Predicting Macroscopic Dynamics in Large Distribute

For More: http://www nist gov/itl/antd/emerge

K. Mills

& J. Filliben (NIST),

D

‐Y. Cho (NIH),

& E. Schwar

For More: http://www.nist.gov/itl/antd/emerge

LSN Mini Seminar June 30, 2011

image generated with http://www.wordle.net/ applied to contents of a paper entitled “Predicting Macroscopic Dynamics in Large Distributed Systems” written by Mills, Filliben
What is the prob

No one understands how to measure

i b h i i l i f 

What are complex systems?

Large collections of interconnected components whose 

interactions lead to macroscopic behaviors macroscopic behavior in complex inf

“[Despite] society’s profound dependence on knowledge about them is primitive. [G]lobal cnetworks have quite advanced technologicatheir behavior under stress still cannot be pr

p

– Biological systems (e.g., slime molds, ant colonies, embryos)– Social systems (e.g., transportation networks, cities, economies)– Information systems (e.g., Internet, Web services, compute grids)– Physical systems (e.g., earthquakes, avalanches, forest fires) 

their behavior under stress still cannot be pr

is no science today that offers the fundamento design large complex networks [so] that thpredicted prior to building them.”— Network Science 2006, recently released 

© http://www.nationalgeographic.com/

©http://emergent.brynmawr.edu 2003 http://www.wtopnews.com/

©http://www.waag.org/realtime/

http://www.kk.org/thetechnium/images/aol-server-farm.jpg http://www.sover.net/~kenandeb/fire/hotshot.html

http://www.english.uiuc.edu/maps/depression/photoessay.htm

http://www.emulab.net/pix/pc3k-back.jpg http://autoinfo.smartlink.net/quake/quake.htm http://www.avalanche.org/

L d l d th ti f th h i l

What is the new idea? Hard Issues & Approache

Hard Issues SolutionsInvestigated a

Leverage models and mathematics from the physical

sciences to define a systematic method to measure, understand and control macroscopic behavior in the Internet and distributed software systems built on the Internet

Internet

g

1. Model Scale

• Model restriction and parameter 

• 2‐level experiment designs• Orthogonal fractional factorial (O

• Markov chains

2. Model Validation

• Sensitivity analysis• Key comparisons with empirical 

• Generating Markov chain model• Correlation analysis with clusteri• Principal components analysis

Technical Approach

• Evaluate models and analysis methods 

– Computationally tractable?– Reveal macroscopic behavior?– Establish causality?

• Evaluate distributed control techniques

Distributed Systems

Scheduler

DSI

Grid Processor

Service 

Negotiator

Agreement

Grid Processor Grid Processor

DSI

Service 

Negotiator

Agreement

Execution 

Control

Grid Processor

DSF

DSI

Service 

Negotiator

Agreement

Scheduler

DRMS 

Front-End

DRMS 

Front-End

DRMS 

Front-End

DRMS 

Front-End

DSF

spawns

spawns

negotiates

monitors

requests 

reservation

GRISGIIS

GRISGIIS

ProviderSite

Provider

Site

Scheduler

DSI

Grid Processor

Service 

Negotiator

Agreement

Grid Processor Grid Processor

DSI

Service 

Negotiator

Agreement

Execution 

Control

Grid Processor

DSF

DSI

Service 

Negotiator

Agreement

Scheduler

DRMS 

Front-End

DRMS 

Front-End

DRMS 

Front-End

DRMS 

Front-End

DSF

spawns

spawns

negotiates

monitors

requests 

reservation

GRISGIIS

GRISGIIS

ProviderSite

Provider

Site

3. Tractable Analysis

p p y

• 10‐step graphical analysis• Cluster analysis• Custom multidimensional visuali

• Exploratory interactive multidim• Eigenanalysis of matrices

4. Causal Analysis

• Principal components analysis• Detailed measurements of mode• Time series analysis• Hypothesistesting• Exploratory analyses

2

• Evaluate distributed control techniques

– Can economic mechanisms elicit desired behaviors?– Can biological mechanisms organize elements?

