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u
s
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et
co
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tio
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[
1
]
.
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m
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n
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,
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s
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[
2
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[
3
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ail
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it
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tab
ili
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[
4
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[
5
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lan
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s
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ied
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y
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s
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et
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t
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atac
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ter
.
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to
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est s
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te
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o
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tim
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l
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[
6
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,
[
7
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.
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o
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tatic
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d
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n
a
m
ic
ca
teg
o
r
y
o
f
al
g
o
r
ith
m
s
[
8
]
.
St
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al
g
o
r
ith
m
s
r
eq
u
ir
es
ad
v
a
n
ce
d
in
f
o
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m
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tio
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d
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s
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tatic
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ith
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in
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v
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m
e
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t
w
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lo
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.
Ho
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en
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n
m
en
ts
w
h
er
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lo
ad
v
ar
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r
ap
id
ly
[
9
]
,
[
1
0
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.
I
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ca
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Evaluation Warning : The document was created with Spire.PDF for Python.
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I
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J
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Sci,
Vo
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22
,
No
.
3
,
J
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2
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2
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6
9
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r
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r
o
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e,
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d
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r
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[
1
1
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in
tr
o
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d
g
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ee
d
y
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ased
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o
r
ith
m
b
y
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lass
if
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ased
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.
[
1
2
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1
2
]
.
L
ak
r
a
an
d
Yad
av
[
1
3
]
a
p
p
l
ied
m
u
lti
-
o
b
j
ec
tiv
e
task
s
ch
ed
u
li
n
g
alg
o
r
it
h
m
to
m
ap
tas
k
to
VM
an
d
o
b
s
er
v
ed
r
ed
u
ctio
n
i
n
t
h
r
o
u
g
h
p
u
t
ti
m
e
a
n
d
ex
ec
u
tio
n
co
s
t
[
1
3
]
.
R
en
et
a
l.
[
1
4
]
q
u
an
t
if
ied
lo
ad
an
d
p
r
o
ce
s
s
i
n
g
p
o
w
er
o
f
VM
s
in
a
d
y
n
a
m
ic
lo
ad
b
alan
ci
n
g
a
lg
o
r
it
h
m
.
I
n
th
i
s
alg
o
r
it
h
m
,
b
y
u
s
i
n
g
s
in
g
le
ex
p
o
n
e
n
tial
m
ec
h
a
n
is
m
,
a
r
ed
u
ctio
n
in
t
h
e
s
er
v
er
lo
ad
an
d
an
i
m
p
r
o
v
e
m
en
t
in
t
h
e
q
u
ali
t
y
o
f
clie
n
t
s
er
v
ic
e
is
o
b
s
er
v
ed
[
1
4
]
.
T
a
w
f
ee
k
et
a
l.
u
s
ed
an
t
co
lo
n
y
o
p
ti
m
i
za
tio
n
al
g
o
r
ith
m
to
allo
ca
te
th
e
in
co
m
in
g
j
o
b
s
to
v
ir
t
u
al
m
ac
h
in
e
an
d
o
b
s
er
v
ed
a
r
ed
u
ctio
n
i
n
m
ak
e
s
p
an
o
f
g
i
v
e
n
task
s
[
1
5
]
.
B
ab
u
et
a
l
.
[
1
6
]
u
s
ed
b
eh
av
io
u
r
o
f
h
o
n
e
y
b
ee
f
o
r
ag
in
g
s
tr
ate
g
y
to
b
alan
c
e
u
n
d
er
lo
ad
ed
an
d
o
v
er
lo
ad
ed
v
ir
tu
al
m
ac
h
i
n
es,
i
n
clo
u
d
co
m
p
u
ti
n
g
e
n
v
ir
o
n
m
e
n
ts
[
1
6
]
.
Sh
ee
j
a
an
d
J
a
y
a
lek
s
h
m
i
[
1
7
]
u
s
ed
co
s
t
a
s
a
p
ar
am
eter
to
s
elec
t
o
p
ti
m
a
l
v
ir
tu
al
m
ac
h
i
n
e
b
ased
o
n
h
o
n
e
y
b
ee
b
eh
av
io
u
r
an
d
o
b
tain
ed
a
co
s
t
-
ef
f
ec
tiv
e
m
et
h
o
d
o
f
lo
ad
b
alan
cin
g
.
Ho
w
e
v
er
,
in
t
h
i
s
tec
h
n
iq
u
e,
q
u
al
it
y
a
n
d
o
v
er
all
p
er
f
o
r
m
a
n
ce
o
f
s
y
s
te
m
d
ec
r
ea
s
ed
d
u
e
to
a
g
r
ea
ter
n
u
m
b
er
o
f
VM
m
ig
r
atio
n
s
[
1
7]
.
B
ab
u
a
n
d
S
a
m
u
el
[
1
8
]
ap
p
li
ed
an
e
n
h
a
n
ce
d
b
ee
co
lo
n
y
alg
o
r
ith
m
in
w
h
ich
a
j
o
b
p
r
io
r
it
y
w
as
co
n
s
id
er
ed
to
m
i
g
r
ate
task
s
f
r
o
m
an
o
v
er
lo
ad
ed
VM
to
an
u
n
d
er
lo
ad
ed
VM
in
o
r
d
er
to
r
ed
u
ce
s
y
s
te
m
i
m
b
alan
ce
.
Ho
w
e
v
er
,
in
t
h
i
s
al
g
o
r
ith
m
,
a
h
i
g
h
r
ate
o
f
m
ig
r
ati
o
n
o
f
tas
k
ad
v
er
s
el
y
af
f
ec
ted
th
e
p
er
f
o
r
m
an
ce
o
f
t
h
e
s
y
s
te
m
[
1
8]
.
