Friday 23rd of February 2018

Multiple Tree Multicast in a Dynamic Environment

David A. Johnston, David R. Mcintyre, Francis G. Wolff and Christos A. Papachristou

Multiple multicast trees have been shown to increase the performance of data distribution when compared with single tree multicast. Node loss and congestion changes the performance characteristics of the multicast trees. Multicast tree performance feedback can be used to determine the optimal tree to use based on the feedback. We further examine an optimizing methodology, Probabilistic Multicast Trees (PMT), for multiple multicast trees which makes use of the performance feedback, generates a probability of usage for each multicast tree based on that feedback and then makes intelligent choices about which multicast tree to use for a given packet in the presence of node loss and congestion.

Keywords: Dynamic Multicast, Application-Level Multicast, ALM, Probabilistic Multicast Trees, PMT, Adaptive tree selection, Content distribution.

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David A. Johnston
David A. Johnston received a Ph.D. in Engineering and Computer Science from Case Western Reserve University.

David R. Mcintyre
David R. McIntyre received a Ph.D. in Computer Science from the University of Waterloo. He is Associate Professor of Computer Science at Cleveland State University.

Francis G. Wolff
Francis G. Wolff received a Ph.D. from Case Western Reserve University. He is a Senior member of the IEEE and the Senior member of the ACM.

Christos A. Papachristou
Christos A. Papachristou is Professor of Electrical Engineering and Computer Science at Case Western Reserve. He received the Ph.D. degree in Electrical Engineering and Computer Science from Johns Hopkins University. He is a Fellow of the IEEE and a member of the ACM and Sigma XI, and is listed in Who's Who in America.

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