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Capacidade Planejada
Rodrigo Albani de Campos - camposr@gmail.com - @xinu



Wednesday, August 25, 2010
Agenda

                   • Motivações
                   • Capacidade + Velocidade
                   • Universal Scalability Model
                   • Extra: Dimensionamento de pools

Wednesday, August 25, 2010
Impacto na imagem do produto


Wednesday, August 25, 2010
Impacto na imagem do produto


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




Wednesday, August 25, 2010
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
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
Capacity and Velocity

Wednesday, August 25, 2010
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
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
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
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
Satisfied          Tolerating   Frustrated

APDEX - http://www.apdex.org

Wednesday, August 25, 2010
Samples       12000
                             Satisfied      10000
                             Tolerating      800


                             Apdex        86,67%




APDEX - http://www.apdex.org

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

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

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




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

Wednesday, August 25, 2010
Redundância

               55%                         55%



               55%                         55%




Wednesday, August 25, 2010
Redundância

            73,3%
                                      X
            73,3%                          73,3%




Wednesday, August 25, 2010
Redundância

             110%
                                      X
             110%
                                      X
Wednesday, August 25, 2010
How many servers do we need ?

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

Wednesday, August 25, 2010
Perguntas ?



Wednesday, August 25, 2010

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Capacidade Planejada

  • 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
  • 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
  • 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
  • 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