A
J
o
s
h
i
et
a
l
.
a
s
s
ig
n
ed
VM
s
to
h
o
s
t
b
ased
o
n
n
u
m
b
er
o
f
p
r
o
ce
s
s
o
r
s
in
u
s
e
an
d
as
s
i
g
n
ed
tas
k
s
to
t
h
e
r
eso
u
r
ce
s
b
ased
o
n
b
alan
ce
co
n
d
itio
n
o
f
V
Ms.
T
h
is
r
ed
u
ce
d
d
eg
r
ee
o
f
i
m
b
ala
n
ce
o
f
s
y
s
te
m
a
n
d
also
w
aiti
n
g
ti
m
e
o
f
ta
s
k
s
[
1
9]
.
J
o
s
h
i
an
d
Mu
n
is
a
m
y
[
2
0
]
ass
ig
n
ed
VM
s
to
h
o
s
t
b
ased
o
n
m
e
m
b
er
s
h
ip
v
alu
e
o
f
h
o
s
t.
T
h
is
al
g
o
r
ith
m
i
m
p
r
o
v
es
d
e
g
r
ee
o
f
i
m
b
ala
n
ce
,
ex
ec
u
tio
n
co
s
t,
th
r
o
u
g
h
p
u
t
ti
m
e
,
ex
ec
u
t
io
n
t
i
m
e,
m
a
k
esp
a
n
a
n
d
ce
n
tr
al
p
r
o
ce
s
s
i
n
g
u
n
i
t
(
C
P
U
)
ti
m
e.
I
n
t
h
is
alg
o
r
it
h
m
,
VM
allo
ca
tio
n
a
n
d
tas
k
allo
ca
tio
n
p
o
lic
y
ar
e
m
o
d
if
ied
in
o
r
d
er
to
f
in
d
o
p
ti
m
al
m
ap
p
in
g
o
f
r
eso
u
r
ce
s
.
T
o
m
o
d
if
y
V
M
allo
ca
tio
n
p
o
lic
y
,
m
e
m
b
er
s
h
ip
v
alu
e
o
f
h
o
s
t
i
s
ca
lcu
lated
.
A
l
s
o
,
to
m
o
d
if
y
tas
k
allo
ca
tio
n
p
o
lic
y
,
u
n
d
er
u
tili
za
tio
n
a
n
d
o
v
er
u
tili
za
tio
n
o
f
VM
s
w
er
e
c
alcu
lat
ed
[
20]
.
Kr
is
h
n
ad
o
s
s
a
n
d
J
ac
o
b
[
2
1
]
d
ev
elo
p
o
p
p
o
s
itio
n
al
cu
c
k
o
o
s
ea
r
c
h
alg
o
r
ith
m
(
O
C
S
A
)
to
i
m
p
r
o
v
es
ex
ec
u
tio
n
co
s
t
a
n
d
m
ak
e
s
p
an
p
ar
a
m
eter
.
T
h
is
al
g
o
r
ith
m
i
s
co
m
b
i
n
atio
n
o
f
cu
ck
o
o
s
ea
r
ch
al
g
o
r
ith
m
(
C
S
A
)
an
d
o
p
p
o
s
itio
n
al
b
ased
lear
n
in
g
(
OB
L
)
.
T
h
is
h
y
b
r
id
v
er
s
io
n
p
r
o
v
id
e
s
o
lu
tio
n
to
task
s
c
h
ed
u
li
n
g
f
o
r
t
h
e
d
y
n
a
m
ic
a
llo
ca
tio
n
o
f
r
eso
u
r
c
es
[
2
1
]
.
Kr
is
h
n
ad
o
s
s
an
d
J
a
co
b
[
2
2
]
d
ev
elo
p
o
p
p
o
s
itio
n
al
lio
n
o
p
ti
m
izatio
n
alg
o
r
ith
m
(
O
L
O
A
)
to
i
m
p
r
o
v
es
ex
ec
u
tio
n
co
s
t
a
n
d
m
a
k
e
s
p
an
p
ar
a
m
eter
.
T
h
is
alg
o
r
ith
m
is
co
m
b
i
n
atio
n
o
f
l
i
o
n
o
p
ti
m
izatio
n
al
g
o
r
ith
m
(
L
OA
)
an
d
o
p
p
o
s
itio
n
al
b
ased
lear
n
in
g
(
OB
L
)
.
T
h
is
h
y
b
r
id
v
er
s
io
n
o
f
al
g
o
r
ith
m
p
r
o
v
id
e
s
o
lu
tio
n
f
o
r
tas
k
s
c
h
ed
u
l
in
g
o
p
ti
m
izatio
n
[
2
2
]
.
T
h
e
ab
o
v
e
s
tu
d
ies
s
h
o
w
t
h
at
an
ad
eq
u
ate
r
esear
ch
w
as
ca
r
r
ied
o
u
t
to
ev
alu
ate
th
e
p
er
f
o
r
m
an
ce
o
f
s
ch
ed
u
lin
g
al
g
o
r
ith
m
s
u
s
i
n
g
t
h
e
p
ar
a
m
eter
s
li
k
e
ex
ec
u
tio
n
t
i
m
e,
m
a
k
esp
a
n
,
r
eso
u
r
ce
u
tili
z
atio
n
etc.
