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ticle
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R
ev
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Oct
15
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2
0
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4
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ted
Oct
30
,
2
0
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P
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Gs
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d
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t
tas
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s
t
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t
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re
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ted
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d
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e
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s
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g
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d
m
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tary
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e
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o
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a
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in
tro
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c
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a
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e
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a
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ter
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s
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CAWS
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ted
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sin
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h
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In
sp
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ic
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d
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Re
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lt
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t
ECAWS
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t
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c
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k
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n
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c
o
sts,
a
n
d
e
n
e
rg
y
c
o
n
su
m
p
ti
o
n
.
K
ey
w
o
r
d
s
:
C
lo
u
d
co
m
p
u
tin
g
C
o
s
t r
ed
u
ctio
n
E
n
er
g
y
ef
f
icien
cy
Ma
k
esp
an
ef
f
icien
c
y
R
eso
u
r
ce
p
r
o
v
is
io
n
in
g
W
o
r
k
lo
ad
s
ch
ed
u
lin
g
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Ma
n
ju
n
ath
a
Sh
iv
a
n
an
d
a
p
p
a
Dep
ar
tm
en
t o
f
C
o
m
p
u
ter
Scie
n
ce
an
d
E
n
g
in
ee
r
in
g
B
NM
I
n
s
titu
te
o
f
T
ec
h
n
o
lo
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af
f
iliated
with
Vis
v
esv
ar
ay
a
T
ec
h
n
o
lo
g
ical
Un
iv
er
s
ity
B
an
g
alo
r
e,
Kar
n
atak
a
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I
n
d
ia
E
m
ail: m
an
ju
n
ath
s
@
b
n
m
it.in
1.
I
NT
RO
D
UCT
I
O
N
C
lo
u
d
co
m
p
u
tin
g
[
1
]
,
co
u
p
led
with
v
ir
tu
aliza
tio
n
tech
n
o
lo
g
y
,
o
p
en
s
u
p
ex
te
n
s
iv
e
r
esear
ch
o
p
p
o
r
tu
n
ities
ac
r
o
s
s
n
u
m
er
o
u
s
d
o
m
ain
s
a
n
d
a
p
p
licatio
n
s
.
As
g
lo
b
al
d
ata
ex
p
an
d
s
,
th
e
n
ee
d
f
o
r
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to
m
ated
d
ata
p
r
o
ce
s
s
in
g
is
in
cr
ea
s
in
g
l
y
ap
p
ar
e
n
t.
T
h
is
is
p
ar
ticu
lar
l
y
r
elev
an
t
in
f
ield
s
s
u
ch
as
B
io
in
f
o
r
m
atics
an
d
Ast
r
o
n
o
m
y
,
wh
er
e
s
u
b
s
tan
tial
d
ata
is
g
ath
er
ed
f
o
r
r
esear
ch
p
u
r
p
o
s
es.
Of
ten
,
th
is
d
ata
is
m
an
ag
ed
as
s
cien
tific
wo
r
k
lo
ad
s
.
Scien
tific
wo
r
k
lo
ad
s
[
2
]
,
wh
ic
h
ar
e
f
r
eq
u
en
tly
m
o
d
eled
as
d
ir
ec
ted
ac
y
clic
g
r
ap
h
s
(
DAGs)
,
in
v
o
lv
e
in
ter
d
ep
en
d
en
t
task
s
th
at
co
m
m
u
n
icate
v
ia
f
ile
e
x
ch
an
g
es.
T
h
e
o
u
t
p
u
t
f
r
o
m
o
n
e
task
o
f
ten
s
er
v
es
as
th
e
in
p
u
t
f
o
r
an
o
th
er
.
T
h
ese
wo
r
k
lo
ad
s
ca
n
co
m
p
r
is
e
th
o
u
s
an
d
s
o
f
task
s
an
d
ar
e
ty
p
ically
ex
ec
u
ted
o
n
lar
g
e
-
s
ca
le
p
ar
allel
o
r
d
is
tr
ib
u
ted
s
y
s
tem
s
,
in
clu
d
in
g
cl
o
u
d
c
o
m
p
u
tatio
n
al
p
latf
o
r
m
s
[
3
]
.
