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.

Global-scale Content Distribution Networks (CDNs), such as Akamai, distribute thousands of servers worldwide providing a highly reliable service to their customers. Not only has reliability been one of the main design goals for such systems -they are engineered to operate under severe and constantly changing number of server failures occurring at all times. Consequently, in addition to being resilient to component or network outages, CDNs are inherently considered resilient to denial-of-service (DoS) attacks as well.In this paper, we focus on Akamai's (audio and video) streaming service and demonstrate that the current system design is highly vulnerable to intentional service degradations. We show that (i) the discrepancy among streaming flows' lifetimes and DNS redirection timescales, (ii) the lack of isolation among customers and services, (e.g., video on demand vs. live streaming), (iii) a highly transparent system design, (iv) a strong bias in the stream popularity, and (v) minimal clients' tolerance for low-quality viewing experiences, are all factors that make intentional service degradations highly feasible. We demonstrate that it is possible to impact arbitrary customers' streams in arbitrary network regions: not only by targeting appropriate points at the streaming network's edge, but by effectively provoking resource bottlenecks at a much higher level in Akamai's multicast hierarchy. We provide countermeasures to help avoid such vulnerabilities and discuss how lessons learned from this research could be applied to improve DoS-resiliency of large-scale distributed and networked systems in general.

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.

Abstract-Search engines have greatly influenced the way people access information on the Internet as such engines provide the preferred entry point to billions of pages on the Web. Therefore, highly ranked web pages generally have higher visibility to people and pushing the ranking higher has become the top priority for webmasters. As a matter of fact, search engine optimization (SEO) has became a sizeable business that attempts to improve their clients' ranking. Still, the natural reluctance of search engine companies to reveal their internal mechanisms and the lack of ways to validate SEO's methods have created numerous myths and fallacies associated with ranking algorithms; Google's in particular.In this paper, we focus on the Google ranking algorithm and design, implement, and evaluate a ranking system to systematically validate assumptions others have made about this popular ranking algorithm. We demonstrate that linear learning models, coupled with a recursive partitioning ranking scheme, are capable of reverse engineering Google's ranking algorithm with high accuracy. As an example, we manage to correctly predict 7 out of the top 10 pages for 78% of evaluated keywords. Moreover, for content-only ranking, our system can correctly predict 9 or more pages out of the top 10 ones for 77% of search terms. We show how our ranking system can be used to reveal the relative importance of ranking features in Google's ranking function, provide guidelines for SEOs and webmasters to optimize their web pages, validate or disapprove new ranking features, and evaluate search engine ranking results for possible ranking bias.

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