Ho
w
e
v
er
,
r
esear
ch
o
n
ev
alu
atio
n
o
f
alg
o
r
ith
m
s
co
n
s
id
er
i
n
g
d
eg
r
ee
o
f
im
b
a
lan
ce
,
ex
ec
u
tio
n
co
s
t
an
d
m
ak
e
s
p
an
ti
m
e
h
a
s
n
o
t
b
ee
n
ad
eq
u
atel
y
ad
d
r
ess
ed
.
T
h
er
ef
o
r
e
,
th
is
w
o
r
k
co
n
s
id
e
r
ed
m
et
h
o
d
to
r
ed
u
ce
th
e
p
er
f
o
r
m
an
ce
p
ar
a
m
e
ter
s
s
u
c
h
as
d
eg
r
ee
o
f
i
m
b
ala
n
ce
,
ex
ec
u
tio
n
co
s
t
a
n
d
m
a
k
esp
a
n
ti
m
e.
T
h
is
p
ap
er
p
r
o
p
o
s
es
an
al
g
o
r
ith
m
w
h
ic
h
allo
ca
te
VM
to
a
b
est
s
u
itab
le
h
o
s
t,
b
ased
o
n
av
a
ilab
ilit
y
o
f
r
an
d
o
m
ac
ce
s
s
m
e
m
o
r
y
(
R
A
M
)
an
d
m
icr
o
p
r
o
ce
s
s
o
r
w
it
h
o
u
t
in
ter
lo
c
k
ed
p
ip
elin
ed
s
tag
e
s
(
MI
P
S)
o
f
h
o
s
t.
I
n
ad
d
itio
n
,
p
r
o
p
o
s
ed
alg
o
r
ith
m
allo
ca
te
task
to
a
b
est
s
u
itab
le
VM
b
y
co
n
s
id
er
in
g
b
alan
ce
d
co
n
d
itio
n
o
f
VM
.
I
f
VM
is
in
a
n
o
v
er
lo
ad
ed
co
n
d
itio
n
,
task
w
i
ll
b
e
tr
an
s
f
er
r
ed
to
an
u
n
d
er
lo
ad
ed
VM
.
T
h
u
s
,
a
n
e
w
l
y
p
r
o
p
o
s
ed
alg
o
r
ith
m
is
g
i
v
en
a
n
a
m
e
a
s
a
d
y
n
a
m
ic
d
e
g
r
ee
m
e
m
o
r
y
b
alan
ce
d
a
llo
ca
tio
n
(
D2
MB
A
)
al
g
o
r
ith
m
.
T
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
D2
MB
A
h
as
b
ee
n
s
i
m
u
lated
u
s
in
g
a
s
i
m
u
latio
n
to
o
l Cl
o
u
d
Si
m
[
2
3
]
.
T
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
v
i
z.
D2
MB
A
b
ased
is
co
m
p
ar
ed
w
it
h
t
h
e
o
th
er
lo
ad
b
alan
cin
g
al
g
o
r
ith
m
s
v
iz.
R
o
u
n
d
R
o
b
in
(
R
R
)
an
d
d
y
n
a
m
i
c
d
eg
r
ee
b
alan
ce
w
it
h
C
P
U
b
ased
(
D2
B
_
C
PU
b
ased
)
[
1
9
]
.
T
h
e
D2
MB
A
alg
o
r
ith
m
s
h
o
w
s
a
n
i
m
p
r
o
v
ed
ef
f
icien
c
y
o
f
s
y
s
te
m
in
ter
m
s
o
f
p
er
f
o
r
m
a
n
ce
p
ar
am
eter
s
s
u
c
h
as d
e
g
r
ee
o
f
i
m
b
a
lan
ce
,
e
x
ec
u
t
io
n
co
s
t a
n
d
m
ak
e
s
p
an
ti
m
e.
T
h
is
p
ap
er
is
o
r
g
an
ized
in
t
h
e
f
o
llo
w
i
n
g
w
a
y
.
Sect
io
n
2
d
escr
ib
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
w
h
i
ch
in
cl
u
d
e
f
u
n
ctio
n
i
n
g
o
f
al
g
o
r
ith
m
.
Sec
tio
n
3
ex
p
lai
n
s
e
x
p
er
i
m
e
n
tal
s
etu
p
u
s
ed
f
o
r
th
e
al
g
o
r
ith
m
.
Sectio
n
4
e
x
p
lain
m
at
h
e
m
a
tical
m
o
d
el
o
f
s
y
s
te
m
.
Sectio
n
5
ex
p
lain
s
co
m
p
lex
it
y
a
n
d
w
o
r
k
f
lo
w
an
al
y
s
is
o
f
p
r
o
p
o
s
ed
alg
o
r
ith
m
.
Sectio
n
6
an
al
y
s
e
s
th
e
r
es
u
lt
s
an
d
w
h
ic
h
is
f
o
llo
w
ed
b
y
co
n
c
lu
s
io
n
s
.
2.
P
RO
P
O
SE
D
M
E
T
H
O
D
T
h
i
s
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N
:
2502
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4752
I
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p
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e
n
t
s
.
A
l
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:
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sk
Allo
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p
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sk
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V
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t
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locat
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er
e
,
=
N
o.
o
f
p
ro
c
esso
r
=
M
illio
n
s
o
f
i
n
st
ru
c
t
ion
s
p
er
seco
n
d
of a
ll
p
ro
c
ess
or
=
B
an
d
w
i
d
t
h
o
f
C
ap
acit
y o
f
all
(
)
=
1
+
2
+
⋯
+
Loa
d
o
n
(
,
)
=
(
,
)
(
,
)
Wh
er
e
,
,
=
Loa
d
o
f
at
t
im
e
,
,
=
N
o.
o
f
t
ask
at
t
ime
on
s
er
vic
e
q
u
eu
e
(
,
)
=
Se
rvic
e
ra
t
e
of
at
t
ime
3.
