It presents an overview on the subject of the cloud computational model.

1. Cloud Computing
Dr. João Paulo Delgado Preti
joao.preti@cba.ifmt.edu.br
Instituto Federal de Mato Grosso (IFMT)
Campus Cuiabá

2.  This presenation is based on conferences, papers and
personal experiments;
 There is a belief about making a lot of money with
the cloud – so there is a lot of hype;
 But there are fundamentals that promote significant
IT changes
Introduction

3. Traditional Server Model
 Benefits
 Easy to conceive
 Easy to deploy
 Easy to do backups
 Virtually any
application/service can be
installed and executed
 Disadvantages
 Hardware expensive to
acquire and maintain
 Very limited scalability
 Hard to replicate
 Redundance is hard to
implement
 Hardware fail vulnerability
 Hardware is commonly
underused

4. Virtual Server Model
 Benefits
 Resource Pool
 High redundancy
 High availability
 New servers are quickly
deployed
 Reconfigurable with
running services
 Optimization of physical
resources
 Disadvantages
 A little more expensive
to conceive (initial cost)
 Need to acquire
hardware, OS, Apps and
na abstraction layer

5. Cloud Computing Interest

6. Why the Cloud?
Size Matters

7.  Provide access to real-time financial market data
 Why pay for computing resources at night or on weekends
Why the Cloud?
Forbes.com
9 AM - 5 PM,
M-F
ALL OTHER
TIMES
Rate of
Server
Accesses
Source: https://www.slideshare.net/GirishShivanna1/qspiders-cloud-computingcloud-server

8.  Provides the system at Amazon's EC2 Elastic
Compute Cloud
 Start servers every day and releases them at night
 Pay $0.10* per server per hour
 * more for servers of higher capacity
 Let Amazon care about the hardware!
Forbes.com Solution

9. Gartner Hype Cycle
Source: http://www.zdnet.com/article/has-microsoft-just-redefined-collaboration-introducing-project-gigjam/

10. Gartner Hype Cycle 2009
Emerging Technologies

11. Concept
 Characteristics
 Self-servisse on demand
 Broad network access
 Resources pool
 Fast elasticity
 Service measures (billing)
 Service Models
 SaaS
 PaaS
 IaaS
 Deployment Modes
 Private
 Community
 Public
 Hybrid

12. Gartner Hype Cycle 2015
Cloud Computing

13. Service Models
Source: https://www.smartfile.com/blog/the-differences-between-iaas-saas-and-paas/

14. Source: http://compress.ru/article.aspx?id=23954

15. Source: http://blog.itil.org/2014/11/kategorie-liste-home/itil/cloud-business-view/

16.  Cost (Systems with different needs)
 Batch processing
 Systems with spikes demands
 Systems with unknown demands
 Agility
 More than scalability - elasticity
 Focus
 Many companies DON´T want to administer systems
Benefits

17.  Amazon (Iaas)
 Microsoft (PaaS & SaaS)
 Google (PaaS & SaaS)
 ...
Big Players

18. Amazon Web Services (AWS)

19. AWS

20. AWS

21. AWS

22. Amazon Data Centers
Source: https://www.turnkeylinux.org/blog/aws-datacenters

23. Amazon EC2 Pricing

24. Amazon S3 Pricing

25. Windows Azure

26. Windows Azure

27. Windows Azure

28. Windows Azure Data Centers
Source: https://mitra.computa.asia/article/msdn-virtual-machine-scale-sets-it-really-about-protecting-your-applications-performance

29. Windows Azure

30. Google App Engine

31. Google App Engine

32. Google App Engine

33. Google Data Centers
Source: https://www.google.com/about/datacenters/inside/locations/index.html

34. Google App Engine
Pricing

35.  PaaS definem diversas restrições que podem
inviabilizar uma aplicação na nuvem
 Ex.: Google App Engine
 Permite somente leitura ao sistema de arquivos
 Python ou Java (sem extensões de C)
 Aplicações não podem criar novas threads
 10MB de limite para request/response
 Quantidade máxima de 1000 registros por consulta
 Deadline de 30 segundos por requisição/resposta
Things we SHOULD know

36.  Quanto tempo
leva para que os
dados na nuvem
se tornem
consistentes?
Things we SHOULD know
Source:
https://www.slideshare.net/annali
u/10-things-you-didnt-know-
about-aws-gae-azure

37.  O quão imprevisível ou variável é a nuvem?
Things we SHOULD know
Runtime for a MapReduce Job
Performance Variance of a MapReduce Job for a 50-node EC2 cluster and a
50-node local cluster Source: https://www.slideshare.net/annaliu/10-things-you-
didnt-know-about-aws-gae-azure

38.  Preço varia no espaço e no tempo
 Preço sob demanda (por hora, GB, Nº Requisições)
 Instâncias reservadas
 Localização (Normalmente mais barato no Leste dos EUA)
 Modelo de precificação similar observados no Azure e GAE
Things we SHOULD know
Source: https://www.slideshare.net/annaliu/10-things-you-didnt-know-about-aws-gae-azure

39.  Maioria das soluções não escalam sozinhas
 Tempo para obter uma nova instância
 Normalmente leva minutos para criar uma instância a partir das
imagens disponíveis no EC2
 Dica para obter instâncias mais rapidamente
 Crie um pool de instâncias antecipadamente e as hiberne
 Não paga pela instância mas paga pelo armazenamento
 Acorde as instâncias quando novas forem necessárias
Things we SHOULD know
Source:
https://www.slideshare.net/ann
aliu/10-things-you-didnt-know-
about-aws-gae-azure

40. Things we SHOULD know

41. Things we SHOULD know
Pesquisa do Projeto Pew Internet and American Life realizado com 999 usuários da Internet que utilizam serviços online
para armazenar informações pessoais. Margem de erro de 3,5%.

42. Summing Up

43. Thanks!
Dr. João Paulo Delgado Preti
joao.preti@cba.ifmt.edu.br
Instituto Federal de Mato Grosso
Campus Cuiabá
IFMT
Presentation file is available at:
http://preti.compdevbooks.com