Computer Science 21
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
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS A AMHERST • MHERST • Department of Computer Science Department of Computer Science 22
Experimental settingsTemporal diversity inherent in HaggleSpatial diversity inherent in DieselNet-HybridVarying load
Compared protocolsReplication: RAPID, RandomForwarding: DTLSR, AODV, OLSRMulti-configuration: SWITCH (RAPID+OLSR)
R3 Trace-driven simulation
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS A AMHERST • MHERST • Department of Computer Science Department of Computer Science
Robustness to spatial diversitySimulation based on DieselNet-Hybrid trace
23
R3 improves median delay by 2.1x
1
2
34
5
6
7
8
9
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS A AMHERST • MHERST • Department of Computer Science Department of Computer Science
Robustness to varying loadSimulation based on DieselNet-Hybrid trace
24
R3 reduces delay by up to 2.2x over SWITCH
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS A AMHERST • MHERST • Department of Computer Science Department of Computer Science 25
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
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS A AMHERST • MHERST • Department of Computer Science Department of Computer Science
Q&A
26
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS A AMHERST • MHERST • Department of Computer Science Department of Computer Science 27
Theorem: Replication gain is high iff path delays are highly unpredictable
Predictability of a random variable X = Smallest such that
When is replication gain high?
€
ε
€
P[X ≤ εE[X]] ≥1−ε
Corollary: Replication can yield unbounded gain even with two paths
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS A AMHERST • MHERST • Department of Computer Science Department of Computer Science 28
Path delay Expected delay: Delay distribution of : convolutions of
Estimating path delay distribution
€
Y1
€
Yi
€
Y2
€
Yn
€
X
€
E[X] = E[Y1]+ E[Y2]+ ...+ E[Yn ]
€
X =Y1 +Y2 + ...+Yn
€
Y1,Y2,...,Yn
€
X
Link delay distribution Path delay distribution
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS A AMHERST • MHERST • Department of Computer Science Department of Computer Science
Robustness to temporal diversity Simulation based on Haggle trace
29
R3 reduces delay by up to 60%
R3 increases goodput by up to 30%
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS A AMHERST • MHERST • Department of Computer Science Department of Computer Science
Emulating intermediate connectivity
Mesh-based emulation approach Brings link up and down
to vary connectivity Emulates connectivity
diversity (but not mobility)
30
R3 reduces delay by up to 2.2x