Su
ch
s
y
s
tem
s
allo
w
f
o
r
p
ar
allel
p
r
o
ce
s
s
in
g
o
f
in
d
ep
en
d
en
t
task
s
,
th
er
eb
y
r
ed
u
cin
g
o
v
er
all
c
o
s
ts
an
d
ex
ec
u
tio
n
ti
m
es
(
m
a
k
esp
an
)
.
Ho
wev
er
,
s
ch
ed
u
lin
g
th
ese
task
s
in
clo
u
d
en
v
ir
o
n
m
e
n
ts
is
a
co
m
p
lex
,
n
o
n
-
p
o
l
y
n
o
m
ial
p
r
o
b
le
m
[
4
]
.
T
o
a
d
d
r
ess
th
is
,
p
latf
o
r
m
s
lik
e
clo
u
d
s
im
[
5
]
,
[
6
]
,
a
n
d
e
d
g
e
-
wo
r
k
lo
a
d
[
7
]
s
c
h
ed
u
ler
s
h
a
v
e
r
ec
en
tly
b
ee
n
e
m
p
lo
y
ed
to
m
an
ag
e
in
co
m
in
g
task
s
as sh
o
w
n
in
Fi
g
u
r
e
1
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
E
n
erg
y
a
n
d
co
s
t
-
a
w
a
r
e
w
o
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a
d
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ch
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ler
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r
h
etero
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Ma
n
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t
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a
S
h
iv
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n
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a
p
p
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)
547
Fig
u
r
e
1
.
T
h
e
ar
ch
itectu
r
e
o
f
wo
r
k
f
lo
w
s
ch
ed
u
lin
g
in
a
h
o
m
o
g
en
o
u
s
clo
u
d
p
latf
o
r
m
[
2
]
Var
io
u
s
alg
o
r
ith
m
s
h
av
e
b
ee
n
d
ev
elo
p
e
d
f
o
r
task
s
ch
ed
u
l
in
g
[
8
]
.
T
h
ese
in
clu
d
e
p
ar
tic
le
s
war
m
o
p
tim
izatio
n
(
PS
O)
[
9
]
,
an
t
c
o
lo
n
y
o
p
tim
izatio
n
(
AC
O)
[
1
0
]
,
h
eter
o
g
e
n
eo
u
s
ea
r
lies
t
tim
e
f
ir
s
t
(
HE
FT)
[
1
1
]
,
en
h
an
ce
d
HE
FT
[
1
2
]
,
an
d
en
er
g
y
-
c
o
s
t
-
awa
r
e
[
1
3
]
s
ch
ed
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l
er
s
em
p
lo
y
in
g
d
if
f
e
r
en
t
o
p
ti
m
izatio
n
s
tr
ateg
ies.
Mo
r
e
d
etails
o
f
d
if
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e
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t
s
ch
e
d
u
lin
g
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eth
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d
s
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e
b
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s
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s
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ed
in
s
ec
tio
n
2
.
W
h
ile
t
h
ese
m
eth
o
d
s
[
1
4
]
,
[
1
5
]
h
av
e
im
p
r
o
v
e
d
p
er
f
o
r
m
a
n
ce
,
th
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all
s
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o
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wh
e
n
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g
with
lar
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e
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tific
p
ar
allel
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r
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d
s
.
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h
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d
t
o
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tr
u
g
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le
with
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ed
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o
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ak
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u
r
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m
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.
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n
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h
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o
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u
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e
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o
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el:
th
e
en
er
g
y
an
d
co
s
t
-
awa
r
e
wo
r
k
lo
a
d
s
ch
ed
u
le
r
(
E
C
AW
S).
T
h
is
m
o
d
el
is
d
esig
n
ed
f
o
r
p
ar
allel
wo
r
k
lo
ad
e
x
ec
u
tio
n
in
h
eter
o
g
en
eo
u
s
clo
u
d
e
n
v
ir
o
n
m
en
t
s
.
