This document provides an introduction to cloud computing and big data. It defines cloud computing as a model for providing scalable computing resources over the internet with minimal management. The key characteristics of cloud computing include on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service. Dynamic provisioning allows cloud resources to scale up and down based on demand. This helps solve the problems of underutilization and overload in traditional systems with static capacity. The document also discusses how dynamic provisioning can be used in multi-tier web applications running on cloud infrastructure.

1. Introduc)on
to
Cloud
Compu)ng
and
Big
Data
Waheed
Iqbal,
Ph.D
h<p://www.waheediqbal.info
12th
May,
2015

2. Google
Trends:
Cloud
Compu)ng,
Big
Data,
and
Distributed
Systems

4. Cloud
Compu)ng
Defini)on
Cloud
compu)ng
is
a
model
to
provide
scalable
resources
(network,
storage,
applica)ons,
services,
compu)ng
power
etc.)
over
the
Internet
with
minimal
management
efforts.

5. Cloud
Compu)ng
Defini)on
(Cont.)
Na)onal
Ins)tute
of
Standards
and
Technology
(NIST)
has
published
16th
draS
of
Cloud
Compu)ng
defini)on.
Cloud
Compu)ng
model
is
composed
of
following
five
essen)al
characteris)cs:
1. On-­‐demand
self
service
(get
resources/services
without
human
interven)on)
2. Broad
network
access
(accessible
using
mobile,
laptop,
tablets,
and
worksta)ons)
3. Resource
pooling
(different
physical
and
virtual
resources
dynamically
assigned
and
reassigned
according
to
consumer
demand)
4. Rapid
elas)city
(shrink
and
grow
capabili)es)
5. Measured
services
(resource
usage
monitor,
control,
and
report
transparently)

6. Cloud
Compu)ng
Ecosystem
Deployment
Models
Service
Models

7. Cloud
Ecosystem
(Cont.)
Source:
h<p://blog.ascens-­‐ist.eu/2011/03/dreaming-­‐of-­‐fluffy-­‐clouds/

8. Dynamic
Provisioning
Lets
discuss
more
about
the
most
important
characteris)c
(Rapid
Elas)city/Dynamic
Provisioning)
of
Cloud
Compu)ng!

9. Dynamic
Provisioning
(Cont.)
• In
tradi)onal
compu)ng
model,
two
common
problems
:
1.
Underes)mate
system
u)liza)on
which
result
in
under
provision
Resources
Demand
Capacity
1 2 3
Resources
Demand
Capacity
1 2 3
Resources
Demand
Capacity
Time
(days)
1 2 3
Loss
Users
Loss
Revenue

10. Dynamic
Provisioning
(Cont.)
2.
Overes)mate
system
u)liza)on
which
result
in
low
u)liza)on
#
• How
to
solve
this
problem
??
– Dynamically
provision
resources
Unused
resources
Demand
Capacity
Time
Resources

11. Dynamic
Provisioning
(Cont.)
• Cloud
resources
should
be
provisioned
dynamically
– Meet
seasonal
demand
varia)ons
– Meet
demand
varia)ons
between
different
industries
– Meet
burst
demand
for
some
extraordinary
events
Demand
Capacity
Time
Resources
Demand
Capacity
Time
Resources

12. Mul)-­‐)er
Web
Applica)on
Lets
discuss
a
case
using
dynamic
provisioning
in
mul)-­‐)er
web
applica)ons!

13. Mul)-­‐)er
Web
Applica)on
(Cont.)
• Single-­‐)er
web
applica)on:
consists
only
web
server
mostly
to
serve
sta)c
pages
and
dynamic
pages
without
database
interac)on
• Mul)-­‐)er
web
applica)on:
consists
on
Web
server,
DB
server,
Applica)on
server,
Batch
job
processors
etc
• A
single
)er
resource
management
is
easy
comparing
to
mul)-­‐)er
applica)on!

