Benjamín Barán
National University of Asuncion (UNA)
Paraguay
Data
Cente
ropti
miz
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on
for
Clo
ud C
om
puti
ng
Conte
nt
2
�C
lou
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om
pu
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�C
om
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rin
gs
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asic
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m F
orm
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n
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pen
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�C
on
clu
sio
ns
Clo
ud C
om
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ng
3
Cloud
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gis
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tern
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dco
mp
uti
ng
inw
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hla
rge
gro
up
so
fre
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ote
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ers
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net
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rked
toal
low
shar
ing
of
dat
ap
ro-
cess
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s,ce
n-
tral
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dat
ast
ora
-ge
and
on
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eac
cess
toco
mp
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rse
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ces
or
reso
urc
es.
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1-
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atca
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wit
hm
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Nat
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lIns
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Sta
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d Tec
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IST
)
Clo
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om
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ng
5
�T
he
very
def
init
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of
clou
dco
mput
ing
still
rem
ain
sco
ntr
ove
rsia
l.
�T
her
ear
eal
tern
ativ
ed
efin
itio
nas
the
follo
win
go
ne:
Cloud
Com
put
ing
isthedynamicprovisioningofIT
capabilities
(hardware,software,orservices)fromthirdpartiesoveranetwork.
�C
lou
d c
om
pu
tin
g is
a com
put
ing
mod
el, n
ot
a te
chn
olo
gy. I
n t
his
m
od
el o
f co
mp
uti
ng,
al
l el
emen
ts (
pro
cess
ing,
sto
rage
, etc
.)
rela
ted
to
Dat
aCen
ters
are
mad
e av
aila
ble
to
en
d u
sers
via
th
e In
tern
et.
�V
irtu
aliz
atio
n-
as w
ell
as t
he
clo
ud
co
mp
uti
ng
mo
del
wit
hin
w
hic
h i
t o
ften
ru
ns
-an
swer
s m
uch
of
Dat
aCen
ters
nee
ds.
[http://
www.com
put
erwor
ld.com
/article/2
527305/c
loud
-com
put
ing/
clou
d-co
mput
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defin
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tml]
NIS
TServ
ice M
odels
6
Nat
iona
lIns
titut
eof
Sta
ndar
san
d Tec
hnolog
y
Every
thin
g/A
nyth
ing a
s a
Serv
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sin
ess
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cess
as
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rvic
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aaS
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om
mu
nic
atio
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rvic
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form
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logy
) as
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ervi
ce
•P
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-P
latf
orm
as
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rvic
e
•R
aaS
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eso
urc
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s a
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war
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uri
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rvic
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, pro
vid
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rid
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ust
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esig
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au
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r re
pla
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he
fun
ctio
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of
an e
nti
re D
ataC
ente
r.
Th
e h
igh
est-
pro
file
exam
ple
is Am
azon
's E
lastic
Com
pute
Cloud[EC2]
and
Sim
ple
Stor
age
Serv
ice [S3]
, b
ut
oth
er t
rad
itio
nal
IT
ven
do
rs a
re a
lso
offe
rin
g se
rvic
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Am
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erv
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10
Case S
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Com
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sin
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12
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13
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: fix
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s (r
eso
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s ra
rely
ch
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in
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e, a
s tr
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sour
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w A
maz
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14
1 y
ear
Pri
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xam
ple
INSTANCE
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tin
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nit
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U =
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tern
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16
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17
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BC
AG
R…
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mp
ou
nd
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nu
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row
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ate
Vir
tualizati
on
19
Vir
tualizati
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20
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Vir
tualizati
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xam
ple
: VM
ware
21
DR
S…
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trib
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igh
Ava
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ymm
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last
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ver
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tual
Mac
hin
e Fi
le S
yste
m
Basic
Pro
ble
m F
orm
ula
tion
22
Vir
tual M
achin
e P
lacem
ent
23
Virtual Infrastructure
Wh
ich
vir
tual
mac
hin
es s
ho
uld
be
loca
ted
at
each
phy
sica
l m
ach
ine?
Und
er w
hich
crite
ria?
Obje
cti
ve F
uncti
ons
�M
ain
ob
ject
ive
fun
ctio
ns
[3]
[F.
Ló
pez
Pir
es,
B.
Bar
án,
“Tax
onom
yof
Optim
alVirtu
alM
achine
Plac
emen
tin
Efficient
Dat
acen
ters
,”IE
EE
Ara
nd
uco
n’
20
12
]
(1)
En
ergy
Co
nsu
mp
tio
n M
inim
izat
ion
(2)
Eco
no
mic
al R
even
ue
Max
imiz
atio
n
(3)
Net
wo
rk T
raff
ic M
inim
izat
ion
�M
ath
emat
ical
fo
rmu
lati
on
wit
ho
ut
SLA
[4]
[F.
Ló
pez
Pir
es,
B.
