On properties of multicast routing trees

Abstract

SUMMARYIn the last several years we witnessed the proliferation of multimedia applications on the Internet. One of the unavoidable techniques to support this type of communication is multicasting. However, even a decade after its initial proposal, multicast is still not widely deployed. One of the reasons is the lack of a solid business model. If the gain and the cost of multicast could be predicted, network operators might be encouraged to deploy multicast on a larger scale. In this paper we propose analytical expressions that could be used to estimate the gain of network-layer multicast. We show that the theoretical model matches extensive simulation and Internet measurement results remarkably well.Furthermore, we examine the reliability of traceroute data and of traceroutes-based conclusions. We investigate the node degree distributions in the Internet maps obtained from CAIDA and RIPE and we show the divergency of our results with those obtained by other researchers. We further focus on the analysis of multicast trees based on traceroute data. Only few results have been available on the node degree distribution of multicast routing trees which provided contradictory conclusions. Our results seem to indicate that the node degrees follow power laws only for a large number of multicast users.

Topology Dynamics in a P2PTV Network

“…Interestingly, Pr[D out = k] on a log-lin scale also fits well with a line curve. A similar observation was found by Janic et al [5]. We think the network size is not large enough to warrant a definite conclusion.…”

On the Correlation of Geographic and Network Proximity at Internet Edges and Its Implications for Mobile Unicast and Multicast Routing

“…A highly distributed scanning approach is taken within the DIMES project [12] with the aim of increased accuracy and comprehensiveness. Aside from many others, Janic and Van Mieghem [4] recently performed traceroute scans to investigate the node degree distribution in the Internet and report about complicacies similar to our observations.…”

Morphing distribution trees—On the evolution of multicast states under mobility and an adaptive routing scheme for mobile SSM sources

“…The authors derive an effective approximation of the scale factor k = k(N ) = var[LU (N )] <LU (N )> , which slowly increases to 1 with growing Internet size N . Investigating on self-similarity Janic and Van Mieghem [12] semi-empirically substantiated that multicast shortest path trees in the Internet can be modeled with reasonable accuracy by uniform recursive trees (URT) [36], provided m remains small compared to N .…”

“…Subsequent empirical and analytical work of Phillips et al [24], Van Mieghem et al [37], Chalmers and Almeroth [4], Adjih et al [1] and Janic and Van Mieghem [12] debated the applicability of the Chuang and Sirbu scaling law. Van Mieghem et al [37] proved that the proposed power law cannot hold for an increasing Internet or very large multicast groups, but is indeed applicable for moderate receiver numbers m and a current Internet size of about N = 10 5 routers.…”