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Web Performance Optimization Part II

Web performance optimization streamlines your content and tunes your server to deliver web pages faster. In the following chapters, you’ll learn how to optimize your web pages and multimedia, shrink your Cascading Style Sheets (CSS) and HTML file sizes, and reduce server requests with sprites and suturing. You’ll learn how to squeeze your Ajax code and make it more robust. You’ll explore advanced techniques such as improving parallel downloads, caching, HTTP compression, and URL rewriting.

Finally, in Chapter 10: Website Optimization Metrics you’ll read about best-practice metrics and tools to measure and optimize your search engine marketing (SEM) campaigns and improve web-site performance. First, let’s explore the user psychology of delay, and trends in web page growth.

The chapter goes on to show how the 8 to 10 second rule has diverged into a faster rule for broadband users and a slower one for dial-up users. The chapter makes the case for fast response times, showing the negative effects of slow downloads.

As the average web page has tripled from 2003 to 2008 the number of objects has nearly doubled to 50 objects per page. Research has shown that object overhead now dominates web page delays. While broadband users have experienced slightly faster response times, dial-up users have been left behind. With these trends in mind, we give response time guidelines for broadband and narrowband users.

An outline of the introduction to Part II follows:

Chapters in Part II: Web Performance Optimization

Footnotes

Bouch, A. et al. 2000. “Quality is in the Eye of the Beholder: Meeting Users’ Requirements for Internet Quality of Service.”
In CHI 2000 (The Hague, The Netherlands: April 1-6, 2000), 297-304.
In my own book, Speed Up Your Site: Web Site Optimization (New Riders),
I determined an average of 8.6 seconds for tolerable wait time.
Akamai. June 2006. “Retail Web Site Performance: Consumer Reaction to a Poor Online Shopping Experience.”
Akamai Technologies, http://www.akamai.com (accessed February 10, 2008). This is a JupiterResearch abandonment survey commissioned by Akamai.
Linden, G. November 6, 2006. “Marissa Mayer at Web 2.0.”
Geeking with Greg, http://glinden.blogspot.com/2006/11/marissa-mayer-at-web-20.html (accessed February 8, 2008).
Farber, D. November 9, 2006. “Google’s Marissa Mayer: Speed Wins.”
CNET Between the Lines, http://blogs.zdnet.com/BTL/?p=3925 (accessed February 10, 2008).
Kohavi, R., and R. Longbotham. 2007. “Online Experiments: Lessons Learned.”
Computer 40 (9): 103-105. The Amazon statistic was taken from a <ahref=”http: home.blarg.net=”” ~glinden=”” stanforddatamining.2006-11-29.ppt”=””>presentation by Greg Linden at Stanford: http://home.blarg.net/ ~glinden/StanfordDataMining.2006-11-29.ppt.</ahref=”http:>
Ceaparu, I. et al. 2004. “Determining Causes and Severity of End-User Frustration.”
International Journal of Human-Computer Interaction 17 (3): 333-356. Slow websites inhibit users from reaching their goals, causing frustration.
Akamai. 2007. “Boosting Online Commerce Profitability with Akamai.”
Akamai Technologies, http://www.akamai.com (accessed February 10, 2008). Based on the finding that 30% to 50% of transactions above the four-second threshold bail out, Akamai estimated that by reducing the percentage of transactions above this threshold from 40% to 10%, conversion rates will improve by 9% to 15%.
Skadberg, Y., and J. Kimmel. 2004. “Visitors’ flow experience while browsing a Web site: its measurement, contributing factors and consequences.”
Computers in Human Behavior 20 (3): 403-422. Flow is an optimal experience where users are fully engaged in an activity.
Nah, F. 2004. “A study on tolerable waiting time: how long are Web users willing to wait?”
Behaviour & Information Technology 23 (3): 153-163.
Lindgaard, G. et al. 2006. “Attention web designers: You have 50 milliseconds to make a good first impression!”
Behaviour and Information Technology 25 (2): 115-126.
Tractinsky, N. et al. 2006. “Evaluating the consistency of immediate aesthetic perceptions of web pages,”
International Journal of Human-Computer Studies 64 (11): 1071-1083.
Domenech, J. et al. 2007. “A user-focused evaluation of web prefetching algorithms.”
Computer Communications 30 (10): 2213-2224.
Flinn, D., and B. Betcher. “Re: latest top 1000 website data?”
Email to author, January 8, 2008. Gomez, Inc. provided the top 1000 web page data from June 2006 to January 2008 (available at https://www.websiteoptimization.com/secrets/performance/survey.zip).
Yuan, J.., C. H. Chi., and Q. Sun. 2005. “A More Precise Model for Web Retrieval.”
In WWW 2005 (Chiba, Japan: May 10-14, 2005), 926-927. Figure II-3 used by permission.
Bent, L., and G. Voelker. 2002. “Whole Page Performance.”
In WCW 2002 (Boulder, CO: August 14-16, 2002), 8.
Hall, J. et al. 2003. “The Effect of Early Packet Loss on Web Page Download Times.”
In PAM 2003 (La Jolla, CA: April 6-8, 2003).
Berkowitz, D., and A. Gonzalez. “Andy: Keynote data for your use.”
Email to author (February 8, 2008). Keynote Systems, Inc. provided the graph of the KB40 response time from February 2006 to February 2008.
Rushlo, B. “web performance download time guidelines?”
Email to author, February 21, 2008. Keynote response time guidelines.

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