Profiling podcast-based content distribution

Media distribution via podcasts is a relatively new phenomenon which follows a different paradigm compared to traditional modes of content delivery. Despite its rapidly increasing subscriber base, podcast distribution has not been measured or modeled adequately, if at all. As our contribution, we develop a measurement based profile of podcasters. This profile consists of a comprehensive and detailed analysis of interesting characteristics of podcast streams which could be used for analytical studies and simulations. We start by conducting extensive active measurements to characterize 875 popular podcast streams for over a month. The take away message from our study is that podcast traffic is significantly different from the other types of traffic such as Web traffic. For example, we find that podcast file sizes (between 2 and 35 MB) are not only significantly larger than Web files on the average, but they follow a different distribution (a bimodal Gaussian compared to a heavy tail Pareto distribution for Web files). Other interesting aspects of the podcast profile is the expected daily content download (per podcaster), in the range of 2 to 6 MB, and their content distribution patterns. We also find and quantify the heterogeneity in the intensity of content creation, since approximately 14% of podcasters contribute over 54% of files, amounting to about 30% of total byte-content.