• DocumentCode
    659416
  • Title

    Optimizing throughput on guaranteed-bandwidth WAN networks for the Large Synoptic Survey Telescope (LSST)

  • Author

    Freemon, D. Michael

  • Author_Institution
    Nat. Center for Supercomput. Applic., Univ. of Illinois, Urbana, IL, USA
  • fYear
    2013
  • fDate
    6-9 Oct. 2013
  • Firstpage
    137
  • Lastpage
    142
  • Abstract
    The Large Synoptic Survey Telescope (LSST) is a proposed 8.4-meter telescope that will be located in the Andes mountains in Chile. Every 17 seconds, a 6.4 GB image is transferred to Illinois for immediate processing. That transfer needs to complete within approximately five seconds. LSST is provisioning an international WAN with a 10Gbps bandwidth guarantee for this and other project-related data transfers. The stringent latency requirement drives a re-examination of TCP congestion control for this use case. Specifically, prior work on dedicated Long Fat Networks (LFNs) does not go far enough in fully leveraging the opportunity provided by the bandwidth guarantee. This paper presents an approach for how optimal network throughput can be obtained for the LSST use case, and the conditions under which any project can achieve data throughput rates on long-distance networks approaching wire speed.
  • Keywords
    astronomical telescopes; telecommunication congestion control; telescopes; transport protocols; wide area networks; Andes mountains; Chile; Illinois; LFN; LSST; TCP congestion control; guaranteed-bandwidth WAN networks; international WAN; large synoptic survey telescope; long fat networks; long-distance networks; optimal network throughput; project-related data transfers; stringent latency requirement; throughput optimization; Bandwidth; Crosstalk; Packet loss; Payloads; Servers; Throughput; high speed networks; tcp congestion control; tcpip; transport protocols; wide area networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Big Data, 2013 IEEE International Conference on
  • Conference_Location
    Silicon Valley, CA
  • Type

    conf

  • DOI
    10.1109/BigData.2013.6691565
  • Filename
    6691565