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Internet Traffic Profiling

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Performance Evaluation for Network Services, Systems and Protocols

Abstract

This chapter takes you on a journey on Internet traffic, from understanding its profile (i.e., by modeling and analysis) to generating packets or flows (either real or synthetic), in diverse environments. Usual decision network engineers and researchers find when designing performance evaluation experimental plans that are concerned with traffic generation. Suppose that you have measured and collected sufficient Internet traffic in your core network to derive a statistical model of the aggregate traffic. Now you want to use such analytical model for traffic prediction or capacity planning purposes in another what-if (a.k.a. sensitivity) analysis scenarios [14], via Systems Operational Dependency Analysis (SODA), for example [13]. If your further analysis will be conducted in a simulation environment, you either need to use the available models or to bring your traffic model into the environment as accurate as possible. If your sensitivity analysis will be done in a test bed, you have to assess the adequacy of your hardware- or software-based traffic generator. Sections 4.1 and 4.2 provide an overview of traffic analysis by looking at recent advances in traffic identification and classification and then discussing techniques and tools to effectively profile network traffic in a scalable fashion. Section 4.3 provides some examples of models that can be used to generate traffic. It is worth emphasizing that both traffic analysis and traffic modeling are very broad fields of investigation. Section 4.3 also deals with workload generation. There is a particular interest in methods that effectively and efficiently mimic network traffic in a certain layer of the Internet protocol stack. Last, but not the least, Sect. 4.4 discusses the world of simulation and emulation of computer network protocols and services. There is a massive amount of material in these topics that makes it impossible to condense them into a single book chapter. However, there will be lots of references, so the interested reader can delve into.

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Notes

  1. 1.

    Merriam-Webster – Full Definition of identification – a: an act of identifying, the state of being identified; b: evidence of identity; http://www.merriam-webster.com/dictionary/identification .

  2. 2.

    Merriam-Webster – Full Definition of classification – a: the act or process of classifying; b: systematic arrangement in groups or categories according to established criteria; http://www.merriam-webster.com/dictionary/classification .

  3. 3.

    www.netflix.com .

  4. 4.

    www.hulu.com .

  5. 5.

    www.akamai.com .

  6. 6.

    At the time of writing this chapter.

  7. 7.

    Term coined by SolarWind: Mohan, V., “Deep Packet Inspection: Unearthing Gold in Network Packets,” March 2014, available at https://thwack.solarwinds.com/community/solarwinds-community/geek-speak_tht/blog/2014/03/06/deep-packet-inspection-unearthing-gold-in-network-packets.

  8. 8.

    NTOP’s PF_RING (http://www.ntop.org/products/packet-capture/pf_ring/).

  9. 9.

    The genius of DAG – May 2016, http://www.endace.com/endace-dag-high-speed-packet-capture-cards.html.

  10. 10.

    https://support.skype.com/en/faq/fa31/does-skype-use-encryption.

  11. 11.

    Policy Traffic Switch (PTS) – available at https://www.sandvine.com/platform/policy-traffic-switch.html, accessed May 2016.

  12. 12.

    Cisco Application Visibility and Control (AVC) – http://www.cisco.com/c/en/us/products/routers/avc_control.html .

  13. 13.

    e.g., Nallatech’s FPGA network processing cards, http://www.nallatech.com/solutions/fpga-network-processing/, May 2016.

  14. 14.

    Cavium’s NITROX DPI L7 Content Processor Family, http://www.cavium.com/processor_NITROX-DPI.html, May 2016.

  15. 15.

    Broadcom’s 20 Gbps NETL7™ Knowledge-Based Processor, product code: NLS2008, http://www.broadcom.com/products/enterprise-and-network-processors/knowledge-based-processors/nls2008 .

  16. 16.

    http://netfpga.org/site/#/about/.

  17. 17.

    PF_RING API Revision:7479, API documentation for PF_RING: high-speed packet capture, filtering, and analysis framework. http://www.ntop.org/pfring_api/files.html, available May 16, 2016.

