The Impact of Multihop Wireless Channel on TCP Throughput and Loss

Zhenghua Fu, Petros Zerfos, Haiyun Luo, Songwu Lu, Lixia Zhang, Mario Gerla

INFOCOM2003, San Francisco, April 2003

Presented by Philip Hardebeck

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Outline

Introduction Background TCP Throughput

– Several Topologies: Chain, Cross, Grid, Random

Simulations, Experiments, & Analysis Proposed Solutions Conclusions

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Introduction

Do TCP mechanisms work well for Wireless Multihop Networks (WMN)?

WMNs differ from wired networks. There is an optimal TCP window size

for a given topology and flow pattern.

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More Introduction

Packet losses increase as window size exceeds optimal, up to a threshold.

Link-RED and Adaptive Pacing are proposed to increase throughput.

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Background: MAC Basics

A B C D ERTS

CTS CTS

A B C D EDATA

ACK ACK

A B C D ERTS RTS

A B C D E

RTS8 x

RTS

CTS

… random exponential backoff ...

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Spatial Reuse and Contention

A B C D E F G H

Interfering Range Communication Range

I

A B C D ERTS

CTS

Interfering/Carrier Range of Node B

RTSA B C D E

DATA

Interfering/Carrier Range of Node D

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TCP Throughput

Look at TCP throughput to show how well or poorly it performs spatial reuse.

Typical TCP operation doesn’t do a good job and the throughput is reduced.

Identify window size for highest throughput, and verify with hardware experiments.

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Chain Topology

Packets of a single flow interfere with one another.

Optimal window size is ~1/4 * number of hops in the chain.

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Optimal Window Size vs. Chain Length

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Throughput for 3 Packet Sizes

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Actual vs. Simulated Throughput

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Cross and Grid Topologies

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Aggregate Throughput and Window Size

Topology Numberof flows

OptimalThroughput(Kbps)

MeasuredThroughput(Kbps)

OptimalWindow

AveragemeasuredWindow

6-hop Chain 6 298 272 2 227-hop Chain 3 255 215 2 1613-node Cross 2 248 203 4 12169-node Grid 4 287 241 8 14169-node Grid 8 957 824 8 19169-node Grid 12 872 690 8 26200-node Random 20 1,196 1,015 - -

Table III

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Throughput Summary

Optimal window size exists for all topologies and flow patterns.

Optimal window size derivable only for simple configuration (chain).

Average TCP window size is much larger than optimal– Causes more packet drops and reduced

throughput

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Loss Behavior

Buffer drop probability is not significant in WMN, but contention drop is.

“Network overload is no longer a bottleneck link property, but a shared feature of multiple links.”

Drop probability increases “gracefully” as load increases.

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TCP Packet Drop Probability

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UDP Packet Drop Probability at MAC layer

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Contrasting Drop Characteristics

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Analysis of Link Drop Probability

Modeling a random topology, drop probability is

Three regions of behavior– Pl ~0: m, number of backlogged nodes, is <

B*, maximum number of concurrent DATA transmitting nodes, and m~b~c

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Analysis of Link Drop Probability Continued

Other two regions:– Pl increases linearly: m>B* and m<C*,

maximum number of nodes with a clear channel

– Pl stable: m>C* - the amount of contention cannot increase

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Link-RED Algorithm

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Adaptive Pacing Algorithm

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TCP Throughput Comparison

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Multiflow TCP Throughput Comparison

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Average TCP Window Size Comparison

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Discussions

TCP Vegas doesn’t work as well as New Reno.

Optimal window sizes exist for flows with variable packet size, but more complicated.

LRED and Adaptive Pacing improve drop behavior.

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Related Work

Link-layer retransmission hides channel errors from upper layers

Dynamic ad hoc networks and link failure are studied (routing issues)

Studies of TCP ACK traffic using other MAC protocols

Capacity of ad hoc networks using UDP/CBR flows

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Conclusions

TCP throughput improves if the window size operates at optimal, maximizing channel spatial reuse.

TCP typically operates with a much larger window, reducing throughput.

Wireless nodes exhibit a graceful drop feature.

LRED and Adaptive Pacing improve throughput by up to 30%

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Problems/Weaknesses

No explanation for the 10% difference between simulation and experimental results.

Use of aggregate rate and window size makes it difficult to compare results to other papers’.

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Acknowledgements

Thanks to Professor Kinicki for the opportunity to make this presentation.

Thanks to Shugong Xu and Tarek Saadawi of CUNY for the MAC Basics and Spatial Reuse and Contention graphics.

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Questions/Comments?