modeling internet application traffic for network planning and provisioning takafumi chujo fujistu...
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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