Task

Control

Negotiation

Control

CLIENT

Application

Client 

Negotiator

Task 1

Discovery

Control

Task 2 Client 

Negotiator

Task 3

Task

Control

Negotiation

Control

Application

Task 1

Discovery

Control

Task 2

negotiates

monitors

spawns

Supervisory Process Supervisory Process

spawns

GIIS

ClientSite

Task

Control

Negotiation

Control

CLIENT

Application

Client 

Negotiator

Task 1

Discovery

Control

Task 2 Client 

Negotiator

Task 3

Task

Control

Negotiation

Control

Application

Task 1

Discovery

Control

Task 2

negotiates

monitors

spawns

Supervisory Process Supervisory Process

spawns

GIIS

ClientSite

• Cut set analysis of graphs and pe

5. Controlling Behavior

• Economic algorithmsfor resourc

• Proposed Internet congestion co• Heuristicsfor resource allocation 

June 30, 2011

Innovations in Measurement Science
Outline

• Introduction to the Internet Congestion

Control Problem

• Methods Methods to Reduce Stimulus Stimulus and Respo

Respo

State Spaces

• Selected Methods Methods to Analyze Analyze

Multidimensional Datasets

• Application to Predict Macroscopic Dyn

of Proposed TCP Replacements

June 30, 2011

Innovations in Measurement Science 
Introduction to the Problem

June 30, 2011

Innovations in Measurement Science 4
Internet Congestion Control & TCP

Phases in a TCP Flow Phases in a TCP Flow Utili ti M h L 

Utilizations Much Lower o

Long-Delay Pa

Router #1 162 msround trip Router #2

21 segments/ms 21 segments/ms

bottleneck path

252 Mbps Bottleneck Path

Router #1680 buffers

Router #2680 buffers

162 msround trippropagation delay

Source #1 Source #2 Receiver#1 Receiver#2

80 segments/ms 80 segments/ms 80 segments/ms 80 segments/ms

40005000

ments)avg. cwnd = 2745 avg. cwnd = 3042

TCP Flows Achieve 89 % Utilization So Researchers Pro

TCP Replaceme

Identifier Acronym Name of Algorith

Bi I 

0

100020003000

0 2000 4000 6000 8000 10000

cwnd (segmavg.red cwnd = 1844

avg. blue cwnd = 1196

1 BIC Binary Increase 

Control

2 CTCP Compound TCP3 FAST Fast Active Que

Management Sc

4 HSTCP High Speed TCP5 HTCP Hamilton TCP

0 2000 4000 6000 8000 10000 6 STCP Scalable TCP

Time (0.1 second units)

June 30, 2011

Innovations in Measurement Science 5

6 STCP Scalable TCP7 TCP Transmission Co

Protocol
Proposed TCP Replacements Well‐Studied on Bott

2000300040005000

segments)avg. cwnd = 3607 avg.red cwnd = 2243 avg. cwnd = 3747 2000 3000 4000 5000 segments)avg. cwnd = 2745 avg.red cwnd = 1853 avg. cwnd = 3406 2000 3000 4000 5000 segments)avg. cwnd = 

BIC CTCP 

0

1000

2000

0 2000 4000 6000 8000 10000

cwnd(s

Time (0.1 second units)

avg. blue cwnd = 1504

0

1000

2000

0 2000 4000 6000 8000 10000

cwnd(s

Time (0.1 second units)

avg. blue cwnd = 1553 0

1000

2000

0 2000 

cwnd(s

Ti

5000 5000 5000

HSTCP HTCP Sca

10002000300040005000

cwnd (segments)avg. cwnd = 3485 avg.red cwnd = 2170 avg. blue cwnd = 1348 avg. cwnd = 3518 1000 2000 3000 4000 5000 cwnd (segments)avg. cwnd = 3385avg.red cwnd = 1869 avg. blue cwnd = 1676 avg. cwnd = 3545 1000 2000 3000 4000 5000 cwnd (segments)avg. cwnd = 3

0

0 2000 4000 6000 8000 10000

c

Time (0.1 second units)

avg. blue cwnd 1348

0

0 2000 4000 6000 8000 10000

c

Time (0.1 second units)

