This document discusses techniques for predicting user activity and prerendering web pages to improve page load times. It notes current median page load times of 2.3-4.3 seconds and proposes using idle time between user actions like typing a URL to prefetch and render pages the user is likely to navigate to next based on their browsing history and habits. The document outlines Google Chrome's implementation of this using the omnibox search suggestions to predict and prerender likely future pages, showing how it has resulted in over 15% of omnibox navigations being nearly instant by preloading pages. It encourages web developers to ensure their sites can support prerendering.

1. Predicting User Activity
to Make The Web Fast
Velocity Conference 2012
Arvind Jain and Dominic Hamon
Google

2. How fast is the web today?
● Chrome ~ 2.3s/5.4s page load time
(median/mean)
● Google Analytics ~ 2.9s/6.9s page load time
(median/mean)
● Mobile ~ 4.3s/10.2s page load time
(median/mean)
http://analytics.blogspot.com/2012/04/global-site-speed-overview-how-fast-are.html

3. Web page size over time
http://httparchive.org/

4. Making the web fast
● Faster browsers
● Faster networks
● Faster hardware
● Faster pages
But not enough to make the web Instant...

5. The time between requests
● ~ 3 seconds to type a URL
● ~ 15 seconds to select a search result
● Plenty of idle time
● Why not use it?

6. Predict and Prerender
● Predict user navigation or follow advice from
page <link rel='prerender'>
● Fetch all resources for a page
● Render to a hidden tab
● Swap in when user navigates

7. Chrome Omnibox Prerendering
● Already provide suggestions to the user
● Opportunity to learn browsing behaviour
● Ability to develop good prediction model
● Little contention for resources

8. Implementation details
● Track whether the user takes a suggestion
● Map user's text input to suggestion and
hit/miss counts
● Given user input and suggestion, calculate
confidence C:
○ C = H / (H + M)
where H is the hit count, M is the miss count

9. Implementation example
● User types 'c'
● Suggestions are:
○ www.cnn.com
○ comcast.net
● User types 'n'
● Omnibox shows 'cn'
● Suggestion is www.cnn.com
● User selects www.cnn.com

10. Implementation example (cont.)
● We store:
{ 'c',
{
{ 'cnn.com', 1, 0 },
{ 'comcast.net', 0, 1 },
},
'cn',
{
{ 'cnn.com', 1, 0 },
},
}

11. Implementation example (cont.)
● Over time, this data structure evolves to
something like:
View your data at chrome://predictors in Chrome 20

12. Implementation example (cont..)
● User types 'c'
● Suggestions are:
○ www.cnn.com
○ comcast.net
● www.cnn.com is selected by default
● Confidence is C = 1
● Start prerendering www.cnn.com

13. Demo

14. Key results
● Coverage
○ Almost a third of Omnibox navigations are
prerendered
● Accuracy
○ About 90% of those prerenders are used
● Instant
○ Between 15% and 20% of Omnibox navigations are
instant (<10 ms)
● Median time saved
○ ~1 second (>40%) per Omnibox navigation

15. Total time saved per day
Omnibox: Over 10 years
Search: Over 20 years

16. Why should you care?
● Your site will be prerendered at some point
by some users
● Check your site is compatible
● Page Visibility API
● Consider <link rel='prerender'> for
your own site
https://developers.google.com/chrome/whitepapers/prerender
http://prerender-test.appspot.com/

17. Thank you
Arvind Jain and Dominic Hamon
Google