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i
s
p
ap
er
,
w
e
p
r
o
p
o
s
e
an
o
p
ti
m
al
p
lace
m
e
n
t
al
g
o
r
ith
m
f
o
r
v
ir
t
u
al
m
ac
h
i
n
e
s
b
ased
o
n
en
er
g
y
-
a
w
ar
e
ad
ap
tiv
e
Fo
u
r
-
T
h
r
esh
o
ld
s
tech
n
iq
u
e
f
o
r
r
ed
u
cin
g
e
n
e
r
g
y
co
n
s
u
m
p
tio
n
an
d
m
i
n
i
m
izin
g
s
er
v
ice
le
v
el
v
io
latio
n
s
in
clo
u
d
d
atac
en
te
r
s
,
an
d
W
e
v
er
if
y
th
e
e
f
f
ec
t
iv
en
e
s
s
o
f
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
s
u
s
in
g
t
h
e
C
lo
u
d
Si
m
to
o
lk
i
t.
T
h
e
m
ai
n
p
ar
ts
o
f
t
h
e
p
ap
er
ar
e
s
u
m
m
ar
ized
as f
o
llo
w
s
:
a.
I
n
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
o
f
o
p
ti
m
izi
n
g
th
e
v
ir
tu
a
l
m
ac
h
i
n
e
p
lace
m
en
t
al
g
o
r
ith
m
b
ased
o
n
th
e
E
n
er
g
y
-
Aw
ar
e
ad
ap
tiv
e
f
o
u
r
th
r
es
h
o
ld
s
,
h
o
s
ts
in
d
ata
ce
n
ter
ar
e
class
i
f
ied
in
to
f
i
v
e
ca
teg
o
r
ies
a
cc
o
r
d
in
g
to
th
eir
lo
ad
.
Vir
tu
al
m
ac
h
i
n
es
ar
e
tr
an
s
f
er
r
ed
f
r
o
m
th
e
h
o
s
t
w
it
h
h
i
g
h
lo
ad
an
d
h
ea
v
y
lo
ad
to
t
h
e
h
o
s
t
s
w
it
h
li
g
h
t
lo
ad
an
d
tr
an
s
f
er
r
ed
f
r
o
m
h
o
s
t
s
w
ith
lo
w
lo
ad
to
th
e
h
o
s
ts
w
i
th
m
id
d
le
lo
ad
,
w
h
ile
Vir
t
u
al
Ma
ch
i
n
es i
n
th
e
h
o
s
t
w
it
h
li
g
h
t lo
ad
an
d
m
o
d
er
ate
lo
ad
r
em
ai
n
s
u
n
c
h
a
n
g
ed
.
b.
P
r
esen
tin
g
an
A
d
ap
ti
v
e
Fo
u
r
-
T
h
r
esh
o
ld
A
l
g
o
r
ith
m
to
Dete
r
m
i
n
e
th
e
Fo
u
r
T
h
r
esh
o
ld
s
.
c.
T
h
e
u
s
e
o
f
a
v
ir
t
u
al
d
ev
ice
s
el
ec
tio
n
ap
p
r
o
ac
h
an
d
an
allo
ca
tio
n
alg
o
r
it
h
m
.
d.
E
v
alu
a
tin
g
p
r
o
p
o
s
ed
alg
o
r
ith
m
s
w
i
th
e
x
te
n
s
i
v
e
s
i
m
u
latio
n
u
s
i
n
g
th
e
C
lo
u
d
Si
m
to
o
l.
T
h
e
r
est
o
f
th
is
p
ap
er
is
o
r
g
an
ized
as
f
o
llo
w
s
.
I
n
Sectio
n
2
,
th
e
r
elate
d
w
o
r
k
is
d
is
c
u
s
s
ed
.
Sectio
n
3
p
r
esen
ts
th
e
p
o
w
er
m
o
d
el,
t
h
e
tr
an
s
f
er
co
s
t
o
f
VM
m
i
g
r
ati
o
n
,
S
L
A
v
io
latio
n
m
etr
ics,
a
n
d
en
er
g
y
e
f
f
icie
n
c
y
m
etr
ics.
Sec
tio
n
4
p
r
o
p
o
s
es
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
,
th
e
f
o
u
r
-
s
tep
ap
p
r
o
ac
h
alg
o
r
ith
m
,
th
e
VM
s
elec
tio
n
ap
p
r
o
ac
h
,
an
d
th
e
VM
d
ep
lo
y
m
e
n
t
a
lg
o
r
it
h
m
.
E
x
p
er
i
m
e
n
t
s
an
d
p
er
f
o
r
m
a
n
ce
e
v
al
u
atio
n
ar
e
p
r
esen
ted
i
n
Sectio
n
5
.
Sectio
n
6
p
r
o
v
id
es th
e
co
n
cl
u
s
io
n
s
o
f
t
h
e
p
ap
er
.
2.
RE
L
AT
E
D
WO
RK
S
T
h
er
e
is
cu
r
r
en
tl
y
a
lo
t
o
f
r
es
ea
r
ch
th
at
f
o
cu
s
e
s
o
n
m
an
a
g
i
n
g
e
n
er
g
y
e
f
f
icie
n
c
y
r
eso
u
r
ce
s
in
clo
u
d
d
ata
ce
n
ter
s
.
Z
h
o
u
Z
h
o
u
an
d
et
a
l
.,
p
r
esen
ted
an
ad
ap
tiv
e
th
r
ee
-
th
r
e
s
h
o
ld
e
n
er
g
y
a
w
a
r
en
ess
alg
o
r
it
h
m
i
n
2
0
1
6
.
T
h
e
p
u
r
p
o
s
e
o
f
p
r
o
v
id
i
n
g
th
i
s
al
g
o
r
ith
m
i
s
to
p
r
o
p
er
l
y
ac
co
m
m
o
d
ate
a
v
ir
t
u
al
d
e
v
i
ce
o
n
a
d
ata
c
en
ter
b
y
r
ed
u
ci
n
g
t
h
e
le
v
el
o
f
s
er
v
ice
-
le
v
el
v
io
latio
n
[
7
]
.
Nid
a
J
in
et
a
l
.,
p
r
ese
n
ted
t
h
e
Fire
f
l
y
al
g
o
r
ith
m
FF
O
-
E
E
VM
in
2
0
1
6
.
T
h
is
al
g
o
r
ith
m
is
p
r
o
v
id
ed
f
o
r
en
er
g
y
o
p
ti
m
izatio
n
i
n
d
ata
ce
n
ter
s
a
n
d
v
ir
t
u
al
m
ac
h
i
n
e
m
i
g
r
atio
n
w
i
th
e
n
er
g
y
co
n
s
ci
o
u
s
n
e
s
s
[
8
].
Y
an
g
Qia
n
g
a
n
d
et
a
l
.
