caeyc annual conference & expo

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What do Packet Dispersion Techniques Measure?

Constantinos Dovrolis, Univ-Delaware

Parmesh Ramanathan, Univ-Wisconsin

David Moore, CAIDA

Constantinos Dovrolis - IEEE Infocom 2001 - April 24-26, 2001 1 of 28

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Overview

� Background: capacity and available bandwidth

� Dispersion of packet-pairs

� Dispersion of packet-trains

� A capacity estimation methodology: pathrate


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Part I

Background


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Definition of capacity� Maximum IP-layer throughput that a flow can get, without any cross traffic

AC

Source Sink

Link-1 Link-2 Link-3

� Ci: capacity of link i (i � �� H)

� Path capacity C is limited by narrow link n:

C � min

i��HfCig � Cn


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Definition of available bandwidth� Maximum IP-layer throughput that a flow can get, given (stationary) cross traffic

AC

Source Sink

Link-1 Link-2 Link-3

� ui: utilization of link i

� Available bandwidth A limited by tight link t:

A � min

i��HCi �� ui� � Ct �� ut�


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Part II

Packet-pair dispersion


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Packet-pair: basic idea� Packet transmission time: � � LC

� Send two packets back-to-back

� Measure dispersion � at receiver

� Estimate C as L�

C3C

=L/C∆L/CL/3C

� But.. cross traffic ‘noise’ can affect the packet dispersion �Constantinos Dovrolis - IEEE Infocom 2001 - April 24-26, 2001 7 of 28

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Previous works on packet-pair dispersion

� Largely considered cross traffic effects as ‘random noise’

� Carter and Crovella (Infocom 1997), Lai and Baker (Infocom 1999): Statistical

techniques to extract most common measurement range (mode)

� Paxson (Sigcomm 1997): observed multimodalities but did not relate them with

cross traffic

� They suggest use of maximum-size packet-pair packets


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Multimodality of packet-pair estimates� Cross-traffic causes local modes below (SCDR) and above (PNCM) capacity

mode (CM)

0 10 20 30 40 50 60 70 80Bandwidth (Mbps)

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P={100,75,55,40,60,80}, L=Lc=1500B

u=20%

Capacity Mode (CM)

Post−NarrowCapacity Mode

Sub−CapacityDispersion Range (SCDR)

(PNCM)

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P={100,75,55,40,60,80}, L=Lc=1500B

SCDR

PNCM

CMu=80%

� Heavier cross traffic load makes CM weaker


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Creation of SCDR and PNCM modes

� SCDR is caused by cross traffic interfering with packet-pair

i

L/40L/40

t

L/60 L/60 L/60

CT 21C =60Mbpsi-1

C =40Mbps t1 2

L/40 + (2 L/60 - L/40) = L/30

� PNCMs are caused by back-to-back packet-pairs after narrow link (first packet

is adequately delayed)


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Effect of cross traffic packet size� Distinct cross traffic packet sizes cause SCDR local modes

� Common Internet traffic packet sizes: 40B, 550B, 1500B

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P={100,75,55,40,60,80}, u=50%, L=1500B

Fixed CT packet size: Lc=1500B

SCDR CM

PNCMs

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P={100,75,55,40,60,80}, u=50%, L=770B

SCDR

Lc uniform in [40,1500]BVariable CT packet size:

CM

PNCM


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Effect of packet-pair size� Previous work suggests use of maximum-sized packets

� But.. this is not optimal for uncovering capacity mode

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P={100,75,55,40,60,80}, u=50%

L=100B

SCDR

Lc : uniform in [40,1500]B

CMPNCM

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P={100,75,55,40,60,80}, u=50%

L=1500B

SCDR Lc : uniform in [40,1500]B

CM


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Packet-pair dispersion: summary� Packet-pair technique: simple in unloaded paths

� Multimodal bandwidth distribution in loaded paths

� Most common measurement range (mode) is not always the capacity

� Capacity is normally a local mode (CM)

� Interfering cross traffic packets cause local modes or SCDR

� Loaded post-narrow links also cause local modes (PNCMs)

� Maximum packet size is not optimal for uncovering CM

� How can we tell which local mode is related to the capacity?


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Part III

Packet-train dispersion


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Packet-train dispersion

� What do we measure with the dispersion of packet trains?

3N 2 1 N 23 1123N

∆(N) (N)(N)∆

CT CT

∆

CT

R

RS C C C1 2 3

0 2

� Bandwidth estimate:

�N��L

��N�

� What is the effect of length N on bandwidth estimate?

� Carter & Crovella: packet-train dispersion estimates A


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Packet-train experiments� What happens as we increase the packet-train length N?

