Embedded Networks Laboratory

Understanding Congestion Control in

Multi-hop Wireless Mesh Networks

Sumit Rangwala

Apoorva Jindal, Ki-Young Jang, Konstantinos Psounis,

and Ramesh Govindan

Embedded Networks Laboratory

Mesh Networks

• Static multi-hop mesh networks have been proposed an alternative to wired connectivity

• User’s satisfaction hinges on transport performance– TCP’s performance on 802.11 mesh

networks is known to be poor • Starvation

Is poor transport performance inherent to multi-hop mesh

networks?

Can a correctly designed transport help make mesh networks a viable

alternative?

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Embedded Networks Laboratory

TCP’s Performance

• TCP only signals flows traversing the congested link– Link centric view of congestion

• Fails to account for neighborhood congestion

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TCP

Optimal(Max Min)

What mechanisms can help us achieve near-optimal rates?

Embedded Networks Laboratory

WCPCapWCP

Approach

AIMD Based Design

Neighborhood-centric Transport

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Explicit Rate Notification

Embedded Networks Laboratory

Neighborhood of a Link

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Neighbors (overhearing)

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• Neighborhood of a link – All incoming and outgoing links of

• Sender• Receiver• One hop neighbors of the sender • One hop neighbors of the receiver

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Link → sender receiver pair

Prohibits channel captureProhibits channel capture at the sender or causes

collision at the receiverEnsuing ACK prohibits channel capture at the

sender or causes collision at the receiver

Embedded Networks Laboratory

WCP: AIMD Based Design

When a link is congested, signal all flows traversing the neighborhood of a link to reduce their rate by half, i.e.,

rf = rf / 2 React to congestion after RTTneighborhood

Multiplicative Decrease

Key Insight: Congestion is signaled to all flows traversing neighborhood of a congested link

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Embedded Networks Laboratory

WCP

During no congestion increase a flow’s rate as rf = rf + α

Every RTTneighborhood

Additive Increase

Key Insight: Rate adaptation is clocked at the largest flow RTT in a neighborhood

RTTneighborhood : Largest flow RTT within the neighborhood

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Embedded Networks Laboratory

Simulations: Stack Topology

• WCP achieves near optimal performance – Through congestion sharing in the neighborhood

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• Simulation setup– Qualnet 3.9.5 – 802.11b MAC with default

parameters– TCP SACK– Auto rate adaptation is off

Embedded Networks Laboratory

WCPCapWCP

Approach

AIMD Based Design

Neighborhood-centric Transport

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Explicit Rate Notification

Embedded Networks Laboratory

WCPCap: Explicit Rate Feedback

• Estimate residual capacity in a neighborhood– Need to know the achievable rate region

for 802.11-scheduled mesh networks• Using only local information

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Challenge: Is a given set of rates achievable in a neighborhood?

Embedded Networks Laboratory

Combine, incorporating link dependencies, individual probabilities to find net collision and idle probabilities of the link

Combine, incorporating local link dependencies, individual probabilities to find net collision and idle probabilities for the link

Calculating Achievable Rates

Decompose the neighborhood topology of a link into canonical two-link topologies

Find collision and idle time probability of the link in every two-link topology

Compute expected packet service time for a link from collision and idle probability of the link

Check feasibility, i.e., for each link, Packet arrival rate × E[service time of a packet] ≤ U,

0 ≤ U ≤ 1

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Requires global informationUsing only local information

Jindal et. al., “The Achievable Rate Region of 802.11 Scheduled Multi-hop Networks”.

Embedded Networks Laboratory

WCPCap: Explicit Rate Feedback

• Every epoch– Find, by binary search, the largest increment or

smallest decrement, δ, such that the new rates are achievable yet fair

– Increase/decrease rate of each flow by δ

U=1 (100% utilization) would yield large delays,

we target U=0.7

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Embedded Networks Laboratory

Simulations: Stack Topology

• WCPCap slightly better than WCP– Yields smaller queue and thus smaller delays– Not as good as optimal as we target 70% utilization

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• Simulation setup– Qualnet 3.9.5 – 802.11b MAC with default

parameters– TCP SACK– Auto rate adaptation is off

TCP

OptimalWCPCap

WCP

Embedded Networks Laboratory

Simulations: Diamond Topology

• WCP does not achieve max-min rates– Rates are dependent on the number of congested

neighborhood and the degree of congestion

• WCPCap achieves max-min rates

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Embedded Networks Laboratory

Experimental Setup

• Mini-PCs running Click and Linux 2.6.20– ICOP eBox-3854

• 802.11b wireless cards running the madwifi driver

• Omni directional antennas– some antennas covered

with aluminum foils to reduce transmission range

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Embedded Networks Laboratory

Experimental Results: Stack Topology

Simulations ExperimentsFor this topology, WCP’s simulation and experimental results are nearly identical

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Embedded Networks Laboratory

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Experimental Results: Arbitrary Topology

• 14 nodes and five flows• TCP starves different flows during different runs

WCP consistently gives fair rates

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Embedded Networks Laboratory

Related Work• WCP

– Congestion control schemes explicitly recognizing neighborhood

• NRED, EWCCP, and IFRC

– Congestion control for ad-hoc wireless networks• TCP-F, TCP-ELFN, TCP-BuS, ATCP, etc.• COPAS, LRED, ATP, etc.

– Congestion control for last-hop wireless networks• I-TCP, Snoop, WTCP, etc.

• WCPCap– Heuristic based capacity estimation

• WXCP and XCP-b

• Schemes that also change the MAC layer– e.g, wGDP, DiffQ

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Embedded Networks Laboratory

Conclusions and Future Work

• Demonstrate plausibility of distributed fair rate control for mesh networks – Low overhead AIMD scheme– Explicit rate feedback scheme

• Future Work– Optimizing AIMD parameters in WCP– Reduce control overhead of WCPCap– More extensive experiments

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Embedded Networks Laboratory

Thank You

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