,
(
2
0
1
3
)
in
tr
o
d
u
ce
d
a
m
u
lti
-
o
b
j
ec
tiv
e
an
io
n
clo
n
e
s
y
s
te
m
al
g
o
r
ith
m
f
o
r
clo
u
d
co
m
p
u
ti
n
g
v
ir
t
u
al
m
ac
h
i
n
es
ca
lled
VM
P
A
C
S.
T
h
e
g
o
al
o
f
th
i
s
al
g
o
r
ith
m
w
a
s
to
i
m
p
r
o
v
e
t
h
e
p
o
w
er
ef
f
i
cien
c
y
an
d
r
eso
u
r
ce
u
tili
za
tio
n
i
n
a
clo
u
d
co
m
p
u
ti
n
g
e
n
v
ir
o
n
m
e
n
t
b
y
f
itti
n
g
th
e
v
ir
tu
a
l
m
ac
h
in
e
s
in
t
h
e
d
ata
ce
n
ter
s
[
9
]
.
Kar
ab
o
g
a
an
d
et
a
l
.,
(
2011
)
an
d
Geo
r
g
e
an
d
e
t
a
l
.,
(
2
0
1
3
)
d
id
a
r
esear
ch
o
n
co
lo
n
y
b
ee
alg
o
r
it
h
m
s
ca
lled
A
B
C
.
B
ee
b
ased
al
g
o
r
ith
m
s
ar
e
m
o
d
eled
o
n
th
e
b
eh
av
io
r
o
f
b
ee
s
i
n
th
e
h
iv
e
o
r
o
u
ts
id
e
it
,
e
s
p
ec
i
all
y
t
h
eir
b
eh
av
io
r
in
f
in
d
i
n
g
th
e
s
o
u
r
ce
o
f
f
o
o
d
.
T
h
e
A
B
C
alg
o
r
it
h
m
a
m
o
n
g
o
th
er
alg
o
r
it
h
m
s
h
as
t
h
e
b
est
p
er
f
o
r
m
an
ce
i
n
ter
m
s
o
f
f
i
n
d
in
g
t
h
e
r
i
g
h
t
r
esp
o
n
s
e
a
n
d
s
p
ee
d
,
an
d
is
also
s
u
itab
le
f
o
r
s
o
lv
i
n
g
co
m
p
lex
p
r
o
b
lem
s
[
10
]
.
Ma
n
s
u
r
M
u
r
s
h
a
d
et
a
l
., (
2014
)
o
f
f
er
ed
th
e
A
V
VM
C
al
g
o
r
it
h
m
to
b
alan
ce
r
eso
u
r
ce
s
ac
r
o
s
s
s
er
v
er
s
w
i
th
v
ar
io
u
s
co
m
p
u
tin
g
r
e
s
o
u
r
ce
s
s
u
c
h
as
m
e
m
o
r
y
,
p
r
o
ce
s
s
o
r
an
d
n
et
w
o
r
k
o
u
tlet
i
n
o
r
d
er
to
m
i
n
i
m
ize
p
o
w
er
co
n
s
u
m
p
tio
n
.
T
h
is
m
e
th
o
d
p
r
o
d
u
ce
s
a
co
m
p
le
x
s
o
l
u
tio
n
f
o
r
co
m
p
lex
p
r
o
b
lem
s
li
k
e
b
in
-
pa
c
k
i
n
g
an
d
p
r
o
d
u
ce
s
an
o
p
tim
al
s
o
l
u
tio
n
f
o
r
r
eg
u
lar
p
ath
s
[
11
]
.
B
o
y
a
et
a
l
.,
Pro
v
id
ed
a
VM
(
s
i
n
g
le
-
t
h
r
es
h
o
ld
)
(
ST
)
d
ep
lo
y
m
e
n
t
al
g
o
r
ith
m
b
a
s
ed
o
n
a
co
m
b
i
n
atio
n
o
f
VM
c
h
o
ices
.
T
h
e
ST
alg
o
r
ith
m
ad
j
u
s
ts
th
e
s
a
m
e
v
al
u
e
f
o
r
s
er
v
er
C
P
U
u
t
ilizatio
n
to
en
s
u
r
e
th
at
a
ll
Ser
v
er
s
ar
e
b
elo
w
th
is
v
alu
e.
I
t
i
s
k
n
o
w
n
th
at
t
h
e
ST
alg
o
r
ith
m
ca
n
s
a
v
e
en
er
g
y
an
d
r
ed
u
ce
t
h
e
a
g
g
r
e
s
s
io
n
o
f
th
e
S
L
A
,
b
u
t
t
h
e
a
g
g
r
e
s
s
io
n
r
e
m
ai
n
s
h
i
g
h
[
12
]
.
B
elo
g
lazo
v
an
d
B
o
y
a
p
r
o
v
id
e
an
ef
f
icie
n
t
en
er
g
y
r
eso
u
r
ce
m
a
n
ag
e
m
e
n
t
s
y
s
te
m
th
at
i
n
clu
d
e
s
th
e
d
is
t
r
ib
u
to
r
,
g
lo
b
al
m
a
n
a
g
er
,
lo
ca
l
m
a
n
ag
er
a
n
d
v
ir
t
u
al
m
ac
h
i
n
e
m
o
n
ito
r
(
VM
M)
.
B
elo
g
laz
o
v
et
a
l
.,
co
n
s
id
er
a
n
e
w
DT
(
d
o
u
b
le
th
r
esh
o
ld
)
VM
alg
o
r
ith
m
to
i
m
p
r
o
v
e
en
er
g
y
e
f
f
icien
c
y
.
DT
test
s
th
e
t
wo
th
r
esh
o
ld
s
s
o
th
at
th
e
C
P
U
u
tili
ze
s
all
t
h
e
h
o
s
ts
b
et
w
ee
n
th
e
t
w
o
th
r
es
h
o
ld
s
,
al
th
o
u
g
h
en
er
g
y
co
n
s
u
m
p
tio
n
a
n
d
S
L
A
ag
g
r
es
s
io
n
f
o
r
th
e
DT
alg
o
r
ith
m
s
h
o
u
ld
b
e
r
ed
u
ce
d
to
a
g
r
ea
ter
d
eg
r
e
e.
P
r
io
r
to
th
at,
B
elo
g
lazo
v
a
n
d
B
o
y
a
p
r
o
p
o
s
ed
a
d
o
u
b
le
th
r
es
h
o
ld
ad
ap
tiv
e
V
M
p
lace
m
e
n
t
al
g
o
r
ith
m
to
i
m
p
r
o
v
e
en
er
g
y
e
f
f
ic
ien
c
y
i
n
d
a
ta
ce
n
ter
s
.
Ho
w
e
v
er
,
en
er
g
y
co
n
s
u
m
p
tio
n
i
n
d
atac
en
ter
s
r
e
m
a
in
s
h
i
g
h
[
13
].
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
8
,
No
.
5
,
Octo
b
er
2
0
1
8
:
3
8
90
–
3
9
0
1
3892
3.