D
et
e
rmin
e
if
t
h
e
sy
st
em
is
b
alan
c
e
d
or
n
ot b
y ch
e
c
k
in
g
t
h
e
valu
e
of st
a
n
d
a
rd
d
eviat
io
n
“
”
w
i
t
h
t
h
re
sh
old valu
e
(
0
-
1)
4.
Loa
d
is
b
ala
n
c
ed
on
ly
i
f
l
oa
d
is
smalle
r
t
h
an
m
aximu
m
c
ap
aci
t
y
5.
Fin
d
out
se
t
s
o
f
viz
. o
ver
loa
d
e
d
or
u
n
d
er
loa
d
ed
,
d
ep
e
n
d
i
n
g
u
p
o
n
lo
ad
on
6.
So
rt
ove
rlo
a
d
ed
in
a
d
ec
re
asin
g
o
rd
er
an
d
u
n
d
er
loa
d
e
d
in
a
n
i
n
c
re
asin
g
or
d
er
7.
Fin
d
t
o
t
ran
s
f
er
t
ask
f
r
o
m
an
ove
rlo
a
d
ed
.
if
(
<
)
t
h
e
n
if
(
=
=
)
t
h
e
n
8.
Fin
d
t
o
t
ran
s
f
er
t
ask
f
r
o
m
an
u
n
d
er
l
oa
d
e
d
.
if
(
>
)
t
h
e
n
if
(
=
=
)
t
h
e
n
9.
Up
d
at
e
ove
rl
oa
d
ed
,
u
n
d
er
loa
d
e
d
a
n
d
b
ala
n
c
ed
s
et
o
f
10.
Ret
u
rn
re
s
u
lt
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esi
a
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
E
n
h
a
n
ce
me
n
t o
f c
lo
u
d
p
erfo
r
ma
n
ce
metrics
u
s
in
g
d
yn
a
mic
d
eg
r
ee
mem
o
r
y
... (
A
p
a
r
n
a
J
o
s
h
i
)
1701
Fig
u
r
e
3
.
C
lo
u
d
Si
m
an
d
it
s
co
m
p
o
n
en
ts
T
h
e
co
m
p
o
n
e
n
ts
o
f
C
lo
u
d
Si
m
u
s
ed
in
t
h
i
s
s
i
m
u
latio
n
a
n
d
its
f
u
n
ctio
n
s
ar
e
ex
p
lai
n
ed
as
:
-
C
lo
u
d
I
n
f
o
r
m
atio
n
Ser
v
ice
:
T
h
is
co
m
p
o
n
en
t r
e
g
is
ter
s
d
atac
en
ter
en
ti
t
y
a
n
d
d
is
co
v
er
s
t
h
e
r
eso
u
r
ce
s
-
Data
ce
n
ter
:
I
t
m
o
d
els t
h
e
co
r
e
in
f
r
a
s
tr
u
ct
u
r
e
lev
e
l ser
v
ice
s
(
h
ar
d
w
a
r
e)
,
w
h
ic
h
i
s
o
f
f
er
ed
b
y
clo
u
d
p
r
o
v
id
er
-
Data
ce
n
ter
B
r
o
k
er
:
I
t
m
o
d
els
th
e
b
r
o
k
er
w
h
ic
h
is
r
esp
o
n
s
i
b
le
f
o
r
m
ed
iati
n
g
n
e
g
o
tiatio
n
s
b
et
w
ee
n
clo
u
d
p
r
o
v
id
er
an
d
clo
u
d
u
s
er
-
Ho
s
t
:
I
t
m
o
d
els a
p
h
y
s
ical
s
er
v
er
-
VM
:
I
t
m
o
d
els a
v
ir
t
u
al
m
ac
h
i
n
e
w
h
ich
i
s
r
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clo
u
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h
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t
to
d
ea
l w
ith
t
h
e
clo
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d
let
s
-
C
lo
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d
let
:
I
t
m
o
d
el
s
th
e
clo
u
d
-
b
ased
ap
p
licatio
n
s
er
v
ice
-
VM
Sch
ed
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ler
:
T
h
is
is
a
n
ab
s
tr
ac
t
class
i
m
p
le
m
en
ted
b
y
h
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s
t
co
m
p
o
n
e
n
t
t
h
at
m
o
d
el
th
e
p
o
li
cies
r
eq
u
ir
ed
to
allo
ca
te
p
r
o
ce
s
s
o
r
co
r
e
to
VMs
.
I
t r
u
n
s
o
n
ev
er
y
h
o
s
t in
d
atac
e
n
te
r
-
C
lo
u
d
letSc
h
ed
u
ler
D
y
n
a
m
icW
o
r
k
lo
ad
:
T
h
is
class
i
m
p
le
m
e
n
ts
a
p
o
lic
y
o
f
s
c
h
ed
u
l
in
g
p
er
f
o
r
m
ed
b
y
VMs
.
T
h
is
class
in
h
er
its
C
lo
u
d
Si
m
“
C
l
oudl
e
tSch
e
du
l
e
r
T
ime
Sha
r
e
d
”
class
w
h
ic
h
allo
ca
te
task
s
to
VMs
f
o
r
a
f
i
x
ed
p
er
io
d
o
f
ti
m
e
[
1
]
.
