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Traffic Classification for Application Specific Peering

Traffic Classification for Application Specific Peering. Jia Wang Joint work with Balachander Krishnamurthy AT&T Labs Research November 7, 2002. Motivation. Application specific peering P2P-like protocols

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Traffic Classification for Application Specific Peering

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  1. Traffic Classification for Application Specific Peering Jia Wang Joint work with Balachander Krishnamurthy AT&T Labs Research November 7, 2002 Jia Wang

  2. Motivation • Application specific peering • P2P-like protocols • Allow searches for resources to be directed to a copy of a resource on an ISP’s network • Traffic classification: graph transformation • AS relationship • Flow size Jia Wang

  3. Background • Inter-AS relationship • Provider-customer: customer pays provider • Peer-peer: mutually benefit by exchanging traffic between respective customers • BGP export rules • An AS can export its routes and routes to its customers to its provides/peers, but can not export routes learnt from other providers/peers. • An AS can exports its routes, routes of its customers and routes learnt from other providers/peers to its customers. Jia Wang

  4. AS paths AS categories ISP ISP-CUST ISP-PEER ISP-CUST-CUST* ISP-PEER-CUST* ISP-MH-CUST* UNKNOWN Methodology provider provider customer customer provider provider customer customer peer provider provider customer peer customer provider provider customer customer Jia Wang

  5. Traffic classification P2P traces Netflow AS relationship BGP configuration ISP ISP-PEER ISP-CUST P2P traffic flows ISP-CUST-CUST* ISP-PEER-CUST* ISP-MH-CUST* Graph representation Threshold Connected Components Signaling traffic Data traffic Traffic classification Jia Wang

  6. Traffic data • P2P applications: DirectConnent, FastTrack, Gnutella • Flow records from routers across a large IP network • Control traffic: flow size <= threshold • Data traffic: flow size > threshold • Threshold = 4KB • Experiments: 3 weeks, each 5-7 days, 800 million flows in total from AT&T IP backbone Jia Wang

  7. Classification results • Gnutella heaviest connected component • 99% IPs, 99% bytes • Signaling traffic: 90% IPs, 95% flows, 0.4% bytes • Data traffic: 50% IPs, 5% flows, 99.6% bytes • Signaling traffic is much less skewed than data traffic • Signaling: top 1% IPs, 25% bytes • Data: top 0.1% IPs, 30% bytes Jia Wang

  8. Traffic direction 5% of the traffic are intra-AT&T (ATT and ATT-CUST). Jia Wang

  9. Conclusion • Traffic classification • AS relationship and flow size • Graph transformation • Protocol-independent • Scalable • Automatic • Applications • Feasibility of retaining the proper portion of traffic on an ISP’s network to reduce the costs and latency • Overylay topology efficiency Jia Wang

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