0

0 2000 

c

Time 

June 30, 2011

Innovations in Measurement Science 6
What would be the macroscopic dynamics if these 

TCP replacements were dep y lo ed in the Inte

Four-Tier Topology (based on the Abilene Network) with 11 Backbone Routers (A-K), 22 Poin

Routers (A1-K2) and 139 Access Routers (A1a-K2d) – 6 red and 28 green Access Routers ma

different speeds from the 105 others – not shown is a fourth tier of hundreds of thousands 

June 30, 2011

Innovations in Measurement Science 7

computers
Stimulus and Response State Spaces Are Lar

Network Simulation Models

y1, ..., yz = f(x1|[1,...,l] ..., xp|[1,...,l])

ResponseSt t a e‐Space Sti l mu us St t a e‐Space

e.g., p = 1000 and l = 232 e.g., z = 500 

June 30, 2011

Innovations in Measurement Science 8
Methods to Reduce the Scale of the Stimu

Response Spaces

June 30, 2011

Innovations in Measurement Science 9
We Employ Stimulus State

‐Space

Reduction Techniques

Category Identifier N

X1 N

NIST Meso

Reduction Techniques

(232)1000 O(109633) [ 1080

= atoms in visible universe]

NetworkConfiguration

X2 PX3 BX4 To

User

X5 WX6 PX7 Th

(232)

Discard parameters not germane to study– reduce by 944 parameters

O(109633) [ 1080

= atoms in visible universe]

(232)56 O(10539)

Behavior X8 Pa

X9 Se

X10 Lo

Sources &Receivers

X11 So

X12 N

X13 D

220

(232)20 O(10192)

O(10

6

)

ParameterReduction

Group related remaining parameters– reduce by 36 parameters

Select only

2 values for each parameter

Level

X14 D

Protocols

X15 CX16 InX17 In

Simulation Control & 

M t

X18 MX19 S

Use experiment design theory to reduce

parameter combinations to 256

Use sensitivity analysis

220

220‐12O(10

6

)

Orthogonal 256Fractional Factorial

Reduction

Measurement X20 St

to identity six most

significant parameters

Fractional‐Factorial(OFF) Experiment

Design

2

6‐1 32

Use experiment design theory again to reduce

parameter combinations to 32

SensitivityAnalysis

OFFExperiment

June 30, 2011

Innovations in Measurement Science 10

ExperimentDesign
We Employ Response State‐Space Reduction Te

(here we demonstrate correlation analysis & clu

Response Definition

y1 Number of sources transmittingy2 Proportion transmitting [y1 / all sources]y3 Number segments entering network

Response Correlation M

y4 Number of segments leaving networky5 Loss Rate [y4/ (y3 + y4)]y6 Flows completing per intervaly7 Flow-completion rate [y6/(y6+y1)]y8 Connection failures per intervaly9 Connection-failure rate [y8/(y8+y1)]

y10 Retransmission ratey11 Congestion window (cwnd)y12 cwnd increases per intervaly13 Duplicate ACKs per interval

Response Definition

y17 Average Segments/Second on DD flows

y14 Timeouts per intervaly15 Round-trip time (rtt)y16 Queuing delay

Red 80 > |r|x100 < 100 Blue 30 > |r|x100 < 80 Gre

y18 Average Segments/Second on DF flowsy19 Average Segments/Second on DN flowsy20 Average Segments/Second on FF flowsy21 Average Segments/Second on FN flowsy22 Average Segments/Second on NN flows

June 30, 2011

Innovations in Measurement Science 11

Red 80 > |r|x100 < 100 Blue 30 > |r|x100 < 80 Gre
Decide Which Pairs of Correlated Responses to 

Frequency distribution of |r| for pair-wise correlations – bins highlighted for |r

June 30, 2011

Innovations in Measurement Science 12
Cluster Retained Correlated Responses

2 responses uncorrelated

(1) throughput on DD flows

25 correlatpairs refleccongestion

(2) flow completion rate

14 correlationpairs reflectingpacket losses

3 pair-wise correlations: wise correlations:

