Measuring and modeling applications for content distribution in the internet
Laxmi Narayan Bhuyan, Anirban Banerjee · 2008
The focus of this dissertation is on measuring, analyzing and modeling emerging applications in the Internet. Specifically, we concentrate on understanding the internals of content distribution paradigms such as Peer-to-Peer (P2P) systems and podcasts. This dissertation consists of three main thrusts which we describe below. P2P streams have been reported to constitute nearly 61% of all upstream traffic. P2P streams are used for disseminating content ranging from video programs to linux images. This everpresent ubiquity of P2P networks has also allowed them to be used for sharing copyrighted material. This has resulted in organizations like the RIAA, taking legal action against file-sharers. As a result P2P users have employed defenses against being monitored by such organizations. We have found that a little caution pays off a lot, since there is a 100% probability of a naive P2P user being monitored when accessing these networks. Further, as a case study, we present a comprehensive study about eDonkey, a popular P2P network. We identify the limitations of current approaches to measure P2P networks. Additionally, we find that P2P flows traverse through the Internet quite differently than http flows. Based on this, we present metrics useful for distinguishing P2P traffic from other forms of traditional content distribution in the Internet. Finally, podcasts, a relatively new content distribution mechanism is expected to garner an audience of nearly 56 million subscribers by 2010. Measuring and modeling podcasts remains an open problem despite the significance that has been gained by this application. This form of content distribution is best described as a push based mechanism, which is different from traditional http based content distribution. We measure podcast streams, analyze them and develop a traffic generator, SimPod, for simulation purposes.