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Benjamín Barán
National University of Asuncion (UNA)
Paraguay
Data
Cente
ropti
miz
ati
on
for
Clo
ud C
om
puti
ng
Conte
nt
2
�C
lou
d C
om
pu
tin
g
�C
om
mer
cial
Offe
rin
gs
�B
asic
Pro
ble
m F
orm
ula
tio
n
�O
pen
Res
earc
h
�C
on
clu
sio
ns
Clo
ud C
om
puti
ng
3
Cloud
com
putin
gis
anIn
tern
et-
base
dco
mp
uti
ng
inw
hic
hla
rge
gro
up
so
fre
-m
ote
serv
ers
are
net
wo
rked
toal
low
shar
ing
of
dat
ap
ro-
cess
ing
task
s,ce
n-
tral
ized
dat
ast
ora
-ge
and
on
-lin
eac
cess
toco
mp
ute
rse
rvi-
ces
or
reso
urc
es.
[htt
p:/
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iped
ia.o
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iki/
Clo
ud
_co
mp
uti
ng]
1-
Pu
blic
Clo
ud
2-
Pri
vate
Clo
ud
3-
Hyb
rid
Clo
ud
NIS
Tdefi
nit
ion o
f C
loud C
om
puti
ng
4Cloud
com
putin
gis
am
od
elfo
ren
ablin
gu
biq
uit
ou
s,co
n-
ven
ien
t,o
n-d
eman
dn
etw
ork
acce
ssto
ash
ared
po
ol
of
con
-fig
ura
ble
com
pu
tin
gre
sou
rces
(as,
netw
orks
,se
rver
s,stor
age,
applic
ations
,and
serv
ices
)th
atca
nb
era
pid
lyp
rovi
sio
ned
and
rele
ased
wit
hm
inim
alm
ana-
gem
ent
effo
rto
rse
rvic
ep
ro-
vid
erin
tera
ctio
n.
Th
isclou
dm
odel
isco
mp
ose
do
f5
esse
nti
alch
arac
teri
stic
s,an
d3
serv
ice
mo
del
s.
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Nat
iona
lIns
titut
eof
Sta
ndar
san
d Tec
hnolog
y(N
IST
)
Clo
ud C
om
puti
ng
5
�T
he
very
def
init
ion
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
ing-
defin
ition
s-an
d-so
lutio
ns.h
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
ice -XaaS
7•B
Paa
S-
Bu
sin
ess
Pro
cess
as
a Se
rvic
e
•C
aaS
-C
om
mu
nic
atio
n a
s a
Serv
ice
•D
aaS
-D
ata
as a
Ser
vice
•Ia
aS-
Infr
astr
uct
ure
as
a Se
rvic
e
•IT
aaS
-IT
(In
form
ation Te
chno
logy
) as
a S
ervi
ce
•P
aaS
-P
latf
orm
as
a Se
rvic
e
•R
aaS
–R
eso
urc
es a
s a
Serv
ice
•Sa
aS-
Soft
war
e as
a S
ervi
ce
•SE
Caa
S-
SEC
uri
tyas
a S
ervi
ce
Infr
aestr
uctu
re a
s a
Serv
ice -
IaaS
8Infr
astr
uct
ure
as
a Se
rvic
e –
IaaS
, pro
vid
es g
rid
s o
r cl
ust
ers
or
virt
ual
ized
ser
vers
, net
wo
rks,
sto
rage
an
d
syst
ems
soft
war
e d
esig
ned
to
au
gmen
t o
r re
pla
ce t
he
fun
ctio
ns
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
es.
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p:/
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om
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terw
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icle
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lou
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om
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tin
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and
-so
luti
on
s.h
tml]
IaaSGartner
Magic
Quadra
nt
9
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azo
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om
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AW
S –
Am
azon W
eb S
erv
ices
10
Case S
tudy: using A
WS
11
Com
panie
s u
sin
g
Public Cloud Computing
12
[htt
p:/
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ectr
um
.ieee
.org
/co
mp
uti
ng/
net
wo
rks/
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pe-
fro
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rce=
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ium
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ail&
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aign
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51
4]
Cost
Models
13
�Static
: fix
ed p
rice
s (r
eso
urc
e p
rice
s ra
rely
ch
ange
in
tim
e, a
s tr
adit
ion
al A
maz
on
EC
2)
�Dynamic Prices. R
eso
rce
pri
ces
fluct
uat
es o
n d
eman
d
on
a d
ay o
r w
eekl
y b
asis
(e.
g., w
eeke
nd p
rice
s ar
e differ
ent)
.
