Website content performance modeling html5 conference 2014
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Website Content
Performance Modeling
HTML5 Conference, Oct. 21st, 2014Pierre 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
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What parameters don’t really matter, contd.
● 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
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What parameters have most impact, contd.
● 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
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Middle-Mile RTT (ms) ~ 3.1 % * Distance (miles)For distances > 500 miles
SF - NY RTT
at speed of
light = 27 ms
SF - NY RTT
thru internet ~
80 ms
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Impact of content size & connection type, part 1
Client Server
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Impact of content size & connection type, part 2
Initcwnd = 3 Initcwnd = 10
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Impact of content size & connection type, part 3
● Total download size (KB), per number of Round Trips
50 K
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Connection Reuse: CDN Intermediary Paradigm
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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