Bar
án,
“Multi-
Objec
tive
Virtu
alM
achine
Plac
emen
twith
Serv
ice
Leve
lAgr
eem
ent,”
6th
IEE
E/A
CM
Inte
rnat
ion
alC
on
fere
nce
on
Uti
lity
and
Clo
ud
Co
mp
uti
ng,
UC
C’2
01
3.
Dre
sden
–A
lem
ania
]
24
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al R
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sica
l mac
hin
e � �
; 0 o
ther
wis
e
��� �
:Se
rvic
e L
evel
Agr
eem
ent ��� �
= 1
if � �
is c
riti
cal,
or
0 o
ther
wis
e
;) ���1∀�6���8�8�����1
� �>�
Constr
ain
ts
32
�R
eso
urc
e ca
pac
ity
of
phy
sica
l m
ach
ines
;�����') ��<�����
�>�
;���') ��<��
� �>� ;�����') ��<�����
�>�
Constraint 3
Constraint 4
Constraint 5
wh
ere: �����
:P
roce
ssin
g re
qu
irem
ent
[MIP
S] o
f v
irtu
al m
ach
ine� �
���
: R
AM
mem
ory
req
uir
emen
t [M
B]
of
vir
tual
mac
hin
e� �
�����
: S
tora
ge r
equ
irem
ent
[GB
] o
f v
irtu
al m
ach
ine� �
Mult
i-O
bje
cti
ve M
em
eti
c A
lgori
thm
33
�C
hro
mo
som
e re
pre
sen
tati
on
Solutio
n?�
100
100
100
010
010
010
010
001
001
Proposed Form
ulation
Proposed Chromosome Representation
Mult
i-O
bje
cti
ve M
em
eti
c A
lgori
thm
34
Initialization
Reparation
Local Search
Population Evolution
Pareto Set
Stop
Criteria?
No
Yes
Crossover and Mutation
Reparation
Local Search
Pareto Set Update
Selection
Experi
menta
l R
esult
s
35
�Te
stin
g E
nvir
on
men
t
�A
lgo
rith
ms
in A
NSI
C (
GN
U C
)
�G
NU
/Lin
ux
Ub
un
tu 1
1.1
0 O
per
atin
g Sy
stem
�In
tel
Co
re i
7 d
e 1
.2 G
Hz
Pro
cess
or
�8
GB
of
RA
M M
emo
ry
�R
eal
Inp
ut
Dat
a
�
Experi
menta
l R
esult
s
36
�E
xp
erim
enta
l Tes
t 1
:
�E
xh
aust
ive
sear
chal
gori
thm
can
no
tco
mp
lete
calc
ula
tio
nin
use
fult
ime
.
�It
isn
eces
sary
toim
ple
men
tal
tern
ativ
esto
exh
aust
ive
sear
ch.
@ ABCDB:
Kn
ow
n P
aret
o F
ron
tE ABCDB:
Kn
ow
n P
aret
o S
et
Scenario
Number
of
Physical
Machines
Number
of
Virtual
Machines
Critical SLA
Percentage
Number
of@ABCDB
Elements
Number
ofEABCDB
Elements
10
x2
01
02
05
0%
48
48
Experi
menta
l R
esult
s
37
�E
xp
erim
enta
l Tes
t 2
:
�R
elat
ion
of
vari
able
s:
�Execution Time
and
Critical SLA Percentage
�Number of Solutions
and
Critical SLA Percentage
Scenario
Number of
Physical Machines
Number of
Virtual Machines
Critical SLA
Percentage
3x
53
50
, 10
, 20
, 30
, 40
, 50
, 6
0, 7
0, 8
0,9
0, 1
00
%
4x
10
41
00
, 10
, 20
, 30
, 40
, 50
, 6
0, 7
0, 8
0,9
0, 1
00
%
Futu
re W
ork
38
�A
lter
nat
ive
form
ula
tio
ns
for
the
pro
ble
m:
�C
on
sid
erin
gm
ore
SLA
leve
lsan
dco
nst
rain
s(a
sge
ogr
aph
ical
)
�C
on
sid
erin
gm
ore
SLA
met
rics
:res
pons
etim
e,jitte
r,et
c.
�Fo
rmu
lati
on
wit
ho
ther
ob
ject
ive
fun
ctio
ns
(mor
eth
an80
differ
entob
ject
ive
func
tions
wer
efo
und
inth
esp
ecializ
edliter
atur
e).
�Te
stin
go
ther
bio
-in
spir
edm
eta-
heu
rist
ic,
give
nth
en
ove
lty
of
the
pro
po
sed
con
tex
t.
�P
ure
Dyn
amic
alC
on
tex
tan
dit
su
nce
rtai
nty
.
�U
seo
fa
thir
d-p
arty
Bro
ker.
�C
on
sid
erH
ybri
dcl
ou
ds.
�C
ase
stu
die
san
dco
mm
erci
alap
plic
atio
ns.
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