Module 3 IUT Bobigny : Infrastructure et Opérations

INFRASTRUCTURE ET OPÉRATIONS
Gestion de projet web – Module 3

MODULE 3 : INFRASTRUCTURE ET OPÉRATIONS
 Infrastructure, Cloud computing (scaling automatique)
 Exploitation : monitoring, logs, kpi, instrumentation,
test A/B
 Petit rappel : dans un contexte d’entreprise…
Projetez-vous !
GestiondeprojetWeb|IUTBobigny2012-2013|FrédéricRIVAIN-frederic.rivain@gmail.com
2

HOSTING
 Hosting should be:
 Cheap
 Scalable
 Redundant
 99,9% SLA
 Secured
 Efficient
 Allow for fast rollback
 Compatible with automated deployment
 Self-care
 Capacity to deploy internationally
 CDN for static data
 Separate environments: Preprod, Prod
 Optimize more and more to reduce hosting cost.
4

COMPUTING TIMELINE
6

WHAT IS CLOUD COMPUTING?
 Cloud computing leverages the capabilities of the Internet to deliver
“everything-as-a-service”.
 General term for anything that involves:
 delivering hosted services over the Internet
 3 characteristics that differentiate it from traditional hosting:
• Sold on demand, typically by the minute or the hour
• It is elastic: a user can have as much or as little of a service as they want at any
given time
• The service is fully managed by the provider (the consumer needs nothing but a
personal computer and Internet access).
7

3 LAYERS OF CLOUD COMPUTING
 These services are broadly divided into three categories:
1. Infrastructure-as-a-Service (IaaS): example Amazon Web Services
2. Platform-as-a-Service (PaaS): example Google App Engine
3. Software-as-a-Service (SaaS): example web-based email Gmail, Hotmail
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CLOUD LAYERS
9

IAAS
 IaaS transforms the
traditional datacenter
model to that of a
utility.
 Virtualization permits
pooling of cloud
resources.
 Example:
 Amazon Web services
(AWS)
 Rackspace Cloud
 NTT
 Orange Cloud Pro
10

PAAS
 PaaS shortens the
application
development
lifecycle thus
reduces the time to
market.
 PaaS allows the
user to focus on
application
development
without the burden
of managing the
underlying
development
platform, hardware
and resources. 11

SAAS
 SaaS is an evolution from
the traditional application
service provider (ASP)
model:
 Customers can share
application instances, thus
increasing application
utilization and driving
economies of scale.
 SaaS is optimized for use
over a network and as
such, applications become
available on-demand.
 As a result, the traditional
licensing approach of
software is transformed to
a subscription. 12

PUBLIC CLOUD VS PRIVATE CLOUD
 The public cloud infrastructure is made available to the
general public in the form of a subscription through the
Internet.
 In a private cloud formation, the cloud infrastructure is
operated solely for an organization.
 Hybrid cloud formation consists of two or more
heterogeneous cloud formations.
13

ADVANTAGES OF CLOUD COMPUTING
 API to interact with the Cloud
 Reduced cost based on usage
 Ubiquitous network access: available through any Internet-
enabled devices
 Sharing of resources and costs across a large pool of users
allows for:
 Centralization of infrastructure in locations with lower costs (such
as real estate, electricity, etc.)
 Peak-load capacity increases (users need not engineer for
highest possible load-levels)
 Utilization and efficiency improvements for systems that are often
only 10–20% utilized
 Reliability with multiple redundant sites
 Scalability: consumers can increase or decrease capacity at
will
 Maintenance: done by the provider
 Metering and monitoring
14

ISSUES OF PUBLIC CLOUD COMPUTING
 Privacy
 Security
 Availability and performance
 Lack of control
15