P
O
WE
R
M
O
DE
L
,
CO
ST
O
F
VM
M
I
G
RAT
I
O
N,
SL
A
VIOLA
T
I
O
N
M
E
T
R
I
CS A
ND
E
N
E
R
G
Y
E
F
F
I
CI
E
NC
Y
M
E
T
RICS
3
.
1
.
P
o
w
er
m
o
del
E
n
er
g
y
co
n
s
u
m
p
tio
n
b
y
s
er
v
er
s
in
d
ata
ce
n
ter
s
is
r
elate
d
to
C
P
U,
m
e
m
o
r
y
,
d
is
k
,
an
d
b
an
d
w
id
t
h
.
R
ec
en
t
s
tu
d
ie
s
[
7
]
h
av
e
s
h
o
w
n
th
at,
ev
en
i
f
th
e
DV
FS
m
e
th
o
d
is
u
s
ed
,
th
e
en
er
g
y
co
n
s
u
m
p
tio
n
b
y
s
er
v
er
s
h
as
a
lin
ea
r
r
elat
io
n
s
h
ip
w
i
th
its
C
P
U
u
tili
za
tio
n
.
Ho
w
e
v
er
,
w
it
h
th
e
d
ec
r
ea
s
e
o
f
h
ar
d
w
ar
e
p
r
ice,
m
u
ltico
r
e
C
P
U
s
an
d
m
e
m
o
r
y
w
it
h
lar
g
e
-
ca
p
ac
it
y
ar
e
w
id
el
y
eq
u
ip
p
ed
in
s
e
r
v
er
s
,
an
d
ca
u
s
ed
th
e
co
n
v
en
t
io
n
al
li
n
ea
r
m
o
d
el
n
o
t
to
b
e
ab
le
to
ac
cu
r
atel
y
d
eter
m
in
e
t
h
e
e
n
er
g
y
co
n
s
u
m
p
tio
n
o
f
th
e
s
er
v
er
s
.
I
n
o
r
d
er
to
d
ea
l
w
ith
t
h
i
s
p
r
o
b
lem
,
w
e
u
s
e
ac
tu
al
e
n
er
g
y
co
n
s
u
m
p
tio
n
d
ata,
w
h
ich
w
a
s
s
u
g
g
e
s
ted
b
y
SP
E
C
p
o
w
er
b
en
ch
m
ar
k
.
3
.
2
.
VM
m
ig
ra
t
io
n c
o
s
t
P
r
o
p
er
VM
m
i
g
r
atio
n
b
et
w
ee
n
s
er
v
er
s
ca
n
r
ed
u
ce
e
n
er
g
y
co
n
s
u
m
p
tio
n
a
n
d
S
L
A
v
io
lati
o
n
i
n
d
ata
ce
n
ter
s
.
ex
ce
s
s
i
v
e
VM
m
i
g
r
at
io
n
,
o
f
co
u
r
s
e,
ca
n
n
e
g
ati
v
el
y
af
f
ec
t
t
h
e
p
er
f
o
r
m
an
ce
o
f
a
p
p
licatio
n
s
r
u
n
n
in
g
o
n
VM
s
.
Vo
o
r
s
lu
y
s
et
a
l
.,
[
1
4
]
in
v
es
tig
a
ted
th
e
p
r
o
b
lem
o
f
VM
m
i
g
r
atio
n
co
s
t
.
R
ed
u
c
tio
n
in
th
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
VM
ca
n
b
e
ex
p
r
ess
ed
as
f
o
llo
w
s
:
∫
(
)
(
1
)
(
2
)
W
h
er
e
C
r
ep
r
esen
t
s
a
d
ec
r
ea
s
e
in
t
h
e
o
v
er
all
p
er
f
o
r
m
an
ce
d
u
e
to
th
e
VM
j
(
v
ir
tu
al
m
ac
h
in
e
tr
an
s
f
er
co
s
t)
,
p
ar
am
eter
k
i
s
t
h
e
av
er
a
g
e
co
ef
f
icien
t
o
f
p
er
f
o
r
m
a
n
ce
d
ev
i
atio
n
ca
u
s
ed
b
y
v
ir
tu
al
m
ac
h
i
n
es
(
k
v
al
u
e
ca
n
b
e
esti
m
ated
ab
o
u
t
0
.
1
(
1
0
%)
o
f
C
P
U
u
til
izatio
n
in
te
r
m
s
o
f
ca
teg
o
r
ies
o
f
w
eb
ap
p
licatio
n
s
)
.
T
h
e
f
u
n
ctio
n
u
p
(
t
)
co
r
r
esp
o
n
d
s
to
th
e
a
m
o
u
n
t
o
f
p
r
o
ce
s
s
o
r
u
tili
za
tio
n
b
y
VM
j
,
th
e
p
ar
a
m
eter
t
0
i
s
t
h
e
s
tar
t
ti
m
e
o
f
t
h
e
tr
a
n
s
f
er
,
Tm
j
is
th
e
co
m
p
letio
n
ti
m
e,
M
j
is
th
e
to
tal
m
e
m
o
r
y
u
s
ed
b
y
V
M
j
,
an
d
Bj
r
e
p
r
esen
ts
t
h
e
a
v
ai
lab
le
b
an
d
w
id
t
h
.
W
e
h
av
e
s
elec
ted
t
w
o
s
er
v
er
s
eq
u
ip
p
ed
w
it
h
d
u
al
-
co
r
e
p
r
o
ce
s
s
o
r
s
.
T
h
e
m
a
in
co
n
f
i
g
u
r
a
tio
n
o
f
t
h
e
t
w
o
s
er
v
er
s
is
a
s
f
o
llo
w
s
:
On
e
o
f
t
h
e
m
is
HP
P
r
o
L
ian
t
G4
w
i
th
1
.
8
6
GHz
(
d
u
al
-
co
r
e)
,
4
GB
R
A
M
a
n
d
th
e
o
th
er
i
s
H
P
P
r
o
L
ian
t
G5
w
i
th
2
.
6
6
GHz
(
d
u
al
-
co
r
e)
,
4
GB
R
A
M.
E
n
er
g
y
co
n
s
u
m
p
tio
n
f
o
r
th
e
t
w
o
s
er
v
er
s
at
d
i
f
f
er
en
t
lo
ad
lev
el
s
i
s
p
r
esen
ted
in
T
ab
le
1
[
1
5
]
.
T
ab
le
1
.
P
o
w
er
C
o
n
s
u
m
p
t
io
n
b
y
t
h
e
t
w
o
Ser
v
er
s
at
d
if
f
er
en
t
L
o
ad
L
ev
e
ls
i
n
W
atts
S
e
r
v
e
r
0%
1
0
%
2
0
%
3
0
%
4
0
%
5
0
%
6
0
%
7
0
%
80
%
9
0
%
1
0
0
%
H
P
P
r
o
L
i
a
n
t
G
4
86
8
9
.
4
9
2
.
6
96
9
9
.