0 10 20 30 40 50 60 70 80Bandwidth (Mbps)

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P={100,75,55,40,60,80}, u=80%, L=Lc=1500B

N=2

CM

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P={100,75,55,40,60,80}, u=80%, L=Lc=1500B

CM

N=3


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Packet-train experiments (cont’)� Range of measurements decreases and becomes unimodal

� Measurements tend to Asymptotic Dispersion Rate (ADR) (less than C)

0 10 20 30 40 50 60 70 80Bandwidth (Mbps)

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P={100,75,55,40,60,80}, u=80%, L=Lc=1500B

N=5

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P={100,75,55,40,60,80}, u=80%, L=Lc=1500B

N=10Asymptotic DispersionRate R=15Mbps


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ADR in single-hop paths� For sufficiently large N :

R ��N � ��L

��

�

C�

� � u�C�

C�

� C�

� ADR is not the capacity C

� ADR is not the available bandwidth A

� For single-hop paths, we can estimate A from R

A � C��

C�R

� ��


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Effect of cross traffic routing

� Path-persistent and one-hop persistent cross traffic

1 CCCC

Source

2 3 HC

Path

0

Path

. . .

Sink

Sources SinkCross Traffic Cross Traffic

H321

H30 CCCCC

PathSource

2

Path

1. . .

3 H21Sink

Cross Traffic Sources

Cross TrafficSinks


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Packet-train dispersion in multi-hop paths� Can we derive the ADR for general cross traffic routing and paths?

� Cross traffic packets cause ‘bubbles’ between packet-train packets

� For one-hop path persistent cross traffic with Ci � C :C

� �PH

i�� ui� R �

C

� �maxi��H ui

� Lower bound: bubbles are never filled in

� Upper bound: bubbles are determined by tight link


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Packet-train dispersion: summary

� Packet-trains: do not lead to more robust capacity estimation

� Packet-trains: do not lead to available bandwidth estimation

� As N increases, measurement range decreases and becomes unimodal

� As N increases, measurements tend to ADR

� ADR is always less than capacity

� Available bandwidth can be computed from ADR in single-hop paths


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Part IV

A capacity estimation methodology


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Pathrate: a capacity estimation methodology

Phase I:

� Perform many (2000) packet-pair experiments to form distribution B

� Use packet size of about 800 bytes (maximum size: 1500 bytes)

� Determine local modes of distribution B

� Sequence of local modes in increasing order: M� fm��m�� mMg


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Pathrate: a capacity estimation methodology (cont’)

Phase II:

� Perform several packet-train experiments with certain N to get B�N�

� If bandwidth distribution not unimodal, increase N and repeat previous step

� Let �N be the minimum value of N such that B�N� is unimodal

� Let �� be range of the unique mode in B�N�

� Estimate capacity as:

C � mk � minfmi �M mi � ��gConstantinos Dovrolis - IEEE Infocom 2001 - April 24-26, 2001 24 of 28

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Example: Path f100,70,60,40,55,80,65,90,40,75,90g

� Packet-pair modesM=f9,14,17,23,26,29,33,40,44,56,75,90g

0 10 20 30 40 50 60 70 80 90Bandwidth (Mbps)

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P={100,70,60,40,55,80,65,90,40,75,90}, u=30%

L=770B

N=2

CM=40Mbps

Lc: uniform in [40,1500]B

0 10 20 30 40 50 60 70 80 90Bandwidth (Mbps)

0102030405060708090

100110120130140

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P={100,70,60,40,55,80,65,90,40,75,90}, u=30%

L=770B

N=8

ζ

Lc: uniform in [40,1500]B

=37Mbps+


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From CAIDA (San Diego) to ETH (Zurich)� Packet-pair modesM=f9,11,13,15.5, 19.5, 27, 32, 43g

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jhana (CAIDA) to drwho (Zurich), L=1500B

N=2

CM=27Mbps

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jhana (CAIDA) to drwho (Zurich), L=1500B

N=12

ζ+=24Mbps


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Pathrate verification

0 10 20 30 40 50 60 70 80Actual capacity (Mbps)

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Cap

acity

est

imat

e (M

bps)

ϖ=1Mbps

0 10 20 30 40 50 60 70 80Actual capacity (Mbps)

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Cap

acity

est

imat

e (M

bps)

w=1Mbps if C<40Mbps, w=2Mbps otherwise

ϖ ϖ=1Mbps =2Mbps

� Higher capacity values require larger bandwidth resolution �

� Adaptive selection of bandwidth resolution?


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Summary and contributions

� Capacity estimation in heavily-loaded paths is hard!

� Statistical filtering of packet-pair measurements does not work

� Use of maximum-sized packets is not optimal

� Packet-train dispersion does not estimate available bandwidth

� Available bandwidth can be computed from ADR in single-hop paths

� Pathrate takes into account effect of cross traffic on packet-trains


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