-
R
A
M
&
M
I
P
SV
m
Allo
ca
tio
n
:
T
h
is
class
a
llo
ca
tes
VMs
to
th
e
h
o
s
ts
.
I
t
i
n
h
er
its
C
lo
u
d
Si
m
“
VmAl
l
oc
a
tion
Po
l
ic
y
”
w
h
ic
h
i
s
a
n
ab
s
tr
ac
t
cla
s
s
.
T
h
i
s
clas
s
h
o
ld
s
in
ter
n
al
m
et
h
o
d
o
f
C
lo
u
d
Si
m
w
h
ic
h
ta
k
e
s
in
p
u
t a
s
VMs
to
b
e
allo
ca
ted
an
d
c
h
o
o
s
e
id
ea
l h
o
s
ts
b
ased
o
n
R
A
M
an
d
M
IPS
v
alu
e
s
.
T
h
e
VM
allo
ca
tio
n
o
f
p
r
o
p
o
s
ed
alg
o
r
ith
m
i
s
ad
d
ed
in
th
i
s
clas
s
.
T
h
is
class
h
o
ld
s
t
w
o
d
ata
s
tr
u
c
tu
r
e
i.e
.
VmT
a
b
l
e
w
h
ic
h
m
ap
s
ev
er
y
VM
to
its
allo
ca
ted
h
o
s
t a
n
d
M
a
pT
a
b
l
e
w
h
ic
h
s
to
r
e
i
n
f
o
r
m
atio
n
o
f
a
v
ailab
le
R
A
M
an
d
M
IPS
o
f
ea
ch
h
o
s
t
.
T
ab
le
1
g
iv
es
p
ar
a
m
ete
r
s
an
d
s
p
ec
if
icatio
n
s
o
f
Vir
t
u
al
m
ac
h
i
n
e,
d
atac
en
ter
an
d
ta
s
k
s
u
s
ed
in
t
h
e
s
i
m
u
lat
io
n
i
n
d
etails.
T
ab
le
1
.
A
s
et
o
f
p
ar
a
m
eter
s
c
o
n
s
id
er
ed
f
o
r
an
al
y
s
is
S
i
mu
l
a
t
i
o
n
P
a
r
a
me
t
e
r
V
a
l
u
e
V
i
r
t
u
a
l
M
a
c
h
i
n
e
s
T
o
t
a
l
n
u
m
b
e
r
o
f
V
M
s
P
r
o
c
e
ss
i
n
g
sp
e
e
d
(
M
I
P
S
)
N
u
mb
e
r
o
f
P
E
p
e
r
V
M
R
A
M
(
M
B
)
B
a
n
d
w
i
d
t
h
(
M
b
p
s)
V
M
M
a
n
a
g
e
r
O
p
e
r
a
t
i
n
g
sy
st
e
m
V
a
r
y
i
n
g
R
a
n
d
o
m
1
-
5
n
o
s
R
a
n
d
o
m
R
a
n
d
o
m
X
e
n
L
i
n
u
x
C
l
o
u
d
l
e
t
s
T
o
t
a
l
n
u
m
b
e
r
o
f
t
a
s
k
s
L
e
n
g
t
h
o
f
t
a
s
k
(
M
I
)
F
i
l
e
si
z
e
(
M
B
)
O
u
t
p
u
t
s
i
z
e
(
M
B
)
60
-
8
0
n
o
s
R
a
n
d
o
m
3
0
0
3
0
0
D
a
t
a
c
e
n
t
e
r
N
o
.
o
f
d
a
t
a
c
e
n
t
e
r
N
o
.
o
f
h
o
st
s
1
2
4.
M
AT
H
E
M
AT
I
CAL M
O
DE
L
D
y
n
a
m
ic
d
eg
r
ee
m
e
m
o
r
y
b
ala
n
ce
d
allo
ca
tio
n
(
D2
MB
A
)
al
g
o
r
ith
m
f
o
r
lo
ad
b
alan
ci
n
g
w
h
i
ch
allo
ca
te
VM
to
a
b
est
s
u
itab
le
h
o
s
t,
b
ased
o
n
av
ailab
ilit
y
o
f
R
A
M
&
MI
P
S
o
f
h
o
s
t.
I
n
ad
d
itio
n
,
D2
MB
A
alg
o
r
it
h
m
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2502
-
4752
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
22
,
No
.
3
,
J
u
n
e
2
0
2
1
:
1
6
9
7
-
1
7
0
7
1702
allo
ca
te
task
to
a
b
est
s
u
itab
le
VM
b
y
co
n
s
id
er
in
g
a
b
alan
ce
d
co
n
d
itio
n
o
f
VM
.
Data
ce
n
ter
,
h
o
s
t,
v
ir
tu
a
l
m
ac
h
in
e
a
n
d
tas
k
s
ar
e
th
e
ele
m
en
ts
o
f
D2
MB
A
al
g
o
r
ith
m
.
T
h
e
task
s
h
a
v
e
co
n
s
id
er
ed
ar
e
n
o
n
-
p
r
ee
m
p
ti
v
e
tas
k
s
i.e
tas
k
c
an
n
o
t
b
e
i
n
ter
r
u
p
ted
.
L
et
n
o
n
-
p
r
ee
m
p
tiv
e
task
s
=
{
1
,
2
…
}
b
e
th
e
s
et
o
f
task
w
h
ich
s
h
o
u
ld
b
e
p
r
o
ce
s
s
o
n
v
ir
tu
al
m
ac
h
i
n
e
r
ep
r
esen
ted
b
y
={
1
,
2
, …
}.
