Web Performance Calendar

The speed geek's favorite time of year
2010 Edition
Philip Tellis photo

Philip Tellis (@bluesmoon) is a geek living and working in California's Silicon Valley. He works with Yahoo!'s performance and security groups on measuring and improving the performance and security of Yahoo!'s websites. Philip writes code because it's fun to do. He loves making computers do his work for him and spends a lot of time trying to get the most out of available hardware. He is equally comfortable with the back end and the front end and his tech blog has articles ranging from Operating System level hacks to Accessibility in Rich Internet Applications.

The craft of web performance has come a long way from yslow and the first few performance best practices. Engineers the web over have made the web faster and we have newer tools, many more guidelines, much better browser support and a few dirty tricks to make our users’ browsing experience as smooth and fast as possible. So how much further can we go?


The speed of light has a fixed upper limit, though depending on the medium it passes through, it might be lower. In fibre, this is about 200,000 Km/s, and it’s about the same for electricity through copper. This means that a signal sent over a cable that runs 7,000Km from New York to the UK would take about 35ms to get through. Channel capacity (the bit rate), is also limited by physics, and it’s Shannon’s Law that comes into play here.

This, of course, is the physical layer of our network. We have a few layers above that before we get to TCP, which does most of the dirty work to make sure that all the data that your application sends out actually gets to your client in the right order. And this is where it gets interesting.


Éric Daspet’s article on latency includes an excellent discussion of how slow start and congestion control affect the throughput of a network connection, which is why google have been experimenting with an increased TCP initial window size and want to turn it into a standard. Each network roundtrip is limited by how long it takes photons or electrons to get through, and anything we can do to reduce the number of roundtrips should reduce total page download time, right? Well, it may not be that simple. We only really care about roundtrips that run end-to-end. Those that run in parallel need to be paid for only once.

When thinking about latency, we should remember that this is not a problem that has shown up in the last 4 or 5 years, or even with the creation of the Internet. Latency has been a problem whenever signals have had to be transmitted over a distance. Whether it is a rider on a horse, a signal fire (which incidentally has lower latency than light through fibre[1]), a carrier pigeon or electrons running through metal, each has had its own problems with latency, and these are solved problems.


There are three primary ways to mitigate latency. Cache, parallelise and predict[2]. Caching reduces latency by bringing data as close as possible to where it’s needed. We have multiple levels of cache including the browser’s cache, ISP cache, a CDN and front-facing reverse proxies, and anyone interested in web performance already makes good use of these. Prediction is something that’s gaining popularity, and Stoyan has written a lot about it. By pre-fetching expected content, we mitigate the effect of latency by paying for it in advance. Parallelism is what I’m interested in at the moment.

Multi-lane highways

Mike Belshe’s research shows that bandwidth doesn’t matter much, but what interests me most is that we aren’t exploiting all of this unused channel capacity. Newer browsers do a pretty good job of downloading resources in parallel, and with a few exceptions (I’m looking at you Opera), can download all kinds of resources in parallel with each other. This is a huge change from just 4 years ago. However, are we, as web page developers, building pages that can take advantage of this parallelism? Is it possible for us to determine the best combination of resources on our page to reduce the effects of network latency? We’ve spent a lot of time, and done a good job combining our JavaScript, CSS and decorative images into individual files, but is that really the best solution for all kinds of browsers and network connections? Can we mathematically determine the best page layout for a given browser and network characteristics[3]?

Splitting the combinative

HTTP Pipelining could improve throughput, but given that most HTTP proxies have broken support for pipelining, it could also result in broken user experiences. Can we parallelise by using the network the way it works today? For a high capacity network channel with low throughput due to latency, perhaps it makes better sense to open multiple TCP connections and download more resources in parallel. For example, consider these two pages I’ve created using Cuzillion:

  1. Single JavaScript that takes 8 seconds to load
  2. 4 JavaScript files that take between 1 and 3 seconds each to load for a combined 8 second load time.

Have a look at the page downloads using FireBug’s Net Panel to see what’s actually happening. In all modern browsers other than Opera, the second page should load faster whereas in older browsers and in Opera 10, the first page should load faster.

Instead of combining JavaScript and CSS, split them into multiple files. How many depends on the browser and network characteristics. The number of parallel connections could start of based on the ratio of capacity to throughput and would reduce as network utilisation improved through larger window sizes over persistent connections. We’re still using only one domain name, so no additional DNS lookup needs to be done. The only unknown is the channel capacity, but based on the source IP address and a geo lookup[4] or subnet to ISP map, we could make a good guess. Boomerang already measures latency and throughput of a network connection, and the data gathered can be used to make statistically sound guesses.

I’m not sure if there will be any improvements or if the work required to determine the optimal page organisation will be worth it, but I do think it’s worth more study. What do you think?


  1. Signal fires (or even smoke signals) travel at the speed of light in air v/s light through fibre, however the switching time for signal fires is far slower, and you’re limited to line of sight.
  2. David A. Patterson. 2004. Latency lags bandwith[PDF]. Commun. ACM 47, 10 (October 2004), 71-75.
  3. I’ve previously written about my preliminary thoughts on the mathematical model.
  4. CableMap.info has good data on the capacity and latency of various backbone cables.