With Online Social Networking (OSN) services coming to prominence as a major platform for the popularization of Web content, recent research has focused on using information gleaned from these services to improve the performance of Content Distribution Networks (CDN). In this paper we find that contrary to popular belief, the simple cache replacement policy LRU outperforms methods based on OSN input. To establish this we propose an OSN-assisted caching strategy which we evaluate using a large Twitter dataset and through simulation. Our results show that vanilla LRU mostly outperforms the OSN-aided mechanisms. We study the reasons for this and find that if at any time there is a large enough percentage of highly popular content, which becomes popular within a short enough time span, CDN can do well by just following an LRU principle. While we do not claim generalizability to other workloads, or other caching architectures on the Internet, our results are important as they apply to the common use-case and show that simple algorithms do quite well.

We present the first comprehensive characterization of the diffusion of ideas on Twitter, studying more than 5.96 million topics that include both popular and less popular topics. On a data set containing approximately 10 million users and a comprehensive scraping of 196 million tweets, we perform a rigorous temporal and spatial analysis, investigating the time-evolving properties of the subgraphs formed by the users discussing each topic. We focus on two different notions of the spatial: the network topology formed by follower-following links on Twitter, and the geospatial location of the users. We investigate the effect of initiators on the popularity of topics and find that users with a high number of followers have a strong impact on topic popularity. We deduce that topics become popular when disjoint clusters of users discussing them begin to merge and form one giant component that grows to cover a significant fraction of the network. Our geospatial analysis shows that highly popular topics are those that cross regional boundaries aggressively.

Web-based catch-up TV has revolutionised watching habits as it provides users the opportunity to watch programs at their preferred time and place, using a variety of devices. With the increasing offer of TV content, there is an emergent need for personalised recommendation solutions, which help users to select programs of interest. In this work, we study the watching patterns of users of an Australian nation-wide catch-up TV service provider and develop a suite of approaches for a catch-up recommendation scenario. We evaluate these approaches using a new large-scale dataset gathered by the Web-based catch-up portal deployed by the provider. The evaluation allows us to compare the performance of several recommenders that address the discovery of both TV programs already watched by users and new programs that users may find relevant.

Content delivery networks (CDNs) have become a crucial part of the modern Web infrastructure. This paper studies the performance of the leading content delivery provider - Akamai. It measures the performance of the current Akamai platform and considers a key architectural question faced by both CDN designers and their prospective customers: whether the co-location approach to CDN platforms adopted by Akamai, which tries to deploy servers in numerous Internet locations, brings inherent performance benefits over a more consolidated data center approach pursued by other influential CDNs such as Limelight. We believe the methodology we developed for this study will be useful for other researchers in the CDN arena.

This paper describes our implementation of and initial experiences with DipZoom (for "Deep Internet Performance Zoom"), a novel approach to provide focused, on-demand Internet measurements. Unlike existing approaches that face a difficult challenge of building a measurement platform with sufficiently diverse measurements and measuring hosts, DipZoom implements a matchmaking service instead, which uses P2P concepts to bring together experimenters in need of measurements with external measurement providers. DipZoom offers the following two main contributions. First,since it is just a facilitator for an open community of participants, it promises unprecedented availability of diverse measurements and measuring points. Second, it can be used as a veneer over existing measurement platforms, automating the planning and execution of complex measurements.

We describe DipZoom (for "deep Internet performance zoom"), an approach to provide focused, on-demand Internet measurements. Unlike existing approaches that face a difficult challenge of building a measurement platform with sufficiently diverse measurements and measuring hosts, DipZoom offers a matchmaking service instead, which uses P2P concepts to bring together experimenters in need of measurements with external measurement providers. It then harnesses market forces to orchestrate the supply and demand sides in the resulting open eco-system. This paper outlines the overall design of DipZoom, and discusses payment, trust and security issues in the resulting open system.