Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/26244
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Alwadani, Dhaiffallah
Kolberg, Mario
Buford, John
Contact Email: mko@cs.stir.ac.uk
Title: Opportunistic native multicast under churn
Citation: Alwadani D, Kolberg M & Buford J (2016) Opportunistic native multicast under churn In: Proceedings of 2016 SAI Computing Conference, SAI 2016, Piscataway, NJ, USA: IEEE. 2016 SAI Computing Conference (SAI), 13.7.2016 - 15.7.2016, London, pp. 644-648.
Issue Date: Jul-2016
Conference Name: 2016 SAI Computing Conference (SAI)
Conference Dates: 2016-07-13T00:00:00Z
Conference Location: London
Abstract: In this paper we investigate the behaviour of Opportunistic Native Multicast under node churn. Previously, we have introduced an Opportunistic Native Multicast approach that facilitates native multicast where possible and which reverts to using overlay multicast when needed. Here, we are evaluating Overlay Native Multicast in a more realistic environment considering the effect of network churn and propose election algorithms to offer resilience and adaptivity required in the real world. We have developed a mechanism where the network can autonomously and deterministically elect a primary node for each multicast island. We have further investigated ways to improve the efficiency and responsiveness of the protocol by introducing a secondary node selection process. We have tested our proposed improvements using Oversim simulation. We show that Opportunistic Native Multicast can achieve success rates well above 90 percent despite realistic levels of node churn.
Status: Book Chapter: publisher version
Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.
URL: http://ieeexplore.ieee.org/abstract/document/7556050/

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