14. Mul)-­‐)er
Web
Applica)on
(Cont.)
Network
Web
Server
Database
Server

15. Mul)-­‐)er
Web
Applica)on
(Cont.)
Network
Web
Server
Database
Server

16. Mul)-­‐)er
Web
Applica)on
(Cont.)
Network
Web
Server
Database
Server
0
100
200
300
400
500
600
700
800
900
0
20
40
60
80
Response
Time
(ms)
Number
of
Users/Request

17. Mul)-­‐)er
Web
Applica)on
(Cont.)
Network
Web
Server
Database
Server
0
100
200
300
400
500
600
700
800
900
0
20
40
60
80
Response
Time
(ms)
Number
of
Users/Request

18. Mul)-­‐)er
Web
Applica)on
(Cont.)
Load
balancing
helps
to
maintain
performance!

19. Mul)-­‐)er
Web
Applica)on
(Cont.)
Network
Web
Server
Database
Server
Load
Balancer

20. Mul)-­‐)er
Web
Applica)on
(Cont.)
Network
Web
Server
Database
Server

21. Mul)-­‐)er
Web
Applica)on
(Cont.)
Network
Web
Server
Database
Server
Load
Balancer

22. Cloud
Datacenter
Internet

23. Cloud
Compu)ng:
Take
Home
Message
Source:
Introduc)on
to
Amazon
Web
Services
by
Jeff
Barr,
Senior
Web
Services
Evangelist

25. Data
Growth
• Google
(as
of
around
2009)
processes
around
24
petabytes
of
data
every
day
• This
is
quite
a
lot,
how
much?
Lets
try
to
visualize
the
scale
of
data!

26. Let's
imagine
that
a
single
byte
is
represented
by
a
single
grain
of
rice
1K
or
1024
bytes
would
a
bowl
of
rice

27. 1
MB
1
GB
2
containers
1
TB
2048
Shipping
Containers

28. The
Model
Has
Changed…
The
Model
of
Genera)ng/Consuming
Data
has
Changed
Old
Model:
Few
companies
are
genera)ng
data,
all
others
are
consuming
data
New
Model:
all
of
us
are
genera)ng
data,
and
all
of
us
are
consuming
data

29. Big
Data
Defini)on
No
single
standard
defini)on!
“Big
Data
is
high
volume,
high
velocity,
and/or
high
variety
informa7on
assets
that
require
new
forms
of
processing
to
enable
enhanced
decision
making,
insight
discovery
and
process
op7miza7on.”
(Gartner)
“Big
Data
is
a
data
that
is
difficult
to
store
and
process
using
tradi7onal
techniques
on
commodity
hardware
to
analyse
and
extract
knowledge.”
(Waheed)

30. Who’s
Genera)ng
Big
Data
Social
media
and
networks
(all
of
us
are
genera)ng
data)
ScienJfic
instruments
(collec)ng
all
sorts
of
data)
Mobile
devices
(tracking
all
objects
all
the
)me)
Sensor
technology
and
networks
(measuring
all
kinds
of
data)

31. Big
Data:
3V’s
(Model
for
Describing
Big
Data)

32. Type
of
Data
• Rela)onal
Data
(Tables/Transac)on/Legacy
Data)
• Unstructured
Data
/
Text
Data
(Web,
Applica)on/Server
Logs)
• Semi-­‐structured
Data
(XML)
• Graph
Data
– Social
Network
• Streaming
Data
– You
can
only
scan
the
data
once

33. Big
Data
and
Cloud
Compu)ng
Technologies

34. Real-­‐)me
Tweeter
Analy)cs:
An
Example

35. Ques)ons
and
Discussion

36. Acknowledgment
• Some
of
the
material
used
are
copied
from:
– Lecture
Notes
on
Introduc)on
to
Cloud
Compu)ng
– Introductory
slides
of
course
CS525
Large-­‐Scale
Data
Management
by
Dr.
Mohamed
Eltabakh
– Big-­‐Data
Tutotrial
by
Marko
Grobelnik
– Big-­‐Data
Lecture
Slides
by
Ruoming
Jin's
– What
is
cloud
compu7ng
by
Read
Maloney,
Product
Manger,
Amazon
Web
Services
– Most
of
the
images
used
in
this
presenta)on
are
taken
from
the
Internet