5
1
0
2
1
0
6
1
0
8
1
1
2
1
1
4
1
1
7
H
P
P
r
o
L
i
a
n
t
G
5
9
3
.
7
97
1
0
1
1
0
5
1
1
0
1
1
6
1
2
1
1
2
5
1
2
6
1
3
3
1
3
5
3
.
3
.
SL
A
v
io
la
t
io
n
m
et
ric
s
SLA
v
io
latio
n
is
a
v
er
y
i
m
p
o
r
tan
t
f
ac
to
r
f
o
r
an
y
VM
m
ig
r
atio
n
alg
o
r
it
h
m
.
th
er
e
ar
e
cu
r
r
en
tl
y
t
w
o
m
et
h
o
d
s
f
o
r
d
escr
ib
in
g
t
h
e
S
L
A
v
io
latio
n
[
16
].
a.
P
DM
(
Ov
er
all
lo
s
s
o
f
p
er
f
o
r
m
an
ce
d
u
e
to
VM
Mig
r
atio
n
)
.
I
t is in
d
icate
d
in
t
h
e
eq
u
atio
n
:
∑
(
3
)
W
h
er
e
p
ar
a
m
eter
M
r
ep
r
esen
t
s
t
h
e
n
u
m
b
er
o
f
v
ir
tu
al
m
ac
h
i
n
es
in
t
h
e
d
ata
ce
n
ter
,
Cd
j
is
th
e
e
s
ti
m
ate
o
f
t
h
e
y
ield
lo
s
s
d
u
e
to
t
h
e
tr
an
s
m
is
s
io
n
o
f
VM
j
an
d
Cr
j
co
r
r
esp
o
n
d
to
th
e
to
tal
ca
p
ac
ity
o
f
th
e
d
e
m
an
d
ed
p
r
o
ce
s
s
o
r
b
y
VM
j
d
u
r
in
g
it
s
li
f
eti
m
e.
b.
SLA
T
A
H
(
S
L
A
V
io
latio
n
T
im
e
p
er
A
cti
v
e
Ho
s
t)
.
I
t
m
ea
n
s
th
e
p
er
ce
n
tag
e
o
f
to
tal
S
L
A
v
io
latio
n
ti
m
e,
d
u
r
in
g
w
h
ich
th
e
C
P
U
u
tili
za
tio
n
b
y
th
e
ac
t
iv
e
h
o
s
t
h
a
s
r
e
ac
h
ed
1
0
0
%
an
d
is
s
h
o
w
n
b
y
th
e
f
o
llo
w
i
n
g
eq
u
atio
n
:
∑
(
4
)
W
h
er
e
N
d
en
o
te
s
t
h
e
n
u
m
b
er
o
f
h
o
s
ts
in
th
e
d
ata
ce
n
ter
,
Ts
i
i
s
t
h
e
to
tal
ti
m
e
d
u
r
i
n
g
wh
ich
t
h
e
p
r
o
ce
s
s
o
r
u
tili
za
t
io
n
b
y
t
h
e
h
o
s
t
i
is
1
0
0
%
an
d
r
ai
s
es
t
h
e
s
er
v
ice
lev
e
l
Ag
r
ee
m
e
n
t
,
Ta
i
is
r
elate
d
to
t
h
e
ti
m
e
th
a
t
t
h
e
h
o
s
t
Evaluation Warning : The document was created with Spire.PDF for Python.
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s
o
n
t
h
e
h
o
s
t
ca
n
n
o
t
b
e
p
r
o
v
id
ed
b
y
th
e
p
r
o
ce
s
s
o
r
d
em
an
d
i
n
g
ca
p
ac
it
y
.
B
o
th
P
DM
an
d
SL
A
T
AH
ar
e
t
w
o
ef
f
ec
ti
v
e
m
et
h
o
d
s
f
o
r
in
d
ep
en
d
en
t
as
s
es
s
m
e
n
t
o
f
t
h
e
S
L
A
v
io
latio
n
.
T
h
er
ef
o
r
e,
th
e
SLA
v
i
o
latio
n
i
s
d
ef
i
n
ed
as
th
e
f
o
llo
w
in
g
eq
u
atio
n
[
16
]
:
(
5
)
3
.
4
.
E
nerg
y
ef
f
iciency
m
et
ric
E
n
er
g
y
e
f
f
icie
n
c
y
i
n
cl
u
d
es
en
er
g
y
co
n
s
u
m
p
t
io
n
an
d
S
L
A
v
io
latio
n
.
I
m
p
r
o
v
i
n
g
e
n
er
g
y
ef
f
icie
n
c
y
m
ea
n
s
less
e
n
er
g
y
co
n
s
u
m
p
ti
o
n
an
d
les
s
S
L
A
v
io
latio
n
i
n
d
ata
ce
n
ter
s
.
T
h
er
ef
o
r
e,
th
e
m
etr
ic
o
f
en
er
g
y
ef
f
icien
c
y
i
s
d
ef
i
n
ed
as
:
(
6
)
W
h
er
e
co
r
r
esp
o
n
d
s
to
th
e
en
er
g
y
ef
f
icie
n
c
y
o
f
a
d
ata
ce
n
ter
,
is
th
e
e
n
er
g
y
co
n
s
u
m
p
ti
o
n
o
f
a
d
ata
ce
n
ter
,
an
d
SLA
r
ep
r
esen
t
s
th
e
S
L
A
v
io
latio
n
o
f
a
d
ata
ce
n
ter
.
E
q
u
atio
n
(
6
)
s
h
o
w
s
t
h
at
th
e
h
ig
h
e
r
th
e
,
th
e
g
r
ea
ter
th
e
en
er
g
y
e
f
f
icie
n
c
y
[
7
].
4.
P
RO
P
O
SE
D
M
E
T
H
O
D,
ADAP
T
I
V
E
F
O
UR
-
T
H
R
E
SH
O
L
D
AL
G
O
RI
T
H
M
,
V
M
SE
L
E
C
T
I
O
N
AP
P
RO
ACH
,
AND
VM
AL
L
O
CAT
I
O
N
AL
G
O
R
I
T
H
M
4
.
1
.
P
ro
po
s
ed
m
et
ho
d
VM
m
i
g
r
atio
n
is
a
n
ef
f
ec
ti
v
e
m
eth
o
d
f
o
r
i
m
p
r
o
v
i
n
g
e
n
er
g
y
e
f
f
icie
n
c
y
i
n
d
ata
ce
n
ter
s
.