(1) throughput on flows constrained by F-class routers

(2) network delay(3) packets entering and leaving the network

Figure 9: Index-Index plot sorted by increasing count of correlation pairs to identify clusters of mutual correlations thaseven behavioral dimensions in the response state-space of MesoNet 

Index-Index Plot Sorted by Increasing Count of Correlated Pairs to Indentify Cl

Mutual Correlations that Represent Seven Behavioral Dimensions

June 30, 2011

Innovations in Measurement Science 13
Selected Methods to

Analyze Multidimensional Dataset

June 30, 2011

Innovations in Measurement Science 14
Even with Response State‐Space Reduction Com

Comparisons Comparisons Can Yield Large, Multidimensional Multidimensional 

For examp, p g g g p le, comparing 7 congestion control algorithms with respecresponses under 32 conditions over three time periods with a mixturcompeting TCP flows yields a multidimensional dataset with > 64,00

We used a range of multidimensional data analysis techniques, including:1. Main-Effects Analysis [a b c] (skipping this) Effects Analysis [a, b, c] (skipping this)2. Cluster Analysis [a, b]3. Primary Principal Components Plots (skipping this4. Condition-Response Summaries [a, b, c]5. Biplots (skipping this)6. Colored Bar Graphs (skipping this) 7. Rank Analysis (skipping this)

June 30, 2011

Innovations in Measurement Science 15
2 (a) Cluster Analysis

Compute normalized Euclidian distance between a pair of congestion-control algorithms in 45 dimensional space (45 responses used in this case)Compute linkage between clusters ofcongestion-control algorithms

Plot as dendrogram and color when the linkagevalue for a pair of clusters falls below 70 % of stance value for a pair of clusters falls below 70 % ofthe maximum linkage value 

Algorit

Dis

Note that congestion-control algorithms may only be compared usame conditions (i.e., parameter combinations) because the condvery different from each other – this leads to the need to plot one dendrogram for each condition (32 here)June 30, 2011

Innovations in Measurement Science 16

dendrogram for each condition (32 here)
2 (b) Dendrograms for Each of 32 Conditio

Identical under no congestion; Similar under low congestion; algorithm 3 distinct unde

Similar 

3 33 3

Id ti l

3 Sim

33 33 

IdenticalSi il m ar 33 3 333 

Similar 3 33 3 3

June 30, 2011

Innovations in Measurement Science 17
4 (a) Condition‐Response Summaries: Underlying 

Plot shows thatretransmissionrate for congestion rate for congestioncontrol algorithm 3

is an outlier underconditions withhigh congestion, and

the higher the level ofcongestion, the morethe algorithm is anoutlier.

Sample plot analyzing the influence of condition and congestion control algorithm on the segment this experiment retransmission rate was designated as y6) – y axis gives residuals around the mea

diti d i i diti d d b i i f id l

Grubbs’ test of outliers

June 30, 2011

Innovations in Measurement Science 18

condition and x axis gives conditions ordered by increasing range of residuals 
4 (b) Condition‐Response Summaries: Rollup Out

Underlying Analyses

Condition-response summary identifying any statistically significant outliers among congestion con

June 30, 2011

Innovations in Measurement Science 19

Condition response summary identifying any statistically significant outliers among congestion con

each of 45 responses measured over 32 different conditions in this particular experiment 
4 (c) Condition‐Response Summaries: Apply 

Filtered condition-response summary identifying any congestion control algorithms that are statisti

June 30, 2011

Innovations in Measurement Science 20

Filtered condition response summary identifying any congestion control algorithms that are statisti

outliers and that exhibit at least a 10 % relative effect for each of 45 responses measures over 32 dif
Application to Predict Macroscopic Dyna

Proposed TCP Replacements

June 30, 2011

Innovations in Measurement Science 21
Experiments Conducted

How do the proposed TCP replacements react toand recover from spatiotemporal congestion?

