A Longitudinal View of Dual-Stacked Websites—Failures, Latency and Happy Eyeballs

IEEE/ACM Transactions on Networking(2019)

引用 14|浏览38
暂无评分
摘要
IPv6 measurement studies have focussed on measuring IPv6 adoption, while studies on measuring IPv6 performance have either become dated or only provide a snapshot view. We provide a longitudinal view of the performance of dual-stacked websites. We show that (since 2013) latency towards ALEXA 10K websites with AAAA entries over the six years have reduced by 29% over IPv4 and by 57% over IPv6. As of Dec 2018, 56% of these websites are faster over IPv6 with 95% of the rest being at most 1 ms slower. We also identify glitches in web content delivery that once fixed can help improve the user experience over IPv6. Using a publicly available dataset, we show that 40% of ALEXA 1M websites with AAAA entries were not accessible over IPv6 in 2009. These complete failures have reduced to 1.9% as of Jan 2019. However, our data collection on partial failures helps identify further that 27% of these popular websites with AAAA entries still suffer from partial failure over IPv6. These partial failures are affected by DNS resolution errors on images, javascript and CSS content. For 12% of these websites, more than half of the content belonging to same-origin sources fails over IPv6, while analytics and third-party advertisements contribute to failures from cross-origin sources. Our results also contribute to the IETF standardisation process. We witness that using an happy eyeballs timer value of 250 ms, clients prefer IPv6 connections to 99% of ALEXA 10 K websites (with AAAA entries) more than 96% of the time. Although, this makes clients prefer slower IPv6 connections in 81% of the cases. Our results show that a Happy Eyeballs (MBA) timer value of 150 ms does not severly affect IPv6 preference towards websites. The entire dataset presenting results on partial failures, latency and HE used in this paper is publicly released.
更多
查看译文
关键词
Google,Browsers,Internet,Measurement uncertainty,IEEE transactions,Cascading style sheets,Servers
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要