Of
co
u
r
s
e
,
th
er
e
ar
e
s
ev
er
al
k
e
y
i
s
s
u
es t
h
at
n
ee
d
to
b
e
ad
d
r
ess
ed
:
(
1
)
W
h
en
it
is
as
s
u
m
ed
th
a
t
a
h
o
s
t
h
a
s
a
h
ea
v
y
lo
ad
,
a
n
u
m
b
er
o
f
v
ir
t
u
al
m
ac
h
in
e
s
f
r
o
m
t
h
e
h
o
s
t
m
u
s
t
b
e
tr
an
s
f
er
r
ed
to
an
o
th
er
h
o
s
t
;
(
2
)
w
h
en
w
e
k
n
o
w
th
a
t
a
h
o
s
t
m
u
s
t
b
e
m
o
d
er
atel
y
lo
ad
ed
o
r
lig
h
tl
y
lo
ad
ed
,
w
e
d
ec
id
e
to
k
ee
p
all
v
ir
t
u
al
m
ac
h
in
e
s
i
n
t
h
is
h
o
s
t
u
n
c
h
an
g
ed
;
(
3
)
w
h
e
n
w
e
k
n
o
w
t
h
at
a
h
o
s
t
m
u
s
t
b
e
lo
w
-
lo
ad
ed
,
all
v
ir
tu
a
l
m
ac
h
i
n
es
i
n
th
e
h
o
s
t
m
u
s
t
b
e
tr
an
s
f
er
r
ed
to
an
o
th
er
h
o
s
t;
(
4
)
s
elec
tin
g
a
VM
o
r
m
o
r
e
VM
s
th
a
t
s
h
o
u
ld
b
e
m
i
g
r
ated
f
r
o
m
th
e
h
ea
v
i
l
y
lo
ad
ed
;
(
5
)
f
in
d
in
g
a
n
e
w
h
o
s
t
to
ac
co
m
m
o
d
ate
m
i
g
r
ated
VM
s
f
r
o
m
h
ea
v
i
l
y
lo
ad
ed
o
r
litt
le
-
lo
ad
ed
h
o
s
ts
.
I
n
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
,
w
e
a
u
to
m
at
icall
y
s
elec
t
f
o
u
r
t
h
r
es
h
o
ld
s
,
T
l
o
w
,
T
lig
h
t,
T
m
id
d
le
an
d
T
h
ea
v
y
f
o
r
s
o
lv
i
n
g
p
r
o
b
lem
s
(
0
≤
T
l
<T
li
<
T
m
<T
h
≤
1
)
,
w
h
ic
h
ca
u
s
es
Data
ce
n
ter
h
o
s
t
s
be
d
iv
id
ed
in
to
f
i
v
e
ca
teg
o
r
ies
:
h
o
s
ts
w
it
h
lo
w
lo
ad
,
h
o
s
ts
w
it
h
li
g
h
t
lo
ad
,
h
o
s
t
s
w
it
h
m
id
d
le
lo
ad
,
h
o
s
ts
w
it
h
h
ig
h
lo
ad
an
d
h
o
s
t
s
w
it
h
h
ea
v
y
lo
ad
.
T
h
e
v
a
lu
e
o
f
th
e
s
e
f
o
u
r
th
r
es
h
o
ld
s
ar
e
u
t
ili
ze
d
au
to
m
a
ticall
y
u
s
i
n
g
t
h
e
th
r
esh
o
ld
al
g
o
r
ith
m
ac
co
r
d
in
g
to
th
e
lo
ad
.
I
n
th
e
h
o
s
t,
th
e
p
r
o
ce
s
s
o
r
u
tili
za
tio
n
r
ate
is
les
s
t
h
an
T
l
(
U
<T
l
)
,
in
h
o
s
ts
w
i
th
li
g
h
t
lo
ad
b
et
w
ee
n
T
l
an
d
T
li
(
T
l
<U
<T
li)
,
in
h
o
s
t
s
w
it
h
m
id
d
le
lo
ad
b
et
w
ee
n
T
li
an
d
T
m
(
T
li
<U
<T
m
)
,
at
h
ig
h
lo
ad
b
et
w
ee
n
T
m
an
d
T
h
(
T
m
<U
<T
h
)
,
an
d
in
th
e
h
ea
v
y
lo
ad
th
e
p
r
o
ce
s
s
o
r
u
tili
ze
s
m
o
r
e
t
h
an
T
h
(
U>
T
h
).
Vir
tu
al
m
ac
h
i
n
e
s
ar
e
tr
an
s
f
er
r
ed
f
r
o
m
th
e
h
o
s
t
w
it
h
h
i
g
h
lo
ad
an
d
h
ea
v
y
lo
ad
to
th
e
h
o
s
ts
w
it
h
li
g
h
t
lo
ad
,
an
d
f
r
o
m
h
o
s
ts
w
it
h
lo
w
lo
ad
to
th
e
h
o
s
ts
w
it
h
m
id
d
le
lo
ad
an
d
h
o
s
ts
w
it
h
lo
w
lo
ad
go
to
s
leep
,
w
h
i
le
t
h
e
h
o
s
t
co
m
p
u
ter
i
s
h
o
s
ted
w
it
h
li
g
h
t
lo
ad
an
d
h
o
s
t
m
id
d
le
lo
ad
r
em
ain
s
u
n
ch
a
n
g
ed
.
Fig
u
r
e
1
s
h
o
w
s
t
h
e
f
lo
w
c
h
a
r
t
o
f
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
8
,
No
.
5
,
Octo
b
er
2
0
1
8
:
3
8
90
–
3
9
0
1
3894
Fig
u
r
e
1.
Flo
w
c
h
ar
t
o
f
th
e
P
r
o
p
o
s
ed
Me
th
o
d
4
.
2
.
Ada
ptiv
e
t
hresh
o
ld a
lg
o
rit
hm
(
det
ec
t
ing
o
v
er
hea
d ho
s
t
s
)
As
d
is
c
u
s
s
ed
in
Sectio
n
4
.
1
,
w
h
at
ar
e
th
e
th
r
e
s
h
o
ld
v
alu
es
o
f
T
l
،
T
li
،
T
m
an
d
T
h
?
T
o
s
o
lv
e
th
i
s
P
r
o
b
lem
,
K
-
Me
a
n
s
A
l
g
o
r
ith
m
-
Av
er
ag
e
-
Me
d
ian
A
b
s
o
l
u
te
D
ev
iatio
n
i
s
p
r
o
p
o
s
ed
.
4
.
2
.
1
.
K
AM
(
-
m
ea
ns
clu
s
t
er
ing
a
lg
o
rit
h
m
-
a
v
er
a
g
e
-
m
edia
n a
b
s
o
lute
dev
ia
t
io
n)
Fo
r
th
e
u
n
i
v
ar
iate
d
ata
s
et
o
f
a
v
ar
iab
le
V1
,
V2
,
V3
…Vn
(
V
is
C
P
U
u
tili
za
tio
n
o
f
a
h
o
s
t
at
ti
m
e
,
an
d
th
e
s
ize
o
f
ca
n
b
e
d
eter
m
i
n
ed
b
y
ex
p
er
i
m
en
tal
v
al
u
e)
,
th
e
KA
M
al
g
o
r
ith
m
u
s
es
th
e
-
m
ea
n
s
clu
s
ter
in
g
alg
o
r
ith
m
at
f
ir
s
t
f
o
r
d
i
v
id
in
g
th
e
d
ata
s
et
(
V1
,
V2
,
V3
…Vn
)
in
to
g
r
o
u
p
s
(
1
,
2
,
.
