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An Analysis of Internet Content Delivery Systems. Stefan Saroiu, Krishna P. Gommadi, Richard J. Dunn, Steven D. Gribble, and Henry M. Levy Proceedings of the 5th Symposium on Operating Systems Design and Implementation, 2002 Presented by Dionysios Kostoulas. Outline. Goals

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An analysis of internet content delivery systems

An Analysis of Internet Content Delivery Systems

Stefan Saroiu, Krishna P. Gommadi, Richard J. Dunn, Steven D. Gribble, and Henry M. Levy

Proceedings of the 5th Symposium on Operating Systems Design and Implementation, 2002

Presented by Dionysios Kostoulas


Outline
Outline

  • Goals

  • Overview of Content Delivery Systems

  • Methodology of analysis

  • Results of analysis

  • Potential role of caching

  • Conclusions


Goals
Goals

  • Quantify the rapidly increasing importance of new content delivery systems, particularly peer-to-peer networks

  • Characterize the behavior of these systems from the perspectives of clients, objects, and servers

  • Derive implications for caching in these systems


Overview of content delivery systems
Overview of Content Delivery Systems

  • HTTP Web Traffic

  • Content Delivery Networks

    • Akamai

  • Peer-to-Peer systems

    • Gnutella

    • Kazaa


  • Http web traffic
    HTTP Web Traffic

    • Web clients run on users’ machines & request objects from web servers

    • Web objects/servers are accessed with a Zipf popularity distribution

    • Heavy-tail distribution of web object sizes (most objects are small, 5-10 KB)

    • Caching helps but hit rate increases logarithmically with client population & constrained by increasing amount of dynamic objects


    Content delivery networks cdns
    Content Delivery Networks (CDNs)

    • Dedicated collections of servers distributed strategically across the wide-area Internet

    • Static content, e.g. images, streaming video

    • Content replicated across the wide area & accessed by closest to the client server

    • Replica location done via DNS interposition or URL rewriting at origin servers

    • Reduced average download response time

    • Redirection adds overhead


    Peer to peer systems
    Peer-to-Peer Systems

    • Peers collaborate to form a distributed system for exchanging content

    • Peers act as both servers and clients

    • Most peers have low-availability and limited network capacity

    • Non-interactive, batch-style downloads

    • Files transferred via direct connection between peers


    Methodology
    Methodology

    • Passive network monitoring to collect trace of TCP traffic flowing between the University of Washington (UW) and the rest of Internet

    • 9 day trace, over 500 million transactions and over 20 TB of HTTP data

    • Both ingoing and outgoing traffic traced

    • Classification of traffic in WWW, Akamai, P2P (Gnutella, Kazaa) and non-HTTP


    Results
    Results

    • UW is an HTTP content provider

      • Exported 16.65 TB - Imported 3.44 TB

  • Bandwidth consumption (in + out)

    • 0.2% Akamai

    • 6.04% Gnutella

    • 14.3% WWW

    • 36.9% Kazaa

    • 43% other TCP protocols, e.g. mail, streaming video


  • More results
    More Results

    • Bandwidth consumption over time

      • WWW peaking in middle of day

      • Kazaa peaking late at night

  • Data characteristics compared to 1999:

    • HTML traffic has decreased 43%

    • GIF/JPG traffic has decreased 59%

    • AVI/MPG traffic increased nearly 400%

    • MP3 traffic increased nearly 300%


  • Objects
    Objects

    • Median Web object size: 2KB

    • Median P2P object size: 4MB.

    • 5% of Kazaa objects are over 100MB

    • Top 1% of Web objects account for 16% of bytes transferred

    • Top 1% of Kazaa objects account for 50% of bytes transferred

    • Top-10 offenders for WWW are mix of small popular objects and large less popular ones

    • For Kazaa top-10 offenders are large unpopular objects


    Clients
    Clients

    • For both Web and Kazaa, small number of clients account for large portion of traffic

      • In Web, top-200 clients (0.5% of the population) account for 13% of the traffic

      • In Kazaa, top-200 clients (4% of the population) account for 50% of the traffic

      • Top-200 Kazaa clients account for 20% of total HTTP traffic

    • Kazaa request rate is two orders of magnitude lower than WWW but Kazaa median object size is three orders of magnitude higher than WWW


    Servers
    Servers

    • Top-500 external Web servers provide 22% of the bytes

    • Top-500 external Kazaa servers provide 10% of the bytes

    • Top-400 internal Kazaa peers create 70% of all outgoing HTTP traffic

    • Server load for Kazaa is not distributed enough as one would expect (this is logical for WWW)


    P2p scalability
    P2P Scalability

    • Average web client consumption in 9 days: 41.9 MB

    • Average Kazaa peer consumption: 3.6 GB

    • This implies that adding another 450 Kazaa clients would be equivalent to doubling the web client population (from 40,000 to 80,000)


    Cdn caching potential
    CDN Caching Potential

    • Use local proxy cache instead of Akamai:

      • 88% ideal object hit rate (all objects cacheable)

      • 50% practical hit rate

    • Conclusion: Widely deployed proxy caches reduce need for separate CDNs


    P2p caching potential
    P2P Caching Potential

    • Inbound cache byte hit rate = 35%

    • Outbound cache byte hit rate = 85%

    • Hit rate increases with client population

      • 1,000 clients = 40% hit rate

      • 500,000 clients = 85% hit rate

  • Conclusion: Reverse P2P cache saves the most bandwidth


  • Conclusions
    Conclusions

    • P2P traffic accounts for majority of HTTP bytes transferred

    • P2P objects are significantly larger than Web objects

    • Small number of large objects account for a large percentage of P2P traffic

    • Small number of clients and servers responsible for majority of P2P traffic

    • P2P clients have great bandwidth requirements

    • P2P proxy cache has the potential to reduce P2P requirements


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