R3: R obust Replication Routing in Wireless Networks with Diverse Connectivity Characteristics

R3: Robust Replication Routing in Wireless Networks with Diverse Connectivity Characteristics Xiaozheng Tie, Arun Venkataramani, Aruna Balasubramanian University of Massachusetts Amherst University of Washington

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?

Outline • Compartmentalized design harmful • Quantifying replication gain • R3 design and implementation • Evaluation • Conclusion

Fragile performance • Protocols perform poorly outside target environment • Mesh protocols perform poorly in DTNs • No contemporaneous path • DTN protocols perform poorly in mesh • Replication wasteful 2.2x 2.1x Normalized delay Normalized delay DTN testbed Mesh testbed

Spatial connectivity diversity • DieselNet-Hybrid • Vehicular DTN + Wifi Mesh • 20 buses in Vehicular DTN • 4 open AP WiFi mesh clusters < 100 contacts 100 – 200 contacts > 200 contacts

Temporal connectivity diversity • Haggle • Mobile ad hoc network • 8 mobile and 1 stationary imotes • 9 hour trace in Intel Cambridge Lab Fraction of connected nodes

Compartmentalized design harmful • Fragile performance under spatio-temporal diversity • Makes interconnection of diverse networks difficult • Manageability • Separation of concerns • Long-term innovation

Replication: Key difference DTN Mesh MANET Well connected Sparsely connected Intermittently connected Forwarding Replication Key question: Under what conditions and by how much replication improves performance?

Model to quantify replication gain Src Dst Random variable denoting the delay of path i • Delay of forwarding • Delay of replication Replication gain 10

Example of replication gain Src Dst Replication gain depends on path delay distributions, not just expected value • Delay of forwarding • Delay of replication Replication gain 5x delay improvement 11

Replication gain vs. number of paths • Trace-driven analysis on DieselNet-DTN and Haggle Two paths suffice to capture much of the gain Haggle Vehicular DTN in DieselNet

R3 design overview • Link-state • Estimate per-link delay distribution • Replication • Select replication paths using model • Adapt replication to be load-aware • Source routing along selected path(s) Dst Src

Estimate link delay distribution • Link delay • Estimate link delay using periodic probes Delay to successfully transfer pkt Link availability delay Time 0.1s 10s 20s 30s 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

R3 design overview Src Dst Link-state • Estimate per-link delay distribution Replication • Select replication paths using model • Adapt replication to be load-aware Source routing along selected path(s)

Path selection using model • First path • Path s.t. it minimizes • Selected using Dijkstra’s shortest path algorithm • Second path • Path s.t. it minimizes • Selected using delay distributions and model Src Dst

Adapting replication to load • Problem • Replication hurts performance under high load • Solution • Load aware replication actual_delay > 2 * model_estimated_delay Replication Forwarding Start actual_delay ≤ 2 * model_estimated_delay

R3 design overview • Link-state • Estimate per-link delay distribution • Replication • Select replication pathsusing model • Adapt replication to be load-aware • Source routing along selected path(s) Src Dst

Outline • Compartmentalized design harmful • Quantifying replication gain • R3 design and implementation • Evaluation • Deployment on a DTN and mesh testbed • Simulation based on real traces • Emulation using mesh testbed • Conclusion

R3 Deployment • DieselNet DTN testbed • 20 buses in a 150 sq. mile area • Mesh testbed • 16 nodes in one floor • Simulator validation using DieselNet deployment • < 10% of deployment result

R3 Trace-driven simulation • Experimental settings • Temporal diversity inherent in Haggle • Spatial diversity inherent in DieselNet-Hybrid • Varying load • Compared protocols • Replication: RAPID, Random • Forwarding: DTLSR, AODV, OLSR • Multi-configuration: SWITCH (RAPID+OLSR)

Robustness to spatial diversity • Simulation based on DieselNet-Hybrid trace 9 6 1 5 8 2 4 3 7 R3 improves median delay by 2.1x

Robustness to varying load • Simulation based on DieselNet-Hybrid trace R3 reduces delay by up to 2.2x over SWITCH

Conclusion • Compartmentalized design harmful • R3 ensures robust performance across diverse connectivity characteristics • Unified link metric based on delay distributions • Replication based on delay uncertainty model • Adaptive replication based on network load

Q&A

When is replication gain high? • Theorem: Replication gain is high iff path delays are highly unpredictable • Predictability of a random variable X = Smallest such that Corollary: Replication can yield unbounded gain even with two paths

Estimating path delay distribution • Path delay • Expected delay: • Delay distribution of : convolutions of Link delay distribution Path delay distribution

Robustness to temporal diversity • Simulation based on Haggle trace R3 reduces delay by up to 60% R3 increases goodput by up to 30%

Emulating intermediate connectivity • Mesh-based emulation approach • Brings link up and down to vary connectivity • Emulates connectivity diversity (but not mobility) R3 reduces delay by up to 2.2x

R3: R obust Replication Routing in Wireless Networks with Diverse Connectivity Characteristics