Ao-Jan Su

Drafting behind Akamai: inferring network conditions based on CDN redirections

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 Akamais CDN. We probe Akamais network from 140 PlanetLab (PL) 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 low-overhead pruning algorithms that avoid Akamai-driven paths when they are not beneficial. Because these Akamai nodes are part of a closed system, we provide a method for mapping Akamai-recommended paths to those in a generic overlay and demonstrate that these one-hop paths indeed outperform direct ones.

How to Improve Your Google Ranking: Myths and Reality

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’sin 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.

Evaluating the impact of data center network architectures on application performance in virtualized environments

In recent years, data center network (DCN) architectures (e.g., DCell [5], FiConn [6], BCube [4], FatTree [1], and VL2 [2]) received a surge of interest from both the industry and academia. However, none of existing efforts provide an in-depth understanding of the impact of these architectures on application performance in practical multi-tier systems under realistic workload. Moreover, it is also unclear how these architectures are affected in virtualized environments. In this paper, we fill this void by conducting an experimental evaluation of FiConn and FatTree, each respectively as a representative of hierarchical and flat architectures, in a three-tier transaction system using virtual machine (VM) based implementation. We observe several fundamental characteristics that are embedded in both classes of network topologies and cast a new light on the implication of virtualization in DCN architectures. Issues observed in this paper are generic and should be properly addressed by any DCN architectures before being considered for actual deployment, especially in mission-critical real-time transaction systems.

Relative Network Positioning via CDN Redirections

Many large-scale distributed systems can benefit from a service that allows them to select among alternative nodes based on their relative network positions. A variety of approaches propose new measurement infrastructures that attempt to scale this service to large numbers of nodes by reducing the amount of direct measurements to end hosts. In this paper, we introduce a new approach to relative network positioning that eliminates direct probing by leveraging pre-existing infrastructure. Specifically, we exploit the dynamic association of nodes with replica servers from large content distribution networks (CDNs) to determine relative position information - we call this approach CDN-based relative network positioning (CRP). We demonstrate how CRP can support two common examples of location information used by distributed applications: server selection and dynamic node clustering. After describing CRP in detail, we present results from an extensive wide-area evaluation that demonstrates its effectiveness.

How to Improve Your Search Engine Ranking: Myths and Reality

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 Web masters. As a matter of fact, Search Engine Optimization (SEO) has became a sizeable business that attempts to improve their clients’ ranking. Still, the lack of ways to validate SEO’s methods has created numerous myths and fallacies associated with ranking algorithms. In this article, we focus on two ranking algorithms, Google’s and Bing’s, and design, implement, and evaluate a ranking system to systematically validate assumptions others have made about these popular ranking algorithms. We demonstrate that linear learning models, coupled with a recursive partitioning ranking scheme, are capable of predicting ranking results 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 a search engine’s ranking function, provide guidelines for SEOs and Web masters to optimize their Web pages, validate or disprove new ranking features, and evaluate search engine ranking results for possible ranking bias.

Understanding data center network architectures in virtualized environments: A view from multi-tier applications

In recent years, data center network (DCN) architectures (e.g., DCell [11], FiConn [14], BCube [10], FatTree [1], and VL2 [8]) received a surge of interest from both the industry and academia. However, evaluation of these newly proposed DCN architectures is limited to MapReduce or scientific computing type of traffic patterns, and none of them provides an in-depth understanding of their performance in conventional transaction systems under realistic workloads. Moreover, it is also unclear how these architectures are affected in virtualized environments. In this paper, we fill this void by conducting an experimental evaluation of FiConn and FatTree, each respectively as a representative of hierarchical and flat architectures, in a clustered three-tier transaction system using a virtualized deployment. We evaluate these two architectures from the perspective of application performance and explicitly consider the impact of server virtualization. Our experiments are conducted in two major testing scenarios, service fragmentation and failure resilience, from which we observe several fundamental characteristics that are embedded in both classes of network topologies and cast a new light on the implication of virtualization in DCN architectures. Issues observed in this paper are generic and should be properly considered in any DCN design before the actual deployment, especially in mission-critical real-time transaction systems.