I
ts
p
r
im
ar
y
g
o
als
ar
e
to
m
in
im
ize
en
er
g
y
co
n
s
u
m
p
tio
n
,
r
ed
u
ce
co
s
ts
,
an
d
m
ee
t ta
s
k
d
ea
d
lin
es (
m
a
k
esp
an
)
.
T
h
e
s
ig
n
if
ican
ce
o
f
o
u
r
r
ese
ar
ch
lies
in
its
d
ev
elo
p
m
en
t
o
f
a
s
ch
ed
u
ler
m
o
d
el
th
at
en
h
an
ce
s
p
er
f
o
r
m
an
ce
b
y
lo
wer
in
g
m
ak
esp
an
,
en
er
g
y
u
s
ag
e,
an
d
co
m
p
u
tatio
n
al
co
s
ts
f
o
r
s
cien
tific
p
ar
allel
wo
r
k
lo
a
d
s
.
W
e
co
n
d
u
ct
a
co
m
p
ar
ativ
e
a
n
aly
s
is
o
f
v
ar
io
u
s
wo
r
k
lo
ad
s
c
h
ed
u
ler
m
o
d
els
th
at
u
s
e
d
if
f
er
e
n
t
m
eth
o
d
o
lo
g
ies
f
o
r
m
an
a
g
in
g
s
cien
tific
wo
r
k
l
o
ad
s
.
T
h
e
r
esu
lts
d
em
o
n
s
tr
ate
th
at
E
C
AW
S
s
ig
n
if
ican
tly
im
p
r
o
v
es
p
er
f
o
r
m
a
n
ce
in
ter
m
s
o
f
co
s
t r
e
d
u
ctio
n
,
en
e
r
g
y
ef
f
icien
cy
,
a
n
d
m
a
k
esp
an
r
ed
u
ctio
n
.
T
h
e
s
tr
u
ctu
r
e
o
f
t
h
e
p
a
p
er
is
as
f
o
llo
ws:
s
ec
tio
n
2
r
ev
iews
d
if
f
er
en
t
m
o
d
els,
a
lg
o
r
ith
m
s
,
ar
ch
itectu
r
es,
an
d
m
eth
o
d
o
lo
g
ies
ap
p
lied
to
th
e
ex
ec
u
tio
n
o
f
s
cien
tific
wo
r
k
lo
ad
s
.
Sectio
n
3
in
tr
o
d
u
ce
s
o
u
r
p
r
o
p
o
s
ed
ar
ch
itectu
r
e
an
d
r
eso
u
r
ce
p
r
o
v
is
io
n
in
g
m
o
d
el,
f
o
cu
s
in
g
o
n
r
eso
u
r
ce
allo
ca
tio
n
f
o
r
h
an
d
lin
g
d
ata
-
in
ten
s
iv
e
s
cien
t
if
ic
task
s
.
Sec
tio
n
4
p
r
esen
ts
an
e
v
alu
atio
n
o
f
th
e
r
esu
lts
an
d
a
co
m
p
a
r
is
o
n
with
e
x
is
tin
g
m
o
d
els.
Fin
ally
,
s
ec
tio
n
5
o
f
f
e
r
s
a
co
n
cise c
o
n
clu
s
io
n
s
u
m
m
ar
izin
g
th
e
r
esear
c
h
f
in
d
i
n
g
s
.
2.
RE
L
AT
E
D
WO
RK
T
h
is
s
ec
tio
n
s
tu
d
ies
d
if
f
er
en
t
wo
r
k
lo
ad
s
ch
e
d
u
lin
g
f
o
r
clo
u
d
an
d
e
d
g
e
-
clo
u
d
p
latf
o
r
m
s
[
1
4
]
,
[
1
5
]
.
Yao
et
a
l.
