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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

Traffic Classification for Application Specific Peering

Jia Wang

Joint work with Balachander Krishnamurthy

AT&T Labs Research

November 7, 2002

Jia Wang

motivation
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

background
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

methodology
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

traffic classification
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

traffic data
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

classification results
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

traffic direction
Traffic direction

5% of the traffic are intra-AT&T (ATT and ATT-CUST).

Jia Wang

conclusion
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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