  18. 18.

    http://www.ntop.org/products/deep-packet-inspection/ndpi/.

  19. 19.

    https://code.google.com/archive/p/opendpi/.

  20. 20.

    It was supposed to be a (horrible) pun, but indeed the term exists. Fort Collins-Loveland Municipal Airport (Northern Colorado) has used this term in its “Capacity Analysis and Facility Requirements” report, available at http://www.fortloveair.com/ .

  21. 21.

    Definitions of managed objects for IP Flow Information Export – RFC 5815 – https://datatracker.ietf.org/doc/rfc5815/.

  22. 22.

    Which is like the De Caprio’s Inception movie, since now I am classifying the classifiers.

  23. 23.

    www.sandvine.com .

  24. 24.

    www.proceranetworks.com .

  25. 25.

    www.allot.com .

  26. 26.

    www.solarwinds.com .

  27. 27.

    www.ipoque.com .

  28. 28.

    www.snort.org .

  29. 29.

    www.snort.org/integrators .

  30. 30.

    suricata-ids.org .

  31. 31.

    www.ntop.org .

  32. 32.

    I’m pretty sure there is a reality TV show related to this type of scene.

  33. 33.

    Spirent’s appliances: www.spirent.com/Products/TestCenter/Platforms/Appliances .

  34. 34.

    Spirent’s modules: www.spirent.com/Products/TestCenter/Platforms/Modules .

  35. 35.

    IxLoad: www.ixiacom.com/products/ixload .

  36. 36.

    Ixia’s PerfectStorm Family: www.ixiacom.com/products-services/ixia-test-hardware-products .

  37. 37.

    There are different iperf versions available. iperfv3 has been recently developed and is maintained by ESnet. It is available at https://github.com/esnet/iperf.

  38. 38.

    https://www.youtube.com/watch?v=eHkxmJ88Vsc .

  39. 39.

    www.linuxfoundation.org/collaborate/workgroups/networking/netem .

  40. 40.

    wanem.sourceforge.net/ .

  41. 41.

    IEEE Std 610.12 – 1990 IEEE Standard Glossary of Software Engineering Terminology.

  42. 42.

    1730–2010 – IEEE Recommended Practice for Distributed Simulation Engineering and Execution Process.

  43. 43.

    Information for Real-World Pilots – An aid for training and proficiency: available at https://www.microsoft.com/Products/Games/FSInsider/product/Pages/InfoRealworld.aspx .

  44. 44.

    “Does flight simulation prepare pilots for flying a real plane?”, 14 Oct 2013, available at https://flyawaysimulation.com/news/4492/ .

  45. 45.

    The Wachowskis’ The Matrix Movie – 1999.

  46. 46.

    Merriam-Webster definition of simulacrum: http://www.merriam-webster.com/dictionary/simulacrum.

  47. 47.

    “What Are the Odds We Are Living in a Computer Simulation?” by Joshua Rothman, The New Yorker, June 9, 2016, http://www.newyorker.com/books/joshua-rothman/what-are-the-odds-we-are-living-in-a-computer-simulation .

  48. 48.

    http://www.ieeecss.org/technical-activities/discrete-event-systems .

  49. 49.

    www.isi.edu/nsnam/ns .

  50. 50.

    www.omnetpp.org .

  51. 51.

    http://web.scalable-networks.com/content/qualnet .

  52. 52.

    http://www.riverbed.com/products/steelcentral/steelcentral-riverbed-modeler.html .

  53. 53.

    http://www.estinet.com/ .

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  1. Centro de Informática, Universidade Federal de Pernambuco, Recife, Pernambuco, Brazil

    Stênio Fernandes

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Fernandes, S. (2017). Internet Traffic Profiling. In: Performance Evaluation for Network Services, Systems and Protocols . Springer, Cham. https://doi.org/10.1007/978-3-319-54521-9_4

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  • DOI: https://doi.org/10.1007/978-3-319-54521-9_4

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