Web search is an integral part of our daily lives. Recently, there has been a trend of personalization in Web search, where di erent users receive di erent results for the same search query. The increasing level of personalization is leading to concerns about Filter Bubble e ects, where certain users are simply unable to access information that the search engines' algorithm decides is irrelevant. Despite these concerns, there has been little quanti cation of the extent of personalization in Web search today, or the user attributes that cause it.In light of this situation, we make three contributions. First, we develop a methodology for measuring personalization in Web search results. While conceptually simple, there are numerous details that our methodology must handle in order to accurately attribute di erences in search results to personalization. Second, we apply our methodology to 200 users on Google Web Search and 100 users on Bing. We nd that, on average, 11.7% of results show di erences due to personalization on Google, while 15.8% of results are personalized on Bing, but that this varies widely by search query and by result ranking. Third, we investigate the user features used to personalize on Google Web Search and Bing. Surprisingly, we only nd measurable personalization as a result of searching with a logged in account and the IP address of the searching user. Our results are a rst step towards understanding the extent and e ects of personalization on Web search engines today.
Traffic differentiation-giving better (or worse) performance to certain classes of Internet traffic-is a well-known but poorly understood traffic management policy. There is active discussion on whether and how ISPs should be allowed to differentiate Internet traffic [8, 21], but little data about current practices to inform this discussion. Previous work attempted to address this problem for fixed line networks; however, there is currently no solution that works in the more challenging mobile environment. In this paper, we present the design, implementation, and evaluation of the first system and mobile app for identifying traffic differentiation for arbitrary applications in the mobile environment (i.e., wireless networks such as cellular and WiFi, used by smartphones and tablets). The key idea is to use a VPN proxy to record and replay the network traffic generated by arbitrary applications, and compare it with the network behavior when replaying this traffic outside of an encrypted tunnel. We perform the first known testbed experiments with actual commercial shaping devices to validate our system design and demonstrate how it outperforms previous work for detecting differentiation. We released our app and collected differentiation results from 12 ISPs in 5 countries. We find that differentiation tends to affect TCP traffic (reducing rates by up to 60%) and that interference from middleboxes (including video-transcoding devices) is pervasive. By exposing such behavior, we hope to improve transparency for users and help inform future policies.
No abstract
Google's Quick UDP Internet Connections (QUIC) protocol, which implements TCP-like properties at the application layer atop a UDP transport, is now used by the vast majority of Chrome clients accessing Google properties but has no formal state machine specification, limited analysis, and ad-hoc evaluations based on snapshots of the protocol implementation in a small number of environ-merits. Further frustrating attempts to evaluate QUIC is the fact that the protocol is under rapid development, with extensive rewriting of the protocol occurring over the scale of months, making individual studies of the protocol obsolete before publication. Given this unique scenario, there is a need for alternative techniques for understanding and evaluating QUIC when compared with previous transport-layer protocols. First, we develop an approach that allows us to conduct analysis across multiple versions of QUIC to understand how code changes impact protocol effectiveness. Next, we instrument the source code to infer QUIC's state machine from execution traces. With this model, we run QUIC in a large number of environments that include desktop and mobile, wired and wireless environments and use the state machine to understand differences in transport-and application-layer performance across multiple versions of QUIC and in different environments. QUIC generally outperforms TCP, but we also identified performance issues related to window sizes, re-ordered packets, and multiplexing large number of small objects; further, we identify that QUIC's performance diminishes on mobile devices and over cellular networks.
No abstract
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.