.
.
,
)
(
th
e
s
ize
o
f
ca
n
b
e
d
eter
m
i
n
ed
b
y
ex
p
er
i
m
en
ta
l
v
al
u
e,
an
d
in
th
i
s
p
ap
er
,
=
5
)
,
w
h
er
e
=
(
−
1
+1
,
−
1
+2
,
.
.
.
,
)
,
f
o
r
all
1
≤
≤
5
,
an
d
0
=
0<
1<
2<
⋅⋅⋅
<
5
=
.
Su
b
s
e
q
u
en
tl
y
,
K
A
M
g
et
s
th
e
av
er
a
g
e
v
al
u
e
o
f
ea
c
h
g
r
o
u
p
,
f
o
r
m
al
ized
as f
o
llo
w
s
[
2
4
]
:
GAK
=
(
VJ
+1
+1
,
V
J
+2
+2
…
VJ
K)
/ (
j
k
-
jk
-
1)
(
7
)
Fo
r
all
1
≤
≤
5
.
T
h
en
,
K
A
M
g
ets
th
e
Me
d
ian
A
b
s
o
lu
te
Dev
iatio
n
(
M
A
D)
o
f
(
1
,
2
…
.
5
)
.
T
h
er
ef
o
r
e,
th
e
M
A
D
i
s
d
ef
i
n
ed
as f
o
llo
ws:
MA
D
=
m
ed
ian
(
|
G
A
P
-
m
e
d
ian
q
(
GA
)
|
)
(
8
)
W
h
er
e
1
≤
≤
5
an
d
m
ed
ian
(
)
ar
e
t
h
e
a
v
er
ag
e
v
al
u
e
o
f
.
Fi
n
all
y
,
th
e
f
o
u
r
t
h
r
es
h
o
ld
s
(T
l
،
T
li
،
T
m
an
d
T
h
)
in
th
e
p
r
o
p
o
s
ed
m
e
th
o
d
ca
n
b
e
d
ef
i
n
ed
as
f
o
llo
w
s
:
T
l
=
0
.
5
(
1
−
×
MA
D)
(
9
)
T
li
=
0
.
7
(
1
−
×
MA
D)
(
1
0
)
T
m
=
0
.
9
(
1
−
×
MA
D)
(
1
1
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
E
n
erg
y
-
A
w
a
r
e
A
d
a
p
tive
F
o
u
r
Th
r
esh
o
ld
s
Tech
n
iq
u
e
fo
r
Op
t
ima
l V
ir
tu
a
l Ma
ch
in
e
…
(
A
.
R
.
Mo
h
a
z
a
b
iyeh
)
3895
T
h
=
1
−
×
MA
D
(
1
2
)
T
h
at
r
∈
R
+
r
ep
r
esen
t
s
a
p
ar
a
m
eter
o
f
t
h
e
al
g
o
r
ith
m
th
at
d
eter
m
i
n
es
h
o
w
t
h
e
s
y
s
te
m
w
il
l
co
n
s
o
lid
ate
t
h
e
v
ir
tu
a
l
m
ac
h
i
n
e.
Fo
r
e
x
a
m
p
le,
th
e
m
o
r
e
r
,
th
e
g
r
ea
ter
t
h
e
e
n
er
g
y
co
n
s
u
m
p
tio
n
,
b
u
t
i
n
t
h
e
co
n
s
o
lid
at
io
n
o
f
th
e
v
ir
tu
al
m
ac
h
in
e
s
w
i
ll
r
e
s
u
lt
i
n
le
s
s
v
io
latio
n
s
o
f
t
h
e
s
er
v
ice
le
v
e
l
ag
r
ee
m
e
n
t.
I
n
t
h
e
p
r
o
p
o
s
ed
m
o
d
el,
w
e
h
a
v
e
co
n
s
id
er
ed
ex
p
er
i
m
e
n
ts
to
s
i
m
u
la
te
th
e
v
alu
e
o
f
r
to
b
e
5
.
T
h
e
m
ea
n
ab
s
o
l
u
te
er
r
o
r
co
m
p
le
x
it
y
i
s
t
h
e
m
ea
n
a
v
er
ag
e
cl
u
s
ter
i
n
g
al
g
o
r
ith
m
K,
(
×
×
)
,
w
h
er
e
m
,
n
,
a
n
d
t
ar
e
th
e
n
u
m
b
er
o
f
g
r
o
u
p
s
,
d
ata
s
ize
an
d
t
h
e
n
u
m
b
er
o
f
r
ep
etitio
n
s
,
r
esp
ec
tiv
el
y
.
T
h
e
v
alu
e
o
f
(
T
l
,
T
li
,
T
m
an
d
T
h
)
also
v
ar
ies
ac
c
o
r
d
in
g
to
t
h
e
co
n
ti
n
u
o
u
s
ch
an
g
e
o
f
Vi
(
=
1
,
2
,
3
.
.
.
)
,
.
A
s
a
r
es
u
lt,
t
h
e
m
ea
n
a
b
s
o
lu
te
er
r
o
r
o
f
t
h
e
m
ea
n
cl
u
s
ter
i
n
g
a
lg
o
r
it
h
m
K
is
a
n
ad
ap
tiv
e
f
o
u
r
-
th
r
e
s
h
o
ld
al
g
o
r
ith
m
.
W
h
en
t
h
e
w
o
r
k
lo
ad
s
ar
e
d
y
n
a
m
ic
an
d
u
n
p
r
ed
ictab
le,
th
e
ab
s
o
lu
te
er
r
o
r
o
f
th
e
m
id
d
le
m
ea
n
o
f
t
h
e
m
ea
n
cl
u
s
ter
i
n
g
al
g
o
r
ith
m
K
cr
ea
tes
a
h
ig
h
er
en
er
g
y
e
f
f
icien
c
y
b
y
s
etti
n
g
th
e
v
al
u
e
(T
l
, T
li
,
T
m
an
d
T
h
)
(
in
C
o
m
p
ar
is
o
n
w
it
h
a
f
i
x
ed
-
th
r
e
s
h
o
ld
alg
o
r
ith
m
)
[
7
]
.
4
.
3
.
Virt
ua
l
m
a
c
hin
e
s
elec
t
io
n a
p
pro
a
ches
As
d
escr
ib
ed
in
th
e
p
r
ev
io
u
s
s
ec
tio
n
,
a
n
u
m
b
er
o
f
v
ir
t
u
al
m
a
ch
in
e
s
o
n
h
ig
h
lo
ad
an
d
h
ea
v
y
lo
ad
h
o
s
t
s
h
o
u
ld
b
e
m
ig
r
ated
to
a
n
o
t
h
er
h
o
s
t
w
it
h
li
g
h
t
lo
ad
.
W
h
ic
h
v
ir
tu
al
m
ac
h
i
n
e
s
h
o
u
ld
b
e
tr
an
s
f
er
r
ed
?