[
1
6
]
in
t
r
o
d
u
ce
d
a
task
-
d
u
p
licatio
n
-
b
ased
s
ch
ed
u
lin
g
alg
o
r
ith
m
(
T
DSA)
aim
e
d
at
r
e
d
u
cin
g
co
s
ts
an
d
m
a
k
esp
an
with
in
cl
o
u
d
e
n
v
ir
o
n
m
en
ts
.
T
h
eir
ap
p
r
o
ac
h
in
cl
u
d
es
two
p
r
im
ar
y
m
eth
o
d
s
an
d
was
test
ed
o
n
b
o
th
r
an
d
o
m
an
d
s
cien
tific
w
o
r
k
lo
ad
s
.
T
h
e
r
esu
lts
in
d
icate
d
a
3
1
.
6
%
r
ed
u
ctio
n
in
co
s
t
an
d
a
1
7
.
4
%
d
ec
r
ea
s
e
in
m
ak
esp
an
.
Sin
d
h
u
et
a
l.
[
1
7
]
,
ad
d
itio
n
ally
,
th
e
alg
o
r
it
h
m
ad
d
r
ess
es
en
er
g
y
co
n
s
u
m
p
tio
n
a
n
d
o
v
er
all
co
m
p
u
tatio
n
al
c
o
s
ts
,
en
h
an
cin
g
s
y
s
tem
p
er
f
o
r
m
an
ce
in
ed
g
e
-
f
o
g
c
o
m
p
u
tin
g
en
v
ir
o
n
m
en
t
s
.
I
t
u
tili
ze
s
DAGs
f
o
r
task
s
ch
ed
u
lin
g
an
d
in
c
o
r
p
o
r
ates
a
Ma
r
k
o
v
d
ec
is
io
n
p
r
o
ce
s
s
f
o
r
o
p
tim
al
r
eso
u
r
ce
allo
ca
tio
n
.
T
h
e
alg
o
r
ith
m
d
e
m
o
n
s
tr
ated
s
u
p
er
i
o
r
p
er
f
o
r
m
a
n
ce
co
m
p
ar
ed
t
o
e
x
is
tin
g
m
o
d
els.
Ab
o
h
am
am
a
et
a
l.
[
1
8
]
,
d
ev
el
o
p
ed
a
task
-
s
ch
ed
u
lin
g
alg
o
r
it
h
m
f
o
r
clo
u
d
-
f
o
g
p
latf
o
r
m
s
,
f
r
am
in
g
th
e
s
ch
ed
u
lin
g
p
r
o
b
lem
as
a
p
er
m
u
tatio
n
-
b
ased
o
p
tim
izatio
n
ch
allen
g
e.
T
h
ey
em
p
lo
y
ed
a
n
en
h
an
ce
d
g
en
etic
alg
o
r
ith
m
(
GA)
to
allo
ca
te
task
s
to
v
ir
tu
a
l
m
ac
h
in
es
with
o
p
tim
al
r
eso
u
r
ce
s
an
d
ex
e
cu
tio
n
tim
es.
T
h
ei
r
ex
p
er
im
en
ts
,
co
m
p
ar
in
g
th
e
p
r
o
p
o
s
ed
al
g
o
r
ith
m
with
m
eth
o
d
s
s
u
ch
as
b
est
-
f
it,
f
i
r
s
t
-
f
it,
b
ee
s
'
life
alg
o
r
ith
m
,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
38
,
No
.
1
,
Ap
r
il
20
25
:
5
4
6
-
5
5
4
548
an
d
GA
,
s
h
o
we
d
im
p
r
o
v
em
e
n
ts
in
co
s
t,
to
tal
c
o
m
p
u
tatio
n
tim
e,
an
d
f
ailu
r
e
r
ate.
Mo
v
ah
e
d
i
et
a
l.
[
1
9
]
p
r
o
p
o
s
ed
a
task
s
ch
ed
u
lin
g
m
o
d
el
d
esig
n
e
d
to
m
in
im
ize
en
er
g
y
co
n
s
u
m
p
tio
n
a
n
d
e
x
ec
u
tio
n
tim
e
in
f
o
g
co
m
p
u
tin
g
p
latf
o
r
m
s
.
