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Workshop on Models, Methods and Tools for Reproducible Network Research - On Characterizing Affinity and Its Impact on Network Performance
Lucas, Gabriel; Chuang, John; Ghose, Abhishek (2003)
clipboard
mla
clipboard		 Lucas G., et al. "Workshop on Models, Methods and Tools for Reproducible Network Research - On Characterizing Affinity and Its Impact on Network Performance.", timms video, Universität Tübingen (2003): https://timms.uni-tuebingen.de:443/tp/UT_20030825_009_mometools_0001. Accessed 03 Apr 2025.
apa
clipboard		 Lucas, G., Chuang, J. & Ghose, A. (2003). Workshop on Models, Methods and Tools for Reproducible Network Research - On Characterizing Affinity and Its Impact on Network Performance. timms video: Universität Tübingen. Retrieved April 03, 2025 from the World Wide Web https://timms.uni-tuebingen.de:443/tp/UT_20030825_009_mometools_0001
harvard
clipboard		 Lucas, G., Chuang, J. and Ghose, A. (2003). Workshop on Models, Methods and Tools for Reproducible Network Research - On Characterizing Affinity and Its Impact on Network Performance [Online video]. 25 August. Available at: https://timms.uni-tuebingen.de:443/tp/UT_20030825_009_mometools_0001 (Accessed: 3 April 2025).
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title: Workshop on Models, Methods and Tools for Reproducible Network Research - On Characterizing Affinity and Its Impact on Network Performance
alt. title: Session 4: Modelling the Internet
creators: Lucas, Gabriel (author), Chuang, John (author), Ghose, Abhishek (author)
subjects: Workshop, Modelling the Internet, Network Performance, Affinity
description: Workshop im SoSe; Mittwoch, 25. August 2003
abstract: An important component of simulation-based network research is the selection of nodes to a member group, such as receivers in a multicast group or web clients in a content delivery network. In a seminal paper, Philips et al. introduce an algorithm for generating member groups with di.erent degrees of a.nity (clusteredness) and show that a.nity can have a signi.cant e.ect on multicast e.ciency. Subsequent studies applying this algorithm have all used the algorithm's input parameter as a method for classifying and comparing a.nity groups. In this paper, we propose several distance- and expansion-based analysis metrics and .nd them to be better measurements of the true a.nity of member groups. In three separate case studies (multicast, replica placement, and sensor networks), we demonstrate the bene.t of classifying member groups by their true a.nity in order to predict network performance variation. By systematizing techniques for measuring a.nity, we open the door for more realistic and reproducible research in studies employing a.nity-based member selection techniques.
publisher: ZDV Universität Tübingen
contributors: Carle, Georg (producer), Carle, Georg (organizer), Wehrle, Klaus (chairman)
creation date: 2003-08-25
dc type: image
localtype: video
identifier: UT_20030825_009_mometools_0001
language: eng
rights: Url: https://timmsstatic.uni-tuebingen.de/jtimms/TimmsDisclaimer.html?638792817424142244