Compared seven proposedTCP replacements (excludedFAST-AT) in a large (up to

How do the proposed TCP replacemthroughputs and affect competing T

Compared eight replacements in 26,085 sources), 

Experiment #1a

) g ( p

278,000 sources), fast (up to192 Gbps backbone) network;Web browsing users; 25minutes simulated; three 5-minute time periods; large (> 1billi t ) i iti l t ll

Experiment #2a

, ), 

Gpbs backbone) browsing users users who downlmovies; 60 minute(> 1 billion segm

billion segments) initial sst; all sources use the samecongestion control algorithmSame as #1a, except; smaller(up to 27,800 sources) andslower (up to 28 8 Gpbs

some sources ucongestion controsome sources proposed TCP rep

Experiment #2b Same as #2a ex

segments) initial s

Experiment #1b slower (up to 28.8 Gpbs backbone) network and low

(100 segments) initial sst.(Added FAST-AT)

segments) initial s

Experiment #2c

Same as #2a exceto 261,792 sources384 Gbps backbon

June 30, 2011

Innovations in Measurement Science 22
What distinguishes among proposed TCP replac

Will they be useful? Will they be safe?

1. Increase rate: How quickly can the maximum transmission rate be a2. Loss/Recovery processing processing:

a. How much does the protocol reduce transmission rate upon a l

b. How quickly does the protocol increase transmission rate after 

reduction?

3. TCP Fairness: How well do standard TCP flows do when competing w

proposed TCP replacements?

4. Utility bounds: Under what circumstances can proposed TCP replace

provide improved user throughputs?

5. Safety: Will wid d espread d l ep oyment of proposed TCP replacements 

undesirable macroscopic dynamics into the Internet?

June 30, 2011

Innovations in Measurement Science 23
Increase Rate

3⁄4 Assuming low congestion, setting of initial sst is a key factor

 High initial sst – all algorithms (standard TCP included) achieved maximum

transmission rate with the same (exponential) quickness

 Low initial sst – p p ro osed TCP replacements achieved maximum transmissio

more quickly than (linear) increase of standard TCP

3⁄4 Under heavy congestion, setting of initial sst matters little because initia

slow start terminates terminates upon first packet loss and a flow enters congestion congestionavoidance, where loss/recovery processing determines throughput

3⁄4 On real TCP flows receivers may convey a window (rwnd) that can restric

throughput because sources pace transmission based on min(cwnd, rwn

Typically, rwnd < cwnd. In our studies, we assume an infinite rwnd in

order to compare effects of congestion control algorithms. Throughput o

TCP flows in a real network network might be constrained constrained by rwnd, so that propo

propo

replacements would provide little advantage over standard TCP.

In fact, even TCP congestion control does not have much influence when

rwnd < cwnd.

June 30, 2011

Innovations in Measurement Science 24
Loss/Recovery Processing

3⁄4 One group of algorithms (Scalable TCP, BIC1 and HSTCP) reduce transmiss

rate less than standard TCP after a packet loss

 Unfair to TCP flows and to new flows using alternate alternate algorithms algorithms

3⁄4 Another group of algorithms (CTCP, FAST and FAST‐AT) reduce transmissi

rate by 1⁄2 following a loss (HTCP is a hybrid with reduction between 20 a

 These al h gorit ms seek to obtain h h ig er th h roug put by increasing transmissio

more quickly than standard TCP (the rate of increase varies with the algorith

 HTCP reverts to TCP congestion avoidance for 1 s after each loss, which

can lead to lower throughputs than other alternate algorithms

3⁄4 Under extreme spatiotemporal congestion, most proposed TCP replacem

a low‐window threshold and revert to standard TCP congestion avoidanc

procedures (g g ivin no advantage to alternate p ) rocedures)

 FAST and FAST‐AT do not use TCP congestion avoidance under any

conditions, which can lead to oscillatory behavior and increased loss rates

1Note that on repeated losses occurring close in time, BIC can reduce cwnd substantially more th

June 30, 2011

Innovations in Measurement Science 25

p g , y

standard TCP – thus, on paths with very severe congestion TCP can provide higher throughput t
TCP Fairness