�Spot Prices. I
t is
bas
ed o
n u
ser´
s b
ids.
If u
ser bid
met
or ex
ceed
the
cur
rent
spo
t pr
ice, h
e ga
ins
acce
ss to
requ
este
d re
sour
ces (a
s ne
w A
maz
on E
C2).
14
1 y
ear
Pri
ces E
xam
ple
INSTANCE
CPU
ECU
RAM [GiB]
Storage
[GB]
Price per
hour
t2.micro
1V
aria
ble
1E
BS
$0
.01
3
t2.small
1V
aria
ble
2E
BS
$0
.02
6
t2.medium
2V
aria
ble
4E
BS
$0
.05
2
m3.medium
13
3.7
51
x 4
SSD
$0
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0
m3.large
26
.57
.51
x 3
2 S
SD$
0.1
40
m3.xlarge
41
31
52
x 4
0 S
SD$
0.2
80
m3.2xlarge
82
63
02
x 8
0 S
SD$
0.5
60
EC
U …
EC
2C
om
pu
tin
g U
nit
(e
.g. 1
EC
U =
1.0
-1.2
GH
z 2
00
7 X
eon
)
EB
S…
Ela
stic
Blo
ck S
tora
ge (
$0
.10
per
GB
-mo
nth
)
SSD
… S
olid
Sta
te D
rive
, in
tern
al s
tora
ge
15
htt
ps:
//aw
s.am
azo
n.c
om
/mar
ketp
lace
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rch
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ult
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ctio
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9
AMI:
Amazon Machine Images
Pri
ces E
xam
ple
Spot
Pri
ce e
xam
ple
16
SeeTUTORIALSat:
[h
ttp
://a
ws.
amaz
on
.co
m/e
c2/p
urc
has
ing-
op
tio
ns/
spo
t-in
stan
ces/
]
INSTANCE
LINUX
WINDOWS
m1.small
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71
per
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ur
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71
per
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ur
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ur
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per
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ur
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ur
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ur
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52
per
Ho
ur
$0
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21
per
Ho
ur
Clo
ud C
om
puti
ng T
rend
17
htt
p:/
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w.c
isco
.co
m/c
/en
/us/
solu
tio
ns/
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tera
l/se
rvic
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rovi
der
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bal
-clo
ud
-in
dex
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ud
_In
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_W
hit
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aper
.pd
f
18
Clo
ud C
om
puti
ng T
rend
htt
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isco
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tio
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der
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ud
_In
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aper
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f
1 Z
B =
10
21
BC
AG
R…
Co
mp
ou
nd
An
nu
al G
row
th R
ate
Vir
tualizati
on
19
Vir
tualizati
on
20
htt
p:/
/ww
w.g
artn
er.c
om
/rep
rin
ts/v
mw
are-
vol4
?id
=1
-1G
RG
RR
U&
ct=
13
07
02
&st
=sb
Vir
tualizati
on e
xam
ple
: VM
ware
21
DR
S…
Dis
trib
ute
dR
eso
urc
eSc
hed
ule
r
HA
… H
igh
Ava
ilab
ility
SMP
… S
ymm
etri
cM
ult
i-P
roce
ssin
g
ESX
… E
last
icsk
yX
ser
ver
VM
FS…
Vir
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
Physic
al R
esourc
es M
atr
ix
25
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30
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cem
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ual
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Constraint 1
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ere:
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ary
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Constr
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31
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wh
ere:
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phy
sica
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hin
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) ��:
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ary
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able
eq
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if
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ual
mac
hin
e � �
is lo
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o r
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on
the
phy
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.