T
h
ese
ar
e
ass
ig
n
ed
o
n
s
u
itab
le
h
o
s
t b
ased
o
n
a
v
ail
ab
ilit
y
o
f
R
AM
&
MI
P
S
o
f
h
o
s
t
s
.
O
u
r
ai
m
is
to
i
m
p
r
o
v
e
p
er
f
o
r
m
an
ce
o
f
s
y
s
te
m
b
y
c
o
n
s
id
er
in
g
e
v
alu
a
tio
n
p
ar
a
m
e
t
er
s
u
c
h
as d
e
g
r
ee
o
f
i
m
b
alan
ce
(
DI
)
,
ex
ec
u
tio
n
co
s
t
(
E
C
)
an
d
m
ak
esp
a
n
ti
m
e.
T
h
is
ev
alu
at
io
n
p
ar
a
m
eter
ca
n
b
e
r
ep
r
esen
ted
b
y
in
m
o
d
el.
So
,
p
r
o
p
o
s
ed
m
o
d
el
ca
n
b
e
r
ep
r
esen
ted
as
|
|
.
5.
CO
M
P
L
E
XIT
Y
AN
D
WO
R
K
F
L
O
W
ANAL
YSI
S O
F
P
RO
P
O
SE
D
AL
G
O
R
I
T
H
M
T
h
is
s
ec
tio
n
e
x
p
lain
s
co
m
p
l
ex
it
y
a
n
d
w
o
r
k
f
lo
w
a
n
al
y
s
is
o
f
p
r
o
p
o
s
ed
D2
MB
A
alg
o
r
ith
m
.
T
h
e
co
m
p
le
x
it
y
o
f
t
h
e
s
ch
ed
u
li
n
g
a
lg
o
r
ith
m
m
a
y
h
a
v
e
s
o
m
e
e
f
f
ec
t
o
n
t
h
e
s
y
s
te
m
.
T
h
e
al
g
o
r
ith
m
’
s
ti
m
e
co
m
p
le
x
it
y
is
r
elate
d
to
th
e
n
u
m
b
er
o
f
v
ir
tu
al
m
ac
h
i
n
es a
n
d
th
e
n
u
m
b
er
o
f
task
s
[
2
5
]
.
W
h
ile
D2
MB
A
alg
o
r
ith
m
d
o
es
n
o
t u
t
ilize
p
r
io
r
it
y
m
et
h
o
d
,
its
ti
m
e
co
m
p
lex
it
y
r
e
m
ai
n
s
to
.
Fo
r
s
p
ac
e
co
m
p
le
x
it
y
,
tas
k
s
c
h
ed
u
li
n
g
an
d
VM
s
s
c
h
ed
u
lin
g
i
s
b
o
th
1
.
So
,
th
e
to
tal
s
p
ac
e
co
m
p
le
x
it
y
is
1
.
T
h
e
s
ch
ed
u
l
in
g
m
et
h
o
d
in
th
i
s
p
ap
er
is
s
i
m
p
le
a
n
d
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in
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if
f
er
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tial
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in
te
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ca
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ith
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g
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r
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u
r
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e
p
e
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an
c
e
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f
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ith
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s
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g
u
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e
4
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.
D
eg
r
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e
o
f
im
b
a
la
n
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e
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c
a
l
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la
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d
u
s
in
g
an
ex
p
r
es
s
i
o
n
g
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in
(
3
)
a
n
d
ex
e
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t
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al
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l
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u
s
in
g
an
ex
p
r
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s
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io
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g
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en
in
(
4
)
.
W
h
e
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ea
s
,
m
ak
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a
n
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im
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s
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al
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l
at
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u
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g
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.
Deg
r
ee
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f
I
m
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ala
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3
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w
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eter
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E
x
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=
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w
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6.
RE
SU
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D
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ith
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s
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ased
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m
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1
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Ca
s
e
1
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ase
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I
n
th
is
ca
s
e,
v
ar
iatio
n
in
th
e
t
h
r
ee
p
er
f
o
r
m
a
n
ce
p
ar
a
m
eter
s
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s
t,
d
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r
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f
i
m
b
alan
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an
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m
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k
esp
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n
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m
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f
o
r
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th
e
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r
ith
m
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R
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P
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ased
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d
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1
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th
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s
e,
f
ir
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w
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s
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n
m
a
k
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r
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ted
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a.
Var
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s
i
n
ex
ec
u
tio
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co
s
t
Var
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s
in
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h
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in
t
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task
s
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s
h
o
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n
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n
th
e
T
ab
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2
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d
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ted
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Fig
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5
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2
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iv
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s
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tio
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e
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ith
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is
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er
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at
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o
r
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h
m
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m
th
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ased
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ith
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m
Fig
u
r
e
5
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ased
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ith
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h
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f
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ec
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s
t
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ec
r
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es
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y
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8
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w
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,
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it
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h
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ith
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n
th
e
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f
r
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m
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0
0
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o
s
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h
u
s
,
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t
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o
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er
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t
h
at
t
h
e
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MB
A
al
g
o
r
ith
m
h
as
lo
w
est
v
al
u
e
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f
ex
ec
u
tio
n
co
s
t f
o
r
s
m
al
ler
n
u
m
b
er
o
f
tas
k
s
.
b.
