WBest: a Bandwidth Estimation Tool for IEEE 802.11 Wireless

Networks

Presented by Feng Li (lif@cs.wpi.edu)

Mingzhe Li, Mark Claypool, and Robert Kinicki{lmz, claypool, rek}@cs.wpi.edu Department of Computer Science,Worcester Polytechnic Institute,

Worcester MA, 01609 USA

33rd IEEE Conference on Local Computer Networks (LCN), Montreal, Quebec, Canada, October 16th,2008

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Motivation Bandwidth estimation techniques focus on

network capacity or available bandwidth.

Most bandwidth estimation involved only wired networks.

This paper presents a new Wireless Bandwidth estimation tool, WBest, designed for fast, non-intrusive, accurate estimation of available bandwidth over wireless LANs.

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Challenges on Bandwidth Estimation

Traditional approaches. (e.g. pathChirp v2.4.1 [Ribeiro 2003], pathload v1.3.2 [Jain 2003] etc.) – Designed for precisely estimate the bandwidth

in wired networks.– Converge based on searching algorithms.– Provide limited bandwidth information.

Impacted by wireless networks.(e.g. shared media, retransmission, interference etc),– Inaccurate results.– Long estimation time.– High intrusiveness.

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Capacity Estimation with Packet Dispersion

Bottleneck router

),max(i

inout C

L

outi

LC

L : Packet sizeCi: Bottleneck capacity∆in: Initial gap∆out: Dispersed gap

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Example: Packet Dispersion with Wireless

Contention

Access Point

Client A

Client B

Probing traffic

Contending traffic / Co-channel interference

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Outline

Motivation and Backgrounds WBest Algorithm Evaluation Experiments Result Analysis Conclusions

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Terminology

Effective Capacity (Ce )– Maximum possible bandwidth that a link or end-to-

end path can deliver.

Available Bandwidth (A )– Maximum unused bandwidth at a link or end-to-end

path in a network. – Typically, it is a time-varying metric.

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Wireless Bandwidth Estimation Tool (WBest)

Objective– Fast, low intrusiveness, adequately accurate

estimation of available bandwidth and variance of bandwidth in wireless networks.

Two-step algorithm– Packet pair technique to estimate effective

capacity (Ce) of wireless network.

– Packet train technique to estimate mean and standard deviation of available bandwidth (A).

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WBest Assumptions Assume last hop wireless network (hth

hop) is bottleneck link with a single FCFS queue and:

1,...1

)min(

hi

ie ACA

Assume no significant changes in network conditions between two steps (estimating Ce and A).

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Estimating Effective Capacity (Ce)

Send n packet pairs to estimate Ce:

ii T

LC

– Ti : dispersion time of ith packet pair (seconds),

– L : packet size (bytes).

Use median of n estimations to minimize impacts of crossing and contending traffic.

, .., n ), i median(C C ie 1

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Estimating Available Bandwidth (A)

A packet train of m packets is sent at effective capacity (Ce) to estimate available bandwidth (A).

ee

e

C

R

SR

R

SC

C

'

1 ..., ,1)(

miTmean

LR

i

FCFS queuing at AP.

─ R : dispersion rate S : crossing/contending traffic

─ S’ : reduced crossing/contending traffic

Estimate contending and crossing traffic (S) using dispersion rate (R)

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Estimating Available Bandwidth (A) (cont’d)

R

CCSCA e

ee 2

Mean available bandwidth (A).

ee

e

C

R

SR

R

SC

C

'

SCA e

Fig 3 Estimating Available Bandwidth using Average Dispersion Rate (R).

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WBest Algorithm

2nd Phase

Calculating A

Error

Correction

1st Phase

Calculating Ce

m = 30

n = 30

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Outline

Motivation and Background WBest Algorithm Evaluation Experiments Result Analysis Conclusions

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Evaluation Setup

Build testbed– Open source drivers– Wireless sniffer

Various wireless conditions– Traffic load– Power saving mode– Rate adaptation

Implementation of WBest

Compare with:– IGI/PTR v2.0 [Hu 2003]

(PGM/PRM)– pathChirp v2.4.1 [Ribeiro

2003] (PRM)– pathload v1.3.2 [Jain 2003]

(PRM)

Client C

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Experiment Design 14 cases were designed to evaluate four

bandwidth estimation tools under

different network conditions. Each of 14 cases were repeated 30 times. All clients were placed with pre-selected

locations with RSSI range between -38 and -42 dBm.

All experiments were run during summer break to eliminate effects from occasional wireless activities.

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Result-Convergence Time vs. Error

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Result-Intrusiveness vs. Error

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Future Work Apply WBest to multimedia streaming

applications to improve media performance and playout buffer optimization on wireless networks.

Evaluate WBest performance under more complex wireless environments.

Enhance WBest robustness during AP queue overflow.

Develop new metric to replace Available Bandwidth (A) when TCP flows involved.

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Conclusions Current bandwidth estimation tools are

significantly impacted by wireless network conditions, such as contention or rate adaptations.

Current tools are generally impractical for applications such as streaming multimedia that require fast, accurate and low intrusive bandwidth estimation.

WBest consistently provides fast available bandwidth estimation, with generally more accurate estimates and lower intrusiveness under all conditions evaluated.

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Question ?

WBest with source code is available at: http://perform.wpi.edu/downloads/#wbest

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Thank You!

WBest: a Bandwidth Estimation Tool for IEEE 802.11 Wireless Networks

Presented by Feng Li (lif@cs.wpi.edu)

Mingzhe Li, Mark Claypool, and Robert Kinicki{lmz, claypool, rek}@cs.wpi.edu Department of Computer Science,Worcester Polytechnic Institute,

Worcester MA, 01609 USA

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Reference [Hu 2003] Ningning Hu and Peter Steenkiste,

“Evaluation and characterization of available bandwidth probing techniques,” IEEE Journal on Selected Areas in Communications, vol. 21, no. 6, Aug. 2003.

[Ribeiro 2003] V. Ribeiro, R. Riedi, R. Baraniuk, J. Navratil, and L. Cottrell, “pathchirp: Efficient available bandwidth estimation for network paths,” in PAM ’03, La Jolla, CA, USA, Apr. 2003.

[Jain 2003] Manish Jain and Constantinos Dovrolis, “End-to-end available bandwidth: Measurement methodology, dynamics, and relation with tcp throughput,” IEEE/ACM Transactions in Networking, , no. 295-308, Aug. 2003.

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Analysis of Number of Packet Pairs

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Analysis of Length of Packet Train