CDN
 Content Delivery Network is basically a large network distributed storage
hard drive.
 It can store any types of static files, deployed over multiple location, so
as to optimize bandwidth and download access for any client.
 Advantages:
 Low latency. Improves content load times
 High bandwidth. Enables high bit rate HD video and other bandwidth-heavy
applications
 Redundant. Eliminates single points of failure
 Scalable. Ensure great experience as number of end users grows
 Global. Worldwide network provides great experience regardless of geography
 Cost-effective. Pay as you go model provides flexibility for your business
 Drawbacks:
 Propagation time on all nodes. A CDN is not made to store files that need to
be updated often.
16

HOW CDN WORKS
 Based on object caching:
17
CDN
Edge LocationCustom origin
with content
Data transfer of cached content
to end user (if cached)
Data transfer of content to CDN
edge location to cache
Cached copies of
objects
If content not cached – object
requested from origin
12
3 4
Request routed to most
optimal edge location
End-user requests content

TYPICAL WEB ARCHITECTURE
 Beware of Single Point of Failure (SPoF)
 Mitigate risks
19

AUTO-SCALING
 Auto-scaling = Add or remove servers dynamically
 Scale up and down your servers to:
 Improve availibility of service
 Optimize hosting cost
 Choose the proper metrics to define scale rules
 Set hard limits: 2 min, N max
 Scale up early, scale down slowly 20

UNDERSTANDING AUTO-SCALING
21

CACHING
 4 types of caches:
 Web browser cache (client-side)
 Network cache
 CDN: Amazon CloudFront, Akamai, Azure CDN, …
 Reverse proxy: Nginx, Varnish, Apache
 Application cache:
 Memcached (key-value)
 Database cache
22
Memcached Reverse Proxy

DNS & SSL
 DNS (Domain
Name System)
 Champ A :
adresse IP d'un
hôte
 Champ MX :
nom de domaine
des serveurs de
mail associés
 Champ CNAME :
nom canonique
d'un alias
 SSL (Secure
Sockets Layer)
23

MONITORING
 Monitoring is mandatory in web operations.
 Centralized console
 Powerful alert system with email and SMS
 On-call organization
 Define metrics
25

LOGS
 Logs are critical for operations:
 Application logs
 Database logs
 System logs
 Various log levels:
 Debug: used for problem diagnosis. Not in Production.
 Info: help trace execution in a production environment.
 Warning: A warning message indicates a potential problem in
the system.
 Error: serious problem in the system.
 Include timestamp, caller identity, source code details,
thread id
 Log in real-time
 Centralize logs
 Use Logging libraries (log4j, log4php…) 26

DATA-CENTRIC: MEASURE ANYTHING,
MEASURE EVERYTHING
 “You can’t manage what you don’t measure.”
W. Edwards Deming, Peter Drucker
 Make good use of data. Use science.
 Implement an instrumentation framework, to have the ability to track any
indicator.
27

A/B TESTING
 Pour répondre à la simple question : B est-il meilleur que A ?
Que va-t-il se passer si je fais B ?
 Expérimenter différentes variantes sur différentes populations
 Mesurer
 Décider sur la base de données explicites, statistiquement signifiantes
 Systématiser l’expérimentation en test A/B chaque fois qu’on
introduit de nouvelles évolutions et comparer avec l’existant
28

KPI AND BUSINESS INTELLIGENCE
 Define KPI (Key Performance
Indicators):
 Technical
 Business
 Build simple real-time dashboards to
expose KPI
 Use datamining solutions to do deeper
analysis
 Datamining tools
 QlikView http://www.qlikview.com/
 Jaspersoft http://www.jaspersoft.com/fr
 Tableau Software
http://www.tableausoftware.com/
 And learn about Big Data… 29

EXERCISE 1: DNS & SERVER SECURITY
1. Set up a DNS name by using
https://freedns.afraid.org/menu/
2. Check DNS propagation:
 http://www.whatsmydns.net
3. Check your site security, for instance with:
http://www.websecurify.com/
31

