The time it takes to download http resources is a corner stone of a website performance, and ultimately user experience. In this presentation, we review the different network and system setup variables that affect the speed at which each piece of dynamic content gets fetched from a client (e.g. browser) , and explain their relative impact. By leveraging the massive web traffic seen on the Akamai platform, the existing literature and the typical website properties listed at httparchive.org we present a performance model both simple and comprehensive. In particular, we share empirical data for the middle-tier observed RTT, which is one of the main factor impacting download latencies over long distances. To illustrate the model via a web-based chart, we plot the expected performance against the distance client-origin, based on a key set of parameters. It will allow its users to better understand what variables really matter and take actions to improve their website performance

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Website Content
Performance Modeling
HTML5 Conference, Oct. 21st, 2014
Pierre Lermant, Akamai Technologies
@plermant

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What problem are we trying to solve?
● Help IT organizations optimize their serving infrastructure
by modeling website dynamic resource download times

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Existing Models:
● Can be too simple:
○ It is bounded by the speed of light !
● Can be too theoretical
● As a result, no reliable and practical ways to predict
download times of dynamic website resources

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Trivia Question
● How long does it take to download a 50K resource
from New-York to San-Francisco (~2,500 miles)?

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What parameters are at play?

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What parameters don’t really matter
● Last Mile bandwidth is rarely a download bottleneck
Sources: https://www.belshe.com/2010/05/24/more-bandwidth-doesnt-matter-much/
Akamai State of the Internet report, Q2 2014

7. What parameters don’t really matter, contd.
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● Client receiving window buffer size
○ i.e. how much data can be ‘in flight’ between a server and a client
○ Typically set at 65 K, larger than most website resources
● Network loss
○ Assuming here it’s negligible for small resources
Source: httparchive.org

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What parameters have most impact
● New or reused connection, HTTP vs HTTPS
○ TCP connection establishment can require many round trips
● Round Trip Times
○ Last mile latency: Can vary greatly, from ~0 to 100s of ms
○ Middle Mile RTT: Has most impact over long-haul distances

9. What parameters have most impact, contd.
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● Origin server initial TCP congestion window
○ How many packets can be sent at the start of data download
● Content size and distance client-server

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Middle Mile RTT
● Proven difficult to model
○ Cannot be approximated by mathematical equations
○ Is driven by peering negotiations between ISPs
● -> Built an experimental setup to model its value

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RTT modeling experimental setup
Ping agents (ICMP)
Ping targets, distribution
matching internet usage

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RTT/Distance typical distribution

13. Middle-Mile RTT (ms) ~ 3.1 % * Distance (miles)
For distances > 500 miles
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SF - NY RTT
at speed of
light = 27 ms
SF - NY RTT
thru internet ~
80 ms

14. Impact of content size & connection type, part 1
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Client Server

15. Impact of content size & connection type, part 2
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Initcwnd = 3 Initcwnd = 10

16. Impact of content size & connection type, part 3
● Total download size (KB), per number of Round Trips
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50 K

17. Connection Reuse: CDN Intermediary Paradigm
©2014 AKAMAI | FASTER FORWARDTM

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Download times of a 50 KB resource*
● Direct is ~ 400 ms (Initcwnd=3), ~ 300 ms (Initcwnd=10)
● Thru CDN intermediaries ~ (80+10+10) ~ 100 ms
* New http connection, 2,500 miles client-server distance, no network loss, no first
or last mile latencies

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Take Aways

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Main take aways
● Median Middle Mile RTT ~ 3 % of Distance Client-Server
○ This is the primary performance driver over long distances
● Client bandwidth and TCP buffer size are rarely a
download bottleneck

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Main take aways, contd.
● Critical impact of TCP initial congestion window
(=Initcwnd) for new connections.
○ Recent server builds (Linux > 2.6.39) set initcwnd to 10
● Dramatic differences between new and re-used
connections over long distances
○ Set permanent connections at your origin if possible
○ Consider CDN intermediaries for far-away end-users

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Live Model, Screenshot 1
Source: http://www.akamai.com/html/ms/delivering-dynamic-web-content.html

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Live Model, Screenshot 2

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Live Model, Screenshot 3