Modeling Internet Application Traffic

for Network Planning and Provisioning

Takafumi 　 ChujoFujistu Laboratories of America, Inc.

Traffic mix on converged IP networks

ROBERT B. COHENROBERT B. COHEN, GRID COMPUTING AND THE GROWTH OF THE INTERNET, GGF 41GRID COMPUTING AND THE GROWTH OF THE INTERNET, GGF 41

IP TRAFFIC MIX - P2P SCENARIO

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2001 2002 2003 2004 2005 2006 2007 2008

SHAR

E OF T

OTAL

TRAF

FIC

WEB PAGES

RICH MEDIA

P2P

S2S

IP TRAFFIC BY TYPE - JP MORGAN-MCKINSEY

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1999 2000 2001 2002 2003 2004 2005

WEB PAGES

RICH MEDIA

P2P

S2S

Next-generation application traffic demands

Current metro collects traffic from local users and send it to core and distributes the traffic from core to the users.

Future metro Suppor

ts randomly fluctuating, bursty traffic with randomly distributed peers.

Metro

PoP

Metro

MobileMeshNetwork

Web services

Gaming Grid

Appliance(PS3)

IP Flow Size = mean 47kB

Core

PoP

Core

IP Flow Size = 600MB,5GB

Future traffic modeling

Develop understanding of future traffic properties on core and metro networksTraffic GrowthTraffic MixTraffic Pattern (Metro/Core)Traffic Characteristics

Develop understanding of technical and economic impacts on core and metro network architecture.

Identify new technical issues on network planning and provisionin.

Self-similarity of traffic

W. Willinger, et. al., Self-Smilarity Through High-Variability Statistical Analysis of Ethernet LAN Traffic at the Source Level, Apr. 1997

Burstiness of traffic Characterize property of future Internet

traffic in terms of number of users, access bandwidth, content size and application

Number of Users

AccessBandwidth

Self Similar,Bursty LAN Traffic

Bellcore

Poisson-likeSmooth WAN Traffic

Bell Labs

FutureMAN Traffic

Bursty??

FutureWAN Traffic

Bursty??

Modeling Web traffic: Web user distribution

Boston

New YorkNew YorkPhiladelphia

Atlanta

MiamiHouston

Minneapolis

KansasCity

Denver

PhoenixSan Diego

LosLosAngelesAngeles

Seattle

SanFrancisco

RaleighGreensboro

Tampa

Albany

San Antonio

Knoxville

Salt LakeCity

Chicago

St. Louis

Allentown

Hartford

Bakersfield Dover

Washington D.C.

Pittsburgh

Des Moines

Austin

Dallas

Cleveland

DetroitSacramento

West Palm BeachOrlando

ManchesterGrand Rapids

Milwaukee

40 Largest US Metropolitan Areas

Modeling Web traffic: Web server popularity

Boston

New YorkPhiladelphia

Atlanta

MiamiHouston

Minneapolis

KansasCity

Denver

PhoenixSan Diego

LosAngeles

SanSanFranciscoFrancisco

RaleighGreensboro

TampaSan Antonio

Knoxville

Salt LakeCity

Chicago

Allentown

Hartford

Bakersfield Dover

Pittsburgh

Des Moines

Austin

Dallas

Cleveland

Detroit

West Palm BeachOrlando

ManchesterGrand Rapids

Milwaukee

Sacramento

SeattleAlbany

Washington D.C.Washington D.C.St. Louis

Based on IRCache logs, Jun. 2002

Modeling P2P traffic: Control traffic

Control traffic volume: 3PB/month

Gnutella network Aug. 2002

Modeling P2P traffic: P2P user distribution

Boston

New York

Philadelphia

Washington D.C.

Buffalo

Atlanta

Miami

Dallas

Houston

Chicago

MinneapolisMilwaukee

St. Louis

KansasCity

Denver

PhoenixSan Diego

LosAngeles

SanFrancisco

Seattle

Gnutella network Aug. 2002

Usage daily pattern

Daily pattern

00.20.40.60.8

11.21.4

Time (PST)

Varia

tion

AverageAverage3,000,0003,000,000

Gnutella network Aug. 2002

Web Usage Daily Pattern

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

Time of Day

Pe

rce

ntag

e

Web P2P

Content size distribution

SoftwareSoftwareAverageAverage34.5MB34.5MB

VideoVideoAverageAverage52.5MB52.5MB

10KB 100KB 1MB 10MB 100MB 1GB

10KB 100KB 1MB 10MB 100MB 1GB

10KB 100KB 1MB 10MB 100MB 1GB

AudioAudioAverageAverage4.5MB4.5MB

Gnutella network Aug. 2002

Content Size Distribution

1E-14

1E-12

1E-10

1E-08

1E-06

0.0001

0.01

0 0 1/10 1 10 100 1000 10000 100000

File Size (KByte)

Lognormal Pareto

(Average ~ 47 KBytes)

Web P2P

Traffic simulation and visualization tool

Traffic Matrix: 3D viewTraffic Volume: 2D time seriesMean/Peak Ratio: 2D time series

Total Population for Ring: 5,600,000

Node Population

Router 1 (R1) 800,000

Router 2 (R2) 700,000

Router 3 (R3) 500,000

Router 4 (R4) 1,200,000

Router 5 (R5) 600,000

Router 6 (R6) 300,000

Router 7 (R7) 1,200,000

Router 8 (R8) 300,000

POP (POP) -

Total Population for each Node:

800,000

500,0001,200,000

700,000600,000

300,000

1,200,000

300,000

R1R1

R2R2

R3R3R4R4

R5R5

R6R6

R7R7

R8R8

POPPOP

Test network configuration

Web traffic: Current scenario

9-node metro ring, 2.8 million online users, 1.5Mbps access

10msec 100msec 1sec 10sec 100sec

Web traffic: Future scenario

9-node metro ring, 2.8 million online users, 100 Mbps access

10msec 100msec 1sec 10sec 100sec

Access BW (max.): 3Mbps/384kbps, File Size Distribution: 10KB-1GBP2P Population : 5% of total population(5,600,000)

Traffic Volume (kbps)

Mean / Peak

Window size:

10msec 100msec 1sec 10sec 1min 10min

P2P traffic: Current scenario

Traffic Volume (kbps)

Mean /Peak

Window size:

10msec 100msec 1sec 10sec 1min 10min

Access BW (max.): 100Mbps/100Mbps, File Size Distribution: 10KB-5GBP2P Population : 15% of total population(5,600,000)

P2P traffic: Future scenario

Resource provisioning window

Resource Provision WindowIn a provision window, link capacity is

provisioned at the peak of the trafficSystem efficiency = system utilization =

mean to peak ratio

time1.5Mbps

3Mbps

10Mbps

50Mbps

100Mbps

AccessBandwidth

Population

ProvisionWindow

(90% efficiency)

1 hour

1 minute

1 second

NetworkManagement System

GMPLS Burst

Conclusions

Internet traffic projectionP2P accounts for 50% of total Internet trafficP2P traffic in particular very large video

objects are dominating the Internet traffic growth

Application traffic simulations allow accurate estimation and prediction of inter-metro traffic

Traffic being self-similar ≠ traffic being burstyActual factors that affect traffic burstiness:

Number of users, access bandwidth, content size and application

Potential for network planning and proactive bandwidth provisioningDynamic resource provisioning to improve

system efficiency for bursty traffic