Vivaldi: a decentralized network coordinate system
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
Computer Networking: A Top-Down Approach (4th Edition)
Computer Networking: A Top-Down Approach (4th Edition)
Perspectives on tracing end-hosts: a survey summary
ACM SIGCOMM Computer Communication Review
BSense: a system for enabling automated broadband census: short paper
Proceedings of the 4th ACM Workshop on Networked Systems for Developing Regions
Netalyzr: illuminating the edge network
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Broadband internet performance: a view from the gateway
Proceedings of the ACM SIGCOMM 2011 conference
Crowdsourcing ISP characterization to the network edge
Proceedings of the first ACM SIGCOMM workshop on Measurements up the stack
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Broadband characterization has recently attracted much attention from the research community and the general public. Given this interest and the important business and policy implications of residential Internet service characterization, recent years have brought a variety of approaches to profiling Internet services, ranging from Web-based platforms to dedicated infrastructure inside home networks. We have previously argued that network-intensive applications provide an almost ideal vantage point for broadband service characterization at sufficient scale, nearly continuously and from end users. While we have shown that the approach is indeed effective at characterization and can enable performance comparisons between service providers and geographic regions, a key unanswered question is how well the performance characteristics captured by these network-intensive applications can predict the overall user experience with other applications. In this paper, using BitTorrent as an example network-intensive application, we present initial results that demonstrate how to obtain estimates of bandwidth and latency of a network connection by leveraging passive monitoring and limited active measurements from network intensive applications. We then analyze user experienced web performance under a variety of network conditions and show how estimated metrics from this network intensive application can serve as good web performance predictors.