The document discusses capacity planning and velocity. It covers topics like the universal scalability model, defining SLAs based on user experience, and using measured performance data to model and forecast system throughput needs. It emphasizes that speed is as important as availability and advocates defining SLAs based on how the user perceives performance rather than just technical metrics. Questions from the audience are also discussed.

1. Capacidade Planejada
Rodrigo Albani de Campos - camposr@gmail.com - @xinu
Wednesday, August 25, 2010

2. Agenda
• Motivações
• Capacidade + Velocidade
• Universal Scalability Model
• Extra: Dimensionamento de pools
Wednesday, August 25, 2010

3. Impacto na imagem do produto
Wednesday, August 25, 2010

4. Impacto na imagem do produto
Wednesday, August 25, 2010

5. SLA Horas / Trimestre Impacto $
98% 43,2 $142.600.000
99% 21,6 $71.300.000
99,90% 2,16 $7.130.000
99,99% 0,216 $713.000
99,9990% 0,0216 $71.300
99,9999% 0,00216 $7.130
Amazon 2010Q1 Net Sales
$7.13 Billion Dollars
Wednesday, August 25, 2010

6. Distinct Query Revenue/ Any Clicks Satisfaction Time to Click
Queries/User Refinement User (increase in
ms)
50ms 0 0 0 0 0 0
200ms 0 0 0 -0,30% -0,40% 500
500ms 0 -0,60% -1,20% -1,00% -0,90% 1200
1000ms -0,70% -0,90% -2,80% -1,90% -1,60% 1900
2000ms -1,80% -2,10% -4,30% -4,40% -3,80% 3100
$85.000.000 em três meses na
Amazon
Receita
Impacto de lentidão na entrega de conteúdo
The User and Business Impact of Server Delays, Additional Bytes, and HTTP
Chunking in Web Search - Eric Schurman (Amazon), Jake Brutlag (Google)
Wednesday, August 25, 2010

7. Receita
Impacto de lentidão na entrega de conteúdo
The User and Business Impact of Server Delays, Additional Bytes, and HTTP
Chunking in Web Search - Eric Schurman (Amazon), Jake Brutlag (Google)
Wednesday, August 25, 2010

8. “Fast isn’t a feature, fast is
a Requirement”
Jesse Robins - OPSCode
Wednesday, August 25, 2010

9. Average # of Average
Average # of
resources / document size
hosts / page
page Kb
42,14 8,39 477,26
Composição de páginas na Web - Top Sites
Web Metrics: Size and number of resources - Sreeram
Ramachandran
http://code.google.com/speed/articles/web-metrics.html
Wednesday, August 25, 2010

10. Load Time First Byte Start Complete Requests Bytes In
Render Time (s)
Mercado Livre 1,972 329 ms 543 ms 2,491 22 74 KB
Ebay 1,999 399 ms 1493 ms 3,103 36 237 KB
Amazon 4,777 504 ms 1105 ms 6,289 69 454 KB
newegg 6,848 328 ms 1211 ms 7,891 138 459 KB
Bestbuy 7,508 447 ms 1733 ms 10,41 99 676 KB
Submarino 10,436 250 ms 2474 ms 10,436 151 1,125 KB
Casas Bahia 15,09 500 ms 4401 ms 15,799 100 732 KB
http://www.webpagetest.org/ TEST RESULTS
July 2010
Wednesday, August 25, 2010

11. Capacity and Velocity
Wednesday, August 25, 2010

12. Service time: Tempo de ocupação do recurso
(s,ms,μs)
Arrival rate: Taxa de chegada de requisições
para o recurso (hit/s,qps,etc...)
Little’s Law: The long term average number of customers in a
stable system L is equal to the long term average arrival rate λ,
multiplied by the long term average time a customer spends in the
system,W
Queuing Theory 101
Wednesday, August 25, 2010

13. Service A
0,1 1600
0,09
1400
0,08
1200
0,07
1000
0,06
Service Time (s)
Frequency
0,05 800
0,04
600
0,03
400
0,02
200
0,01
0 0
0 50 100 150 200 250 300
Hits/s
Service Time Frequency
Average Service Time against Arrival Rates
Wednesday, August 25, 2010

14. Service B
2,5 6000
5000
2
4000
1,5
Service Time (s)
Frequency
3000
1
2000
0,5
1000
0 0
0 20 40 60 80 100 120 140 160 180 200
Hits/s
Service Time Frequency
Average Service Time against Arrival Rates
Wednesday, August 25, 2010

15. Service B
2,5 6000
5000
2
4000
Frustrated 1,5
Service Time (s)
Frequency
3000
1
2000
Tolerating
0,5
1000
0 0
0 20 40 60 80 100 120 140 160 180 200
Hits/s
Satisfied Service Time Frequency
APDEX - http://www.apdex.org
Wednesday, August 25, 2010

16. Satisfied Tolerating Frustrated
APDEX - http://www.apdex.org
Wednesday, August 25, 2010

17. Samples 12000
Satisfied 10000
Tolerating 800
Apdex 86,67%
APDEX - http://www.apdex.org
Wednesday, August 25, 2010

18. Forecasting (Sort of...)
Wednesday, August 25, 2010

19. System C
40
35
30 Measured Throughput
Users (N) X(N)
1 2,91
25
Throughput
20 2 5,67
4 10,86
15
8 18,65
10
16 25,91
32 36,68
5
64 37,34
0
0 10 20 30 40 50 60 70
Virtual Users
Measured X
Forecasting (Sort of...)
Wednesday, August 25, 2010

20. System C
45
40
35
30
25
Throughput
20
15
10
5
0
0 10 20 30 40 50 60 70 80 90
Virtual Users
Measured X Poly. (Measured X)
Forecasting (Sort of...)
Wednesday, August 25, 2010

21. System C
45
40
35
30
25
Throughput
20
15
10
5
0
0 10 20 30 40 50 60 70 80 90
Virtual Users
Measured X Poly. (Measured X)
Forecasting (Sort of...)
Wednesday, August 25, 2010

22. System C
40
35
30
25
Throughput
20
15
10
5
0
0 20 40 60 80 100 120 140
Virtual Users
Measured X Modeled X(N)
Using the Universal Scalability Model
Neil J. Gunther http://www.perfdynamics.com/Test/gcaprules.html#sec:scalability
Wednesday, August 25, 2010

23. System D
60
50
40
Throughput
30
20
10
0
0 100 200 300 400 500 600
Virtual Users
Measured X Modeled X(N)
Using the Universal Scalability Model
Neil J. Gunther http://www.perfdynamics.com/Test/gcaprules.html#sec:scalability
Wednesday, August 25, 2010

24. Conclusões e
Considerações
• Velocidade é tão importante quanto
disponibilidade
• Fast is a requirement
• O SLA deve serdo usuário
experiência
definido considerando a
• Não existem bolas de cristal
Wednesday, August 25, 2010

25. In God we trust.
Everyone else
please show me
the data.
Wednesday, August 25, 2010

26. http://capacitricks.wordpress.com/
Wednesday, August 25, 2010

27. How many servers do we need ?
Wednesday, August 25, 2010

28. Redundância
55% 55%
55% 55%
Wednesday, August 25, 2010

29. Redundância
73,3%
X
73,3% 73,3%
Wednesday, August 25, 2010

30. Redundância
110%
X
110%
X
Wednesday, August 25, 2010

31. How many servers do we need ?
Wednesday, August 25, 2010

32. How many servers do we need ?
Wednesday, August 25, 2010

33. Perguntas ?
Wednesday, August 25, 2010