IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v456y2016icp327-339.html
   My bibliography  Save this article

Unraveling the impacts of IXP in internet ecosystem using bi-layered network

Author

Listed:
  • Fan, Zhongyan
  • Tang, Wallace K.S.

Abstract

The Internet is a huge complex network, consisting of tens thousands of Autonomous Systems (ASes). Being a logical fabric of the Internet, the AS-level topology serves as a manageable and useful vehicle for the study of Internet characteristics. However, Internet exchange points (IXPs) have been ignored in previous studies despite of being one of the primary mechanisms for AS interconnections and playing an important role for improving data traffic. In this paper, a novel bi-layered network model is proposed to present an IXP–AS topology. The bi-layered network is built based on the actual architecture, from which a comprehensive study of current AS ecosystem and the impacts of IXP can be made. As revealed by network metrics applied onto the bi-layered network, IXP is always of higher centrality as compared with ASes, well matching its role. By comparing the results in 2009 and 2014, rapid growth in IXP number, membership and impacts are noticed. There are over 98% source–destination pairs routes (with shortest path routing protocol) affected by IXP in 2014. Our results also show that, being an IXP member is more favorable than being a non-member, hence it attracts many ASes, especially those with low centrality, in recent years.

Suggested Citation

  • Fan, Zhongyan & Tang, Wallace K.S., 2016. "Unraveling the impacts of IXP in internet ecosystem using bi-layered network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 327-339.
    • Handle: RePEc:eee:phsmap:v:456:y:2016:i:c:p:327-339
    DOI: 10.1016/j.physa.2016.03.050
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437116300486
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2016.03.050?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Jin-Fa & He, Xuan & Si, Shuai-Zong & Zhao, Hai & Zheng, Chunyang & Yu, Hao, 2019. "Using complex network theory for temporal locality in network traffic flows," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 722-736.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:456:y:2016:i:c:p:327-339. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.