I
n
g
en
er
al
,
th
e
a
m
o
u
n
t
o
f
h
o
s
t
u
s
a
g
e
o
f
th
e
p
r
o
ce
s
s
o
r
an
d
t
h
e
s
ize
o
f
t
h
e
m
e
m
o
r
y
w
ill
af
f
ec
t
it
s
e
n
er
g
y
ef
f
icien
c
y
;T
h
er
ef
o
r
e
th
e
ap
p
r
o
ac
h
es (
MM
T
,
MC)
w
ill b
e
a
d
d
r
ess
ed
in
t
h
is
s
ec
tio
n
.
4
.
3
.
1
.
M
ini
m
u
m
m
ig
ra
t
io
n t
i
m
e
(
M
M
T
)
T
h
e
tr
an
s
itio
n
ti
m
e
o
f
a
v
ir
tu
a
l
m
ac
h
i
n
e
w
ill
v
ar
y
ac
co
r
d
in
g
to
its
d
if
f
er
en
t
m
e
m
o
r
y
s
ize
s
.
A
v
ir
t
u
al
m
e
m
o
r
y
d
ev
ice
w
i
th
less
m
e
m
o
r
y
m
ea
n
s
les
s
m
i
g
r
atio
n
ti
m
e
u
n
d
er
th
e
s
a
m
e
s
p
ar
e
n
et
w
o
r
k
b
a
n
d
w
id
th
.
Fo
r
ex
a
m
p
le,
a
VM
w
it
h
1
6
GB
m
e
m
o
r
y
m
a
y
h
av
e
a
tr
an
s
f
er
t
i
m
e
eq
u
al
to
1
6
ti
m
es
th
e
d
e
v
ice
w
i
th
1
GB
o
f
m
e
m
o
r
y
.
I
t’
s
cr
y
s
ta
l
clea
r
th
a
t
s
elec
ti
n
g
t
h
e
VM
w
i
th
1
6
G
B
o
f
m
e
m
o
r
y
o
r
t
h
e
VM
w
it
h
1
GB
o
f
m
e
m
o
r
y
g
r
ea
tl
y
a
f
f
ec
t
en
er
g
y
e
f
f
icie
n
c
y
o
f
d
ata
ce
n
ter
s
.
T
h
er
ef
o
r
e,
if
th
e
h
o
s
t
h
as
a
h
ig
h
lo
ad
,
th
is
ap
p
r
o
ac
h
w
ill
s
elec
t
t
h
e
v
ir
t
u
al
m
ac
h
i
n
e
w
i
t
h
t
h
e
least
a
m
o
u
n
t
o
f
m
e
m
o
r
y
in
co
m
p
ar
is
o
n
w
i
th
o
th
er
v
ir
t
u
al
tr
a
n
s
f
er
d
e
v
ices
to
th
e
h
o
s
t
f
o
r
tr
an
s
f
er
.
T
h
e
ab
o
v
e
m
et
h
o
d
ch
o
o
s
es
v
ir
t
u
al
d
ev
ice
u
to
h
a
v
e
th
e
f
o
llo
w
in
g
co
n
d
itio
n
s
[
1
7
]
,
[
1
9
]
.
R
A
M
(
)
≤
R
A
M
(
v
)
,
∀
v
∈
VM
(
1
3
)
W
h
er
e
VM
,
m
ea
n
s
t
h
e
s
et
o
f
VM
s
a
s
s
i
g
n
ed
to
h
o
s
t
a
n
d
R
A
M (
)
is
th
e
a
m
o
u
n
t o
f
m
e
m
o
r
y
c
u
r
r
en
t
l
y
u
s
ed
b
y
th
e
VM
.
4
.
3
.
2
.
M
a
x
i
m
u
m
c
o
rr
ela
t
io
n (
M
C)
T
h
e
m
a
x
i
m
u
m
co
r
r
elatio
n
p
r
o
ce
d
u
r
e
is
b
ased
o
n
th
e
p
r
o
p
o
s
ed
id
ea
b
y
Ver
m
a.
T
h
e
id
ea
is
th
at,
i
f
th
er
e
is
a
h
i
g
h
co
r
r
elatio
n
b
etw
ee
n
ap
p
licatio
n
s
r
u
n
n
in
g
o
n
a
s
er
v
er
,
it
is
m
o
r
e
lik
el
y
t
h
at
th
e
o
v
er
lo
ad
in
g
o
cc
u
r
s
o
n
t
h
e
s
er
v
er
.
B
ased
o
n
th
is
id
ea
,
t
h
o
s
e
v
ir
tu
al
m
ac
h
in
e
s
s
h
o
u
ld
m
i
g
r
ate
t
h
a
t
h
a
v
e
t
h
e
h
i
g
h
est
co
r
r
elatio
n
o
f
C
P
U
co
n
s
u
m
p
ti
o
n
w
ith
o
t
h
er
v
i
r
t
u
al
m
ac
h
i
n
e
s
.
So
,
if
a
h
o
s
t
h
as
h
ea
v
y
lo
ad
,
m
ac
h
i
n
es
t
h
at
u
s
e
th
e
m
o
s
t
r
eso
u
r
ce
s
o
n
t
h
e
h
o
s
t
(
u
s
e
p
r
o
ce
s
s
o
r
r
eso
u
r
ce
s
a
n
d
m
e
m
o
r
y
m
o
r
e
t
h
an
o
t
h
er
s
a
n
d
k
n
o
w
n
as
a
lar
g
e
v
ir
tu
a
l
m
ac
h
i
n
e)
i
s
c
h
o
s
e
n
f
o
r
d
is
p
lace
m
e
n
t.
T
h
e
ab
o
v
e
a
p
p
r
o
ac
h
ch
o
o
s
es
t
h
e
v
ir
tu
a
l
m
ac
h
in
e
VM
u
i
f
it
s
atis
f
ies t
h
e
f
o
llo
w
in
g
co
n
d
iti
o
n
[
1
7
]
,
[
1
8
]
.
C
P
U
(
)
+
R
A
M
(
)
≤
C
P
U
(
v
)
+
R
A
M
(
v
)
,
∀
v
∈
VM
(
1
4
)
W
h
er
e
VM
r
ep
r
esen
t
s
t
h
e
s
et
o
f
v
ir
tu
al
m
ac
h
i
n
es
as
s
ig
n
ed
to
h
o
s
t
i
,
an
d
th
e
C
P
U
(
)
a
n
d
R
A
M
(
u
)
ar
e
t
h
e
a
m
o
u
n
t o
f
m
e
m
o
r
y
an
d
p
r
o
ce
s
s
o
r
w
h
ich
ar
e
u
s
ed
b
y
VM
u
c
u
r
r
en
tl
y
.
4
.
4
.