EXERCISE 2 : LOAD TESTING & MONITORING
1. Create a Jmeter script to simulate load on your web
site.
 Install from http://jmeter.apache.org/
2. Use a monitoring web site to check availibility of
your site. For instance:
 http://www.siteuptime.com/
 http://www.monitor.us
 http://portal.monitis.com/
 http://www.catchpoint.com/
32

EXERCISE 3: A/B TESTING
 Build a variant of your homepage and simulate A/B
testing by using:
 http://visualwebsiteoptimizer.com/
33

EXERCISE 4 : ARCHITECTURE CLOUD
1. Create a free account on Outscale:
http://www.outscale.com
2. Launch a mini Linux instance
3. Deploy your website into a virtual instance
 Try again load-testing with Jmeter and check out
scale-up and down
34

CLOUD COMPUTING
 Aws Blog http://aws.typepad.com/aws/
 High Scalability http://highscalability.com/
 Netflix blog http://techblog.netflix.com/
 Ysance blog http://decrypt.ysance.com/
36

INSTRUMENTATION
 Make it ridiculously simple for any engineer to get anything they can
count or time into a graph with almost no effort
 http://codeascraft.etsy.com/2011/02/15/measure-anything-measure-
everything/
 http://codeascraft.etsy.com/2010/12/08/track-every-release/
 http://codeascraft.etsy.com/2012/06/21/building-websites-with-
science/
 https://github.com/etsy/statsd
 Graphite for graphics: http://graphite.wikidot.com/
 Big Data et décisionnel:
 http://pro.clubic.com/it-business/article-508407-1-big-data-analyse-
donnees-interesse-entreprises.html
 Big Data: The Management Revolution : http://hbr.org/2012/10/big-
data-the-management-revolution/ar/1
37

MONITORING
 http://newrelic.com/
 http://www.zenoss.com/
 http://sixrevisions.com/tools/10-free-server-network-monitoring-tools-that-kick-ass/
 http://decrypt.ysance.com/2011/02/comparatif-outils-monitoring-metrologie-supervision-1-zabbix-
centreon-nagios-cacti-munin/
 http://decrypt.ysance.com/2011/02/comparatif-outils-monitoring-metrologie-supervision-2-zabbix-
centreon-nagios-cacti-munin/
 http://www.thegeekstuff.com/2009/09/top-5-best-network-monitoring-tools/
 Ganglia : http://ganglia.info/
 Centralized monitoring: http://www.splunk.com/ http://www.moonlit-software.com
 http://www.siteuptime.com/ http://mon.itor.us/index.jsp http://www.scivisum.co.uk/
 http://www.catchpoint.com/ http://www.compuware.com/application-performance-management/
 http://portal.monitis.com/ http://www.keynote.com/ http://www.yottaa.com/
38

TEST A/B
 “Not only a marketing tool focused on UI, but most of all a developer tool to be used
easily when implementing new features, from front-end to back-end.”
“The goal is to have split-testing be a continuous part of our development process, so
much that it is considered a completely routine part of developing a new feature.”
http://www.exp-platform.com/Documents/GuideControlledExperiments.pdf
http://elem.com/~btilly/effective-ab-testing/ / http://elem.com/~btilly/effective-ab-testing/g-test-
calculator.html
http://cdn.oreillystatic.com/en/assets/1/event/61/Building%20an%20A_B%20Testing%20Framework
%20for%20Web%20Applications%20Presentation.pdf
http://cdn.oreillystatic.com/en/assets/1/event/61/Building%20an%20A_B%20Testing%20Framework
%20for%20Web%20Applications%20Presentation%201.pdf
 A/B Framework
http://fr.slideshare.net/patio11/ab-testing-framework-design-3296257
https://github.com/etsy/ab
 Tool
http://abtester.com/calculator/
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UN PETIT SITE POUR RETROUVER CE COURS
 https://sites.google.com/site/iutbobignyweb/
 Support de formation
 Liens utiles
 Coordonnées
 Formulaire d’évaluation
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Module 3 IUT Bobigny : Infrastructure et Opérations

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