3⁄4 All proposed proposed TCP replacements replacements take steps to provide provide improved improved throughp

throughp

thus comparing fairness must consider relative performance of TCP flows 

competing with flows using each proposed replacement

3⁄4 We found CTCP, HTCP and FAST‐AT to be most fair to TCP flows

 Under low initial sst FAST‐AT is more unfair because of its quick increase in ra

 Injecting more FAST‐AT packets induced more losses in TCP flows, which cou

recover only linearly

3⁄4 We found Scalable TCP, BIC and FAST to be most unfair to TCP flows

 Established Scalable and BIC flows (on large files) tended to maintain higher

transmission rates than TCP flows after losses, while FAST recovered more q 

u

and these proposed TCP replacements induced more losses in TCP flows

3⁄4 HSTCP appeared moderately fair to TCP flows, especially under condition

lower congestion congestion and under low initial initial sst – HSTCP appeared appeared unfair undeconditions of heavy congestion

3⁄4 We found that Scalable TCP, BIC and HSTCP are also unfair to competing

June 30, 2011

Innovations in Measurement Science 26

flows that are newly arriving
Utility Bounds

3⁄4 We found that proposed TCP replacements could provide increased

utility (throughput) for users – however, this utility would arise only und

a specific combination of circumstances

 Flow’s rwnd must not be constraining flow transmission rate

 Flow’s initial sst must be relatively low Flow must be transferring a large file

 Flow’s packets must be transiting a relatively uncongested path (i.e., experie

only sporadic losses) or else users must be willing to tolerate marked unfair

in trade for increased throughput

3⁄4 How likely is this combination of circumstances on a given Internet flow

 Certainly possible to engineer a network, or segments of a network, to prov

specific users with improved throughput compared with TCP

 We suspect a rather low probability for such circumstances to arise generall

in the Internet

3⁄4 We conclude that proposed TCP replacements can provide improved

user throughput – however, most users seem unlikely to benefit very oft

June 30, 2011

Innovations in Measurement Science 27
Safety

3⁄4 We can answer this only in part – addi i l dditional cautionary fi di n ngs may be 

 We simulated either homogeneous networks where all flows used one

congestion control algorithm or mixes of TCP flows competing with flows

using one proposed TCP replacement at a time

 The real Internet could contain a mix of many d ff i erent types of congestion 

3⁄4 For most algorithms we studied, under most conditions, we found little

significant change in macroscopic network characteristics

3⁄4 FAST and FAST‐AT are exceptions to this general finding

 Under high spatiotemporal congestion, where there were insufficient buffer

support support flows transiting transiting specific specific routers, routers, FAST and FAST‐AT entered entered an oscill

oscill

behavior where the flow cwnd increased and decreased rapidly with large

amplitude

 Under such conditions the network showed increased loss and retransmissi

a higher number of flows pending pending in the connecting connecting state and a lower numbof flows completed over time

3⁄4 We recommend the need for additional study of FAST and FAST‐AT prior 

widespread deployment and use on the Internet

June 30, 2011

Innovations in Measurement Science 28

widespread deployment and use on the Internet
Omitted Slides

June 30, 2011

Innovations in Measurement Science 29
1(a) Sample Main‐Effects Analysis on One Res

Main effects plot showing absolute and relative influence of each MesoNet parameter (x axis) on 

number of sources transmitting (y1) and identifying six statistically significant parameters – netw

June 30, 2011

Innovations in Measurement Science 30

g (y ) y g y g p 

topology (X4), file size (X5), think time (X7) and number (X12) and distribution (X13) of sources
1 (b) Summary of Main‐Effects Analyses Across 18 

M i

Network User Behavior Source/Receiver Protocol 

Significance of influence of 20 MesoNet parameters (columns) on 18 macroscopic responses (row

period: blue cells indicate significance p < 0.01 and orange cells indicate significance p < 0.05, whighlighted cells indicates the parameter setting that causes an increase in the corresponding re