Var
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s
i
n
th
e
d
e
g
r
ee
o
f
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m
b
alan
ce
I
n
th
is
s
ec
tio
n
,
r
e
s
u
l
ts
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n
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ar
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tio
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s
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n
th
e
d
e
g
r
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f
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m
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ala
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e
w
ith
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in
cr
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s
e
in
t
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m
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w
er
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s
h
o
w
n
i
n
T
ab
le
3
an
d
p
r
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ted
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n
t
h
e
Fig
u
r
e
6
.
T
ab
le
3
g
iv
e
s
a
v
al
u
e
o
f
t
h
e
d
eg
r
ee
o
f
i
m
b
ala
n
ce
f
o
r
all
th
e
t
h
r
ee
al
g
o
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ith
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s
.
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t
is
o
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er
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ed
th
at
t
h
e
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o
r
it
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m
h
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lo
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s
t
v
al
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es
f
o
r
d
eg
r
ee
o
f
i
m
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a
lan
ce
.
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m
th
e
T
ab
le
3
,
it
is
o
b
s
er
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at,
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m
r
ed
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s
d
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m
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y
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n
av
er
ag
e
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1
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8
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s
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m
p
ar
ed
to
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R
a
n
d
4
3
.
3
5
%
as
co
m
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ar
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to
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B
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C
P
U
b
ased
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o
r
ith
m
.
Fro
m
F
ig
u
r
e
6
,
it
is
o
b
s
er
v
ed
t
h
at,
i
n
ca
s
e
o
f
D2
B
_
C
P
U
b
ased
an
d
D2
MB
A
alg
o
r
ith
m
s
(
p
r
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p
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s
ed
alg
o
r
ith
m
)
,
d
eg
r
ee
o
f
i
m
b
alan
ce
r
em
ai
n
s
m
o
r
e
o
r
less
co
n
s
tan
t w
it
h
an
i
n
cr
ea
s
e
in
t
h
e
n
u
m
b
er
o
f
ta
s
k
s
f
r
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m
1
0
0
to
1
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0
0
n
o
s
.
I
n
ca
s
e
o
f
R
R
al
g
o
r
ith
m
,
d
eg
r
ee
o
f
i
m
b
alan
ce
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s
f
ar
h
i
g
h
er
t
h
an
t
h
e
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th
er
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w
o
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g
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r
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iz.
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B
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C
P
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ased
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d
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ith
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ls
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er
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at
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ased
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ased
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ase
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Fig
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ased
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I
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d
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n
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o
th
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k
ee
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g
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co
n
s
tan
t
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n
d
v
a
r
y
in
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ta
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s
(
C
ase
1
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k
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n
s
tan
t
an
d
v
ar
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n
g
VM
s
(
C
a
s
e
2
)
,
it
is
o
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s
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v
ed
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at
t
h
e
D2
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al
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ith
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lar
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m
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ar
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it
h
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an
d
D2
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_
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P
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ased
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o
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ith
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s
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o
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th
e
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o
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it
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m
h
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ti
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e
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m
p
ar
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w
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R
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P
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ased
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o
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ith
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s
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h
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Dy
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ic
Deg
r
ee
Me
m
o
r
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ala
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ce
d
A
llo
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tio
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(
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alg
o
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ith
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ar
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as c
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ar
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ased
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o
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ith
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s
.
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NO
WL
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D
G
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E
NT
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Au
t
h
o
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Dep
ar
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e
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t
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p
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ter
Scie
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ce
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d
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n
g
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n
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Vel
T
ec
h
R
an
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ar
aj
an
Dr
.
Sag
u
n
th
ala
R
&
D
I
n
s
tit
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te
o
f
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ce
a
n
d
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ec
h
n
o
lo
g
y
C
h
e
n
n
ai,
I
n
d
ia
a
n
d
A
r
m
y
I
n
s
tit
u
t
e
o
f
T
ec
h
n
o
lo
g
y
f
o
r
p
r
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v
id
in
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a
n
in
f
r
astr
u
ct
u
r
e
to
ca
r
r
y
r
esear
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h
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n
ab
o
v
e
m
e
n
ti
o
n
ed
to
p
ic
.
RE
F
E
R
E
NC
E
S
[1
]
R.
Bu
y
y
a
,
C.
S
.
Ye
o
,
S
.
V
e
n
u
g
o
p
a
l,
J.
Bro
b
e
rg
,
a
n
d
I.
Bra
n
d
ic,
“
Clo
u
d
c
o
m
p
u
ti
n
g
a
n
d
e
m
e
r
g
in
g
IT
p
l
a
tf
o
r
m
s:
V
isio
n
,
H
y
p
e
a
n
d
re
a
li
ty
f
o
r
d
e
li
v
e
rin
g
c
o
m
p
u
ti
n
g
a
s th
e
5
th
u
ti
li
ty
,
”
Fu
tu
r
e
Ge
n
e
ra
ti
o
n
o
f
C
o
mp
u
ter
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y
ste
m
,
v
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l.
2
5
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o
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p
.
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.
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]
S
.
Af
z
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l
a
n
d
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.
k
a
v
it
h
a
,
“
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o
a
d
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a
lan
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in
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lo
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o
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p
u
ti
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g
-
A
h
iera
rc
h
ica
l
tax
o
n
o
m
ica
l
c
las
si
f
ic
a
ti
o
n
,
”
J
o
u
r
n
a
l
o
f
Clo
u
d
Co
m
p
u
ti
n
g
,
v
o
l.
8
,
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o
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p
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3
6
7
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.
[3
]
B
.
P
.
M
u
ll
a
,
C
.
R
.
Kris
h
n
a
,
a
n
d
R
.
K
.
