Overclocking the Yahoo!: CDN for faster web page loads

  • Authors:
  • Mohammad Al-Fares;Khaled Elmeleegy;Benjamin Reed;Igor Gashinsky

  • Affiliations:
  • University of California San Diego, San Diego, CA, USA;Yahoo! Research, Santa Clara, CA, USA;Yahoo! Research, Santa Clara, CA, USA;Yahoo! Inc., Sunnyvale, CA, USA

  • Venue:
  • Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
  • Year:
  • 2011

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Abstract

Fast-loading web pages are key for a positive user experience. Unfortunately, a large number of users suffer from page load times of many seconds, especially for pages with many embedded objects. Most of this time is spent fetching the page and its objects over the Internet. This paper investigates the impact of optimizations that improve the delivery of content from edge servers at the Yahoo! Content Delivery Network (CDN) to the end users. To this end, we analyze packet traces of 12.3M TCP connections originating from users across the world and terminating at the Yahoo! CDN. Using these traces, we characterize key user-connection metrics at the network, transport, and the application layers. We observe high Round Trip Times (RTTs) and inflated number of round trips per page download (RTT multipliers). Due to inefficiencies in TCP's slow start and the HTTP protocol, we found several opportunities to reduce the RTT multiplier, e.g. increasing TCP's Initial Congestion Window (ICW), using TCP Appropriate Byte Counting (ABC), and using HTTP pipelining. Using live workloads, we experimentally study the micro effects of these optimizations on network connectivity, e.g. packet loss rate. To evaluate the macro effects of these optimizations on the overall page load time, we use realistic synthetic workloads in a closed laboratory environment. We find that compounding HTTP pipelining with increasing the ICW size can lead to reduction in page load times by up to 80%. We also find that no one configuration fits all users, e.g. increasing the TCP ICW to a certain size may help some users while hurting others.