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?