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Modeling Internet Application Traffic for Network Planning and Provisioning Takafumi Chujo Fujistu Laboratories of America, Inc.

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Presentation on theme: "Modeling Internet Application Traffic for Network Planning and Provisioning Takafumi Chujo Fujistu Laboratories of America, Inc."— Presentation transcript:

1 Modeling Internet Application Traffic for Network Planning and Provisioning Takafumi Chujo Fujistu Laboratories of America, Inc.

2 Traffic mix on converged IP networks ROBERT B. COHENGRID COMPUTING AND THE GROWTH OF THE INTERNET, GGF 41 ROBERT B. COHEN, GRID COMPUTING AND THE GROWTH OF THE INTERNET, GGF 41

3 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 Supports randomly fluctuating, bursty traffic with randomly distributed peers. Metro PoP Metro Mobile Mesh Network Web services Gaming Grid Appliance (PS3) IP Flow Size = mean 47kB Core PoP Core IP Flow Size = 600MB,5GB

4 Future traffic modeling Develop understanding of future traffic properties on core and metro networks Traffic Growth Traffic Mix Traffic 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.

5 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

6 Burstiness of traffic Characterize property of future Internet traffic in terms of number of users, access bandwidth, content size and application Number of Users Access Bandwidth Self Similar, Bursty LAN Traffic Bellcore Poisson-like Smooth WAN Traffic Bell Labs Future MAN Traffic Bursty?? Future WAN Traffic Bursty??

7 Modeling Web traffic: Web user distribution Boston New York Philadelphia Atlanta Miami Houston Minneapolis Kansas City Denver Phoenix San Diego LosAngeles Seattle San Francisco Raleigh Greensboro Tampa Albany San Antonio Knoxville Salt Lake City Chicago St. Louis Allentown Hartford Bakersfield Dover Washington D.C. Pittsburgh Des Moines Austin Dallas Cleveland Detroit Sacramento West Palm Beach Orlando Manchester Grand Rapids Milwaukee 40 Largest US Metropolitan Areas

8 Modeling Web traffic: Web server popularity Boston New York Philadelphia Atlanta Miami Houston Minneapolis Kansas City Denver Phoenix San Diego Los Angeles SanFrancisco Raleigh Greensboro Tampa San Antonio Knoxville Salt Lake City Chicago Allentown Hartford Bakersfield Dover Pittsburgh Des Moines Austin Dallas Cleveland Detroit West Palm Beach Orlando Manchester Grand Rapids Milwaukee Sacramento Seattle Albany Washington D.C. St. Louis Based on IRCache logs, Jun. 2002

9 Modeling P2P traffic: Control traffic Control traffic volume: 3PB/month Gnutella network Aug. 2002

10 Modeling P2P traffic: P2P user distribution Boston New York Philadelphia Washington D.C. Buffalo Atlanta Miami Dallas Houston Chicago Minneapolis Milwaukee St. Louis Kansas City Denver Phoenix San Diego Los Angeles San Francisco Seattle Gnutella network Aug. 2002

11 Usage daily pattern Average3,000,000 Gnutella network Aug WebP2P

12 Content size distribution SoftwareAverage34.5MB VideoAverage52.5MB 10KB 100KB 1MB 10MB 100MB 1GB AudioAverage4.5MB Gnutella network Aug WebP2P

13 Traffic simulation and visualization tool Traffic Matrix: 3D view Traffic Volume: 2D time series Mean/Peak Ratio: 2D time series

14 Total Population for Ring: 5,600,000 NodePopulation 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 1,200, , , ,000 1,200, ,000 R1 R2 R3 R4 R5 R6 R7 R8 POP Test network configuration

15 Web traffic: Current scenario 9-node metro ring, 2.8 million online users, 1.5Mbps access 10msec 100msec 1sec 10sec 100sec

16 Web traffic: Future scenario 9-node metro ring, 2.8 million online users, 100 Mbps access 10msec 100msec 1sec 10sec 100sec

17 Access BW (max.): 3Mbps/384kbps, File Size Distribution: 10KB-1GB P2P 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

18 Traffic Volume (kbps) Mean /Peak Window size: 10msec 100msec 1sec 10sec 1min 10min Access BW (max.): 100Mbps/100Mbps, File Size Distribution: 10KB-5GB P2P Population : 15% of total population(5,600,000) P2P traffic: Future scenario

19 Resource provisioning window Resource Provision Window In a provision window, link capacity is provisioned at the peak of the traffic System efficiency = system utilization = mean to peak ratio time 1.5Mbps 3Mbps 10Mbps 50Mbps 100Mbps Access Bandwidth Population Provision Window (90% efficiency) 1 hour 1 minute 1 second Network Management System GMPLSBurst

20 Conclusions Internet traffic projection P2P accounts for 50% of total Internet traffic P2P 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 bursty Actual factors that affect traffic burstiness: Number of users, access bandwidth, content size and application Potential for network planning and proactive bandwidth provisioning Dynamic resource provisioning to improve system efficiency for bursty traffic


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