T
ick
o
o
,
“
L
o
a
d
b
a
lan
c
in
g
a
lg
o
ri
th
m
f
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r
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ff
icie
n
t
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a
ll
o
c
a
ti
o
n
i
n
h
e
tero
g
e
n
e
o
u
s
c
lo
u
d
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Co
mp
u
ter
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two
rk
a
n
d
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o
mm
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n
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v
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2
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o
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.
[4
]
S
.
K
.
M
ish
ra
,
B
.
sa
h
o
o
,
a
n
d
P
.
P
.
P
a
ri
d
a
,
“
L
o
a
d
b
a
lan
c
i
n
g
in
c
l
o
u
d
c
o
m
p
u
ti
n
g
:
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b
ig
p
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re
,
”
J
o
u
rn
a
l
o
f
Kin
g
S
a
u
d
Un
ive
rs
it
y
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Co
mp
u
ter
a
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d
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fo
rm
a
ti
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3
.
[5
]
Y
.
A
/P
P
a
rm
e
si
v
a
n
,
S
.
Ha
sa
n
,
a
n
d
A
.
M
u
h
a
m
m
e
d
,
“
P
e
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rm
a
n
c
e
Ev
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lu
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ti
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lo
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d
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lan
c
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n
g
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lg
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m
f
o
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rtu
a
l
m
a
c
h
in
e
in
d
a
ta
c
e
n
tre
in
c
lo
u
d
c
o
m
p
u
ti
n
g
,
”
I
n
ter
n
a
t
io
n
a
l
J
o
u
r
n
a
l
o
f
En
g
i
n
e
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&
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c
h
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o
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y
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.
7
,
n
o
.
4
,
p
p
:
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,
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0
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8
[6
]
E
.
J
.
G
h
o
m
i,
A
.
M
.
Ra
h
m
a
n
i,
a
n
d
N
.
N
.
Qa
d
e
r,
“
L
o
a
d
b
a
lan
c
i
n
g
a
lg
o
rit
h
m
s
in
c
lo
u
d
c
o
m
p
u
ti
n
g
:
a
su
rv
e
y
,
”
J
o
u
rn
a
l
o
f
Ne
two
rk
Co
m
p
u
ter
A
p
p
li
c
a
ti
o
n
,
v
o
l.
8
0
,
p
p
.
5
0
-
7
1
,
2
0
1
7
,
h
tt
p
s:
//
d
o
i.
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rg
/1
0
.
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0
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6
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.
jn
c
a
.
2
0
1
7
.
0
4
.
0
0
7
.
[7
]
S
.
R
.
G
u
n
d
u
,
C
.
A
.
P
a
n
e
m
,
a
n
d
A
.
T
h
imm
a
p
u
ra
m
,
“
Re
a
l
-
T
i
m
e
Clo
u
d
-
Ba
se
d
l
o
a
d
b
a
lan
c
e
a
lg
o
rit
h
m
s
a
n
d
a
n
an
a
ly
sis
,
”
S
N
Co
mp
u
ter
S
c
ien
c
e
,
v
o
l
1
,
p
p
:
1
-
9
,
2
0
2
0
.
[8
]
A
.
Jo
sh
i,
M
.
S
.
De
v
i,
“
A
S
u
rv
e
y
o
f
Jo
b
S
c
h
e
d
u
li
n
g
A
l
g
o
rit
h
m
s
f
o
r
L
o
a
d
Ba
lan
c
in
g
in
Ha
d
o
o
p
En
v
iro
n
m
e
n
t
,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
P
u
re
a
n
d
Ap
p
l
ied
M
a
t
h
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ma
ti
c
s
,
v
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l
.
1
1
9
,
n
o
.
1
6
,
p
p
.
5
0
3
3
-
5
0
4
6
,
2
0
1
8
.
[9
]
G
.
G
o
p
in
a
th
P
P
,
a
n
d
S
.
K
.
V
a
su
d
e
v
a
n
,
“
A
n
in
-
d
e
p
th
a
n
a
ly
sis
a
n
d
stu
d
y
o
f
lo
a
d
b
a
lan
c
in
g
tec
h
n
i
q
u
e
s
in
t
h
e
c
lo
u
d
c
o
m
p
u
ti
n
g
e
n
v
iro
n
m
e
n
t
,
”
Pro
c
e
d
ia
Co
m
p
u
ter
S
c
ien
c
e
,
v
o
l.
5
0
,
pp:
4
2
7
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4
3
2
,
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0
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5
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o
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tt
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s.2
0
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5
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0
4
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.
[1
0
]
M
.
A
.
S
h
a
h
i
d
,
N.
Isla
m
,
M
.
M
.
A
la
m
,
M
.
M
.
S
u
’
u
d
a
n
d
S
.
M
u
s
a
,
“
A
Co
m
p
re
h
e
n
siv
e
S
tu
d
y
o
f
L
o
a
d
Ba
lan
c
in
g
A
p
p
ro
a
c
h
e
s
in
th
e
Clo
u
d
Co
m
p
u
ti
n
g
E
n
v
iro
n
m
e
n
t
a
n
d
a
N
o
v
e
l
F
a
u
lt
T
o
lera
n
c
e
A
p
p
ro
a
c
h
,
”
IEE
E
Acc
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ss
,
v
o
l.
8
,
p
p
.
1
3
0
5
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0
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S
.
2
0
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[1
1
]
J
.
L
i,
L
.
F
e
n
g
,
a
n
d
S
.
F
a
n
g
,
“
A
n
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y
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ti
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”
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sw
.
9
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4
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-
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2
5
.
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