• DocumentCode
    505999
  • Title

    Evaluating network information models on resource efficiency and application performance in lambda-grids

  • Author

    Taesombut, Nut ; Chien, Andrew A.

  • Author_Institution
    University of California, La Jolla, CA
  • fYear
    2007
  • fDate
    10-16 Nov. 2007
  • Firstpage
    1
  • Lastpage
    12
  • Abstract
    A critical challenge for wide-area configurable networks is definition and widespread acceptance of Network Information Model (NIM). When a network comprises multiple domains, intelligent information sharing is required for a provider to maintain a competitive advantage and for customers to use a provider´s network and make good resource selection decisions. We characterize the information that can be shared between domains and propose a spectrum of network information models. To evaluate the impact of the proposed models, we use a trace-driven simulation under a range of real providers´ networks and assess how the available information affects applications´ and providers´ ability to utilize network resources. We find that domain topology information is crucial for achieving good resource efficiency, low application latency and network configuration cost, while domain link state information contributes to better resource utilization and system throughput. These results suggest that collaboration between service providers can provide better overall network productivity.
  • Keywords
    Application software; Collaboration; Computer networks; Computer science; Delay; Optical fiber networks; Permission; Productivity; Throughput; Wavelength division multiplexing; configurable optical network; information model; lambda-grids;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Supercomputing, 2007. SC '07. Proceedings of the 2007 ACM/IEEE Conference on
  • Conference_Location
    Reno, NV, USA
  • Print_ISBN
    978-1-59593-764-3
  • Electronic_ISBN
    978-1-59593-764-3
  • Type

    conf

  • DOI
    10.1145/1362622.1362632
  • Filename
    5348830