Metric

Class Y#

X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X

Flows

Y1 ‐** +** +* ‐** +** +** +**

Y2 ‐** +** +** ‐** +** +**Y3 +** +** +** ‐** +** ‐** ‐** +Y4 ‐** ‐** ‐** +** ‐** +** +** ‐*Y5 +** +** +** ‐** ‐** +** +* 

Congestion

Y6 ‐** ‐** ‐** +** ‐** +** +** +**

Y7 +*Y8 ‐** ‐** ‐** +** +** +** +**Y9 ‐** +** +** +* +* Delay Y9 + + + + Y10 ‐** +** +** ‐* +** +**

Aggregate

TP

Y11 +** ‐* +** ‐** ‐** +** +**

Y12 +** +** +** +** ‐** ‐** +**

Long‐Lived Y13 +** +* +* +** ‐** Y14 +* +** +** ‐** Flow TP Y14 + + + Y15 +** +** ‐* ‐*

Other Flow

TP

Y16 +** ‐** ‐** +* +** ‐** ‐* +

Y17 +** ‐** +* +** +** ‐** +** ‐** +** Y18 +** ‐** ‐** +** ‐** ‐** +**

June 30, 2011

Innovations in Measurement Science 31
1 (c) Summary of Parameter Influence on Respons

Time Periods and Two Congestion Control Algo

 = (|{y | p < 0.01}| + 1⁄2 |{y | p < 0.05}|) / |{y

Parameters with largest influence: network speed (x1), distribution of sopropagation propagation delay (x2), user think time (x7), buffer size (x3) and file size (number of sources (x12)

June 30, 2011

Innovations in Measurement Science 32
Higher Network Speed

& Longer Propagation Delay

3 Primary Principal Components Plots

Lower Network

g pg y

SmallerFile Size

Here,congestion- Lower Network

Speed

Smalle

g

controlalgorithmdoes notmake a

LargerFile Size

Larger

SmalleFile Siz

make adifference

High& S

D

File Size

PC1 (x axis) vs. PC2 (y axis) from a multivariate dataset containing average throughput achieved on 

using seven different congestion control algorithms under 32 different parameter combinations. Ci

f i t d li ithi i l di ti i h dditi l b

June 30, 2011

Innovations in Measurement Science 33

of points and lines within circles distinguish additional subgroups.
5 Biplots

Under these Scalable TCHSTCP apphigher throhigher throthe expense flows

Seven biplots, where each biplot compares throughput (in units of 100 segments per second) for laover very fast paths with fast interface speeds using a proposed TCP replacement (x axis) with throcompeting flows using standard TCP – here a high initial slow-start threshold is used 

June 30, 2011

Innovations in Measurement Science 34
6 Colored Bar Graphs

FAST & FAST-AT provide best throughput, while CTCP provides s

Legend

Note that TCP flows achieve only 20 throughput, as was measured empiriresearchers

Fraction (y axis) of maximum available throughput achieved when transferring movies over uncong

maximum rate of 1 Gbps – each of the leftmost seven bars – y2(u) – represents flows using one of treplacements for TCP (see legend), while each of the rightmost seven bars – y16(u) – represents flo

and competing with flows using one of the proposed TCP replacements (see legend) here a low in

June 30, 2011

Innovations in Measurement Science 35

and competing with flows using one of the proposed TCP replacements (see legend) – here a low in

threshold is used. 
7 Rank Analyses

CTCP leads high ranking throughputs under a wide range of c

1.4

ll Flows HTCP 1.6 All Flows F

low initial slow-star

high initial slow-start threshold 

0.8

1

1.2

CTCP

roughput Rank for Al

FAST

SCALABLE

0 8

1

1.21.4 HTCP 

roughput Rank for A

SCALABLE

0.20.40.6

FAST-AT

ndard Deviation in Th HSTCP BIC 0.2 0.4 0.6 0.8 

dard Deviation in Thr HSTCP BIC

0

3 3.5 4 4.5 5

Average Throughput Rank for All Flows

Stan

0

3 3.5 4 

Average Throughput Rank 

Stand

Average throughput rank (x axis) vs. standard deviation in throughput rank (y axis) for flows using ereplacement and for competing flows using standard TCP

June 30, 2011

Innovations in Measurement Science 36