Gismo: A Generator of Internet Streaming Media Objects and Workloads

This paper presents a tool called Gismo (Generator of Internet Streaming Media Objects and workloads). Gismo enables the specification of a number of streaming media access characteristics, including object popularity, temporal correlation of request, seasonal access patterns, user session durations, user interactivity times, and variable bit-rate (VBR) self-similarity and marginal distributions. The embodiment of these characteristics in Gismo enables the generation of realistic and scalable request streams for use in the benchmarking and comparative evaluation of Internet streaming media delivery techniques. To demonstrate the usefulness of Gismo, we present a case study that shows the importance of various workload characteristics in determining the effectiveness of proxy caching and server patching techniques in reducing bandwidth requirements.

On the Geographic Location of Internet Resources

One relatively unexplored question about the Internet's physical structure concerns the geographical location of its components: routers, links and autonomous systems (ASes). We study this question using two large inventories of Internet routers and links, collected by different methods and about two years apart. We first map each router to its geographical location using two different state-of-the-art tools. We then study the relationship between router location and population density; between geographic distance and link density; and between the size and geographic extent of ASes. Our findings are consistent across the two datasets and both mapping methods. First, as expected, router density per person varies widely over different economic regions; however, in economically homogeneous regions, router density shows a strong superlinear relationship to population density. Second, the probability that two routers are directly connected is strongly dependent on distance; our data is consistent with a model in which a majority (up to 75-95%) of link formation is based on geographical distance (as in the Waxman topology generation method). Finally, we find that ASes show high variability in geographic size, which is correlated with other measures of AS size (degree and number of interfaces). Among small to medium ASes, ASes show wide variability in their geographic dispersal; however, all ASes exceeding a certain threshold in size are maximally dispersed geographically. These findings have many implications for the next generation of topology generators, which we envisage as producing router-level graphs annotated with attributes such as link latencies, AS identifiers and geographical locations.