VM
deplo
y
m
e
nt
a
lg
o
rit
h
m
(
s
o
urce
a
llo
ca
t
io
n a
lg
o
rit
h
m
)
I
n
th
e
p
r
o
p
o
s
ed
m
e
th
o
d
f
o
r
s
elec
tin
g
t
h
e
b
est
h
o
s
t
f
o
r
VM
e
m
b
ed
d
in
g
,
w
e
u
s
e
P
o
w
er
Aw
ar
e
B
est
Fit
Dec
r
ea
s
i
n
g
A
l
g
o
r
ith
m
(
P
A
B
FD
A
)
.
T
h
e
al
g
o
r
ith
m
’
s
m
eth
o
d
is
t
h
at
at
f
ir
s
t,
it
c
h
ec
k
s
th
e
h
o
s
t
s
li
s
t
a
n
d
v
er
if
y
w
h
et
h
er
a
h
o
s
t
h
as
e
x
t
en
s
i
v
e
lo
ad
u
s
i
n
g
th
e
ad
d
iti
o
n
al
lo
ad
d
etec
tio
n
alg
o
r
it
h
m
.
T
h
en
,
i
f
th
e
h
o
s
t
h
a
s
an
o
v
er
lo
ad
,
t
h
e
al
g
o
r
ith
m
ap
p
lies
t
h
e
v
ir
tu
a
l
m
ac
h
in
e
s
ele
ctio
n
p
o
lic
y
to
s
e
lect
t
h
e
v
ir
t
u
al
m
ac
h
i
n
es
to
b
e
m
i
g
r
ated
f
r
o
m
th
e
h
o
s
t.
W
h
en
a
lis
t
o
f
v
ir
tu
a
l
m
ac
h
in
e
s
th
at
ar
e
m
i
g
r
ated
f
r
o
m
h
o
s
t
s
w
i
th
o
v
er
lo
ad
is
b
u
ilt,
th
e
al
g
o
r
ith
m
f
o
r
p
lacin
g
th
e
v
ir
t
u
al
m
ac
h
in
e
w
ill
f
i
n
d
a
n
e
w
lo
ca
tio
n
f
o
r
v
ir
t
u
al
m
ac
h
in
e
s
t
h
at
co
u
ld
b
e
m
i
g
r
ated
[
1
9
]
.
T
h
e
s
ec
o
n
d
s
tep
o
f
th
e
al
g
o
r
ith
m
is
to
f
in
d
t
h
e
lo
w
lo
ad
h
o
s
ts
an
d
to
p
lace
v
ir
t
u
al
m
ac
h
in
e
s
f
r
o
m
th
ese
h
o
s
ts
to
an
o
th
er
h
o
s
t.
T
h
e
alg
o
r
ith
m
r
etu
r
n
s
a
m
ap
o
f
a
co
m
b
in
atio
n
o
f
m
i
g
r
ato
r
y
v
ir
tu
al
m
ac
h
i
n
es
t
h
at
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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8
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I
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&
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Vo
l.
8
,
No
.
5
,
Octo
b
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2
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1
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–
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co
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ll
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tio
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h
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p
r
o
ce
s
s
o
f
alg
o
r
it
h
m
o
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er
atio
n
is
e
x
p
r
ess
ed
in
s
tag
e
s
as
f
o
ll
o
w
:
a.
A
r
r
an
g
e
a
lis
t o
f
v
ir
t
u
al
m
ac
h
i
n
es
f
r
o
m
v
ir
tu
al
m
ac
h
in
e
s
to
r
ed
u
ce
th
e
p
r
o
ce
s
s
o
r
ef
f
icien
c
y
.
b.
Fo
r
ea
ch
v
ir
tu
al
m
ac
h
i
n
e
in
t
h
e
lis
t
o
f
v
ir
t
u
al
m
ac
h
i
n
es,
all
o
ca
te
th
e
m
i
n
i
m
u
m
p
o
w
er
to
th
e
h
o
s
ts
as
t
h
e
m
ax
i
m
u
m
p
o
w
er
.
c.
Fo
r
ea
ch
h
o
s
t
in
th
e
h
o
s
t
lis
t
,
if
th
e
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o
s
t
h
a
s
s
u
f
f
icie
n
t
r
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u
r
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s
f
o
r
v
ir
t
u
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m
ac
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n
es,
it
ev
alu
ates
t
h
e
p
o
w
er
o
f
v
ir
tu
al
m
ac
h
in
e
s
an
d
h
o
s
ts
.
I
f
th
e
p
o
w
er
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les
s
th
an
t
h
e
m
i
n
i
m
u
m
p
o
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er
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ed
icate
d
h
o
s
t
is
th
e
cu
r
r
en
t h
o
s
t a
n
d
th
e
p
o
w
er
o
f
th
e
v
ir
tu
al
m
ac
h
in
e
s
an
d
t
h
e
h
o
s
t is lo
w
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d.
I
f
th
e
d
ed
icate
d
h
o
s
t is
n
o
t e
m
p
ty
,
t
h
e
n
th
e
v
ir
t
u
al
m
ac
h
i
n
e
w
il
l b
e
ass
i
g
n
ed
to
an
o
t
h
er
d
ev
ice
[
1
8
]
,
[
2
0
]
.
5.
E
XP
E
R
I
M
E
NT
S AN
D
P
E
R
F
O
RM
ANCE E
VALUA
T
I
O
N
5
.
1
.
E
x
peri
m
e
nt
s
et
up
I
n
th
i
s
r
esear
ch
,
t
h
e
C
lo
u
d
S
i
m
s
i
m
u
lato
r
h
as
b
ee
n
u
s
ed
t
o
s
i
m
u
late
alg
o
r
it
h
m
s
,
an
d
t
h
e
s
ce
n
ar
io
p
r
esen
ted
in
[
4
5
]
is
u
s
ed
to
s
i
m
u
late
al
g
o
r
ith
m
s
.
I
n
th
i
s
r
esear
ch
,
th
r
ee
co
m
m
o
n
l
y
u
s
ed
m
et
h
o
d
s
n
a
m
el
y
MA
D,
I
QR
,
3
th
,
ar
e
s
i
m
u
late
d
f
o
r
t
h
e
"
W
h
en
is
m
ig
r
atio
n
ti
m
e"
is
s
u
e
a
n
d
t
h
e
t
h
r
ee
w
i
d
ely
u
s
ed
m
et
h
o
d
s
n
a
m
e
l
y
M
MT
,
R
S,
an
d
MC
ar
e
s
i
m
u
l
ated
f
o
r
th
e
"
W
h
ic
h
v
i
r
tu
al
m
ac
h
i
n
e
to
b
e
s
elec
ted
f
o
r
m
i
g
r
atio
n
"
is
s
u
e
an
d
is
co
m
p
ar
ed
in
s
e
v
er
al
s
c
en
ar
io
s
.
5
.
2
.
Si
m
ula
t
ed
s
ce
na
rio
pro
f
ile
I
n
th
i
s
s
ce
n
ar
io
,
a
d
ata
ce
n
ter
i
s
s
i
m
u
lated
b
y
8
0
0
h
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