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Performance Modelling
Matemáticas y sentido común
import random
import time
import requests
class Speaker(object):
def __init__(self):
born = "Avilés - Asturias"
studies = "Applied Maths and Computability"
jobdescription = "Performance Jedi"
company = "Telefónica I+D"
team = "Product Engineering"
project = "AWAZZA"
talks = [VLC Testing, DevopsDays, WebPerfDays, Velocity]
def talk(self):
start_timer = time.time()
r = requests.get('http://www.slideshare.net/almudenavivanco')
r.raw.read()
latency = time.time() - start_timer
self.custom_timers[‘After Test 2016'] = latency
if __name__ == '__main__':
speech = Speaker()
speech.talk()
print trans.custom_timers
Almudena Vivanco
(@MrsDaehin)
Users vs request
• ¿cuánto aguanta nuestra plataforma?
SCENARIOS: Identify the scenarios that are most commonly executed or most resource-intensive
WORKLOAD MODEL: User Session Duration in average. It is important to define the load levels that will translate into
concurrent usage, overslapping users, or user sessions per second.
USER SCENARIO: Navigational Path, including intermediate steps or activities, taken by the user to complete a task. We will
call it User Session from now on.
THINK TIMES: User thinktimes → Pause between pages during a User Session depending on the User Type*
User Types: Identify the User … new, revisiting or both.
Performance Acceptance Criteria: Response time, System load, Throughput ...
METRICS: Only well-selected metrics that are analyzed correctly and contextually provide information of value.
DESIGN TEST: Using your scenarios, key metrics, and workload analysis …
RUN TEST: the load simulation must reflect the test design
ANALYZE RESULTS: Find bottlenecks, memory leaks, cpu hogs, bad sofware design …
The Maths behind
“mi tiempo medio de respuesta está
bien."
Despues … en PROD ...
Performance
It's not just about adding more servers
Apdex Index
t = acceptable response time
satisfied = response time < t
tolerating = t < response time > 4t
frustrated = response time > 4t
Apdex [t] = (satisfied + tolerating/2)/total requests
• Percentiles
• Moda
• Media
• Mediana
• Desviación Estándar
Matemáticas y Sentido Común
http://www.raosoft.com/samplesize.html
Distribución de Poisson
Modelando el uso de la aplicación
Identificar el objetivo de la prueba
• Volumen de tráfico?
• Escala?
• Picos de carga?
• Robusto?
0 Second
? Second
? Second
? Second
Acceptable User Experience
Unacceptable User Experience
Questionable User Experience
Business is Closed
Response
Time
Load
Estrategia de Test
 Prueba de Carga
 Prueba de Stress
 Prueba de Picos
 Prueba de Robustez
Cuántos Virtual User para simular una población de tamaño N
Estadísticas
Sesión
Páginas Vistas
Flujo
Dispositivo
Localizacion
Modelización y Jmeter
JMeter
Jmeter Test Plan
User Defined Variables
Functional Test Mode
User defined Variables at Test Plan level to be used from Jenkins
53.35
6.91
16.57
10.89
8.47
2.67 0.96 0.18
Visit a Channel
Visit My stuff area
Visit the Home page
Estudio del tráfico Real. Cogemos el Maximo y la Mediana
Porcentaje de Visitas por Funcionalidad
WorkLoad VUsers
Requests Expected per VUser
Results  SPOF
User Behaviour in JMeter
Only Once Controllers
Cache Management
Cookie Management
Header Manager
Think Times
Jmeter in Cloud commercial tools
Resources
• http://www.raosoft.com/samplesize.html
• http://analyze.websiteoptimization.com/wso
• http://mobitest.akamai.com/m/index.cgi
• http://stevesouders.com/mobileperf/mobileperfbkm.php
• https://msdn.microsoft.com/en-us/library/bb924370.aspx
• http://www.quotium.com/performance/load-testing-
¿Quieres desarrollar el FUTURO?
¿Te gustaría ser parte de la próxima REVOLUCIÓN
DIGITAL? Telefónica puede hacerlo posible!
Si estás interesado en saber más…:
https://career012.successfactors.eu/sfcareer/jobreqcareerpvt?jobId=57526&company=Telefonica&
username=&st=F7ADCB33705144D651DF895CABA34576C6732116

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Modelling performance tests