Ad-hoc wireless communication among highly dynamic, mobile nodes in a urban network is a critical capability for a wide range of important applications including automated vehicles, real-time traffic monitoring and vehicular safety applications. When evaluating application performance in simulation, a realistic mobility model for vehicular ad-hoc networks (VANETs) is critical for accurate results. This paper analyzes ad-hoc wireless network performance in a vehicular network in which nodes move according to a simplified vehicular traffic model on roads defined by real map data. We show that when nodes move according to our street mobility model, STRAW, network performance is significantly different from that of the commonly used random waypoint model. We also demonstrate that protocol performance varies with the type of urban environment. Finally, we use these results to argue for the development of integrated vehicular and network traffic simulators to evaluate vehicular ad-hoc network applications, particularly when the information passed through the network affects node mobility.

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To enhance web browsing experiences, content distribution networks (CDNs) move web content "closer" to clients by caching copies of web objects on thousands of servers worldwide. Additionally, to minimize client download times, such systems perform extensive network and server measurements, and use them to redirect clients to different servers over short time scales. In this paper, we explore techniques for inferring and exploiting network measurements performed by the largest CDN, Akamai; our objective is to locate and utilize quality Internet paths without performing extensive path probing or monitoring.Our contributions are threefold. First, we conduct a broad measurement study of Akamai's CDN. We probe Akamai's network from 140 PlanetLab vantage points for two months. We find that Akamai redirection times, while slightly higher than advertised, are sufficiently low to be useful for network control. Second, we empirically show that Akamai redirections overwhelmingly correlate with network latencies on the paths between clients and the Akamai servers. Finally, we illustrate how large-scale overlay networks can exploit Akamai redirections to identify the best detouring nodes for one-hop source routing. Our research shows that in more than 50% of investigated scenarios, it is better to route through the nodes "recommended" by Akamai, than to use the direct paths. Because this is not the case for the rest of the scenarios, we develop lowoverhead pruning algorithms that avoid Akamai-driven paths when they are not beneficial. * Drafting is a technique commonly used by bikers and longdistance runners to reduce wind resistance by moving into the air pocket created behind the leader.

The user experience for networked applications is becoming a key benchmark for customers and network providers. Perceived user experience is largely determined by the frequency, duration and severity of network events that impact a service. While today's networks implement sophisticated infrastructure that issues alarms for most failures, there remains a class of silent outages (e.g., caused by configuration errors) that are not detected. Further, existing alarms provide little information to help operators understand the impact of network events on services. Attempts to address this through infrastructure that monitors end-to-end performance for customers have been hampered by the cost of deployment and by the volume of data generated by these solutions.We present an alternative approach that pushes monitoring to applications on end systems and uses their collective view to detect network events and their impact on services -an approach we call Crowdsourcing Event Monitoring (CEM). This paper presents a general framework for CEM systems and demonstrates its effectiveness for a P2P application using a large dataset gathered from BitTorrent users and confirmed network events from two ISPs. We discuss how we designed and deployed a prototype CEM implementation as an extension to BitTorrent. This system performs online service-level network event detection through passive monitoring and correlation of performance in end-users' applications.