T
h
eir
a
p
p
r
o
ac
h
in
clu
d
es
an
a
r
ch
itectu
r
e
f
o
r
m
an
ag
in
g
in
co
m
in
g
t
ask
s
an
d
em
p
lo
y
s
in
teg
er
-
lin
ea
r
p
r
o
g
r
am
m
in
g
(
I
L
P)
alo
n
g
s
id
e
a
ch
ao
tic
wh
ale
o
p
tim
izatio
n
alg
o
r
ith
m
.
C
o
m
p
ar
is
o
n
s
with
GA
,
ar
tific
ial
-
b
ee
-
co
lo
n
y
alg
o
r
ith
m
s
,
an
d
PSO
r
ev
ea
led
th
at
t
h
e
ir
m
o
d
el
o
u
tp
er
f
o
r
m
e
d
th
ese
e
x
is
tin
g
s
y
s
tem
s
.
Sh
ash
an
k
et
a
l.
[
2
0
]
in
tr
o
d
u
ce
d
a
d
ee
p
r
ein
f
o
r
ce
m
e
n
t
le
ar
n
in
g
(
DR
L
)
alg
o
r
ith
m
f
o
r
I
o
T
ta
s
k
s
ch
ed
u
lin
g
in
f
o
g
-
b
ased
e
n
v
ir
o
n
m
en
ts
.
T
h
ei
r
m
eth
o
d
ad
d
r
es
s
es
task
s
ch
ed
u
lin
g
in
to
v
ir
tu
a
l
m
ac
h
in
es
u
s
in
g
a
d
u
al
q
u
e
u
in
g
tech
n
iq
u
e,
aim
i
n
g
to
r
ed
u
ce
co
s
t,
en
er
g
y
co
n
s
u
m
p
tio
n
,
a
n
d
m
a
k
esp
an
.
L
iu
e
t
a
l.
[
2
1
]
p
r
esen
ted
a
PS
O
alg
o
r
ith
m
f
o
r
task
s
ch
ed
u
lin
g
in
ed
g
e
co
m
p
u
tin
g
en
v
ir
o
n
m
en
ts
.
T
h
is
alg
o
r
it
h
m
aim
s
to
r
ed
u
ce
co
m
p
u
tatio
n
co
s
ts
an
d
was
e
v
alu
ated
u
s
in
g
th
e
C
lo
u
d
Sim
p
latf
o
r
m
.
R
esu
lts
in
d
icate
d
t
h
at
th
eir
ap
p
r
o
ac
h
ef
f
ec
tiv
ely
o
p
tim
ized
co
m
p
u
t
atio
n
tim
e
an
d
c
o
s
t
co
m
p
a
r
ed
to
f
o
u
r
o
th
er
task
s
ch
ed
u
lin
g
alg
o
r
it
h
m
s
.
Nav
ee
n
an
d
An
n
ap
u
r
n
a
[
2
2
]
d
e
v
elo
p
e
d
a
s
ch
e
d
u
lin
g
alg
o
r
ith
m
a
n
d
r
eso
u
r
ce
p
r
o
v
is
io
n
in
g
m
o
d
el
to
cu
t
co
s
ts
d
u
r
in
g
task
s
ch
ed
u
lin
g
.
T
h
eir
m
et
h
o
d
in
v
o
lv
es
b
r
ea
k
in
g
d
o
wn
w
o
r
k
lo
ad
task
s
in
to
s
m
aller
s
u
b
-
task
s
to
ex
p
ed
ite
ex
ec
u
tio
n
an
d
m
ee
t
d
ea
d
lin
es
.
E
v
alu
atio
n
s
o
f
th
eir
m
o
d
el,
f
o
cu
s
in
g
o
n
s
cien
tific
wo
r
k
lo
ad
s
,
s
h
o
wed
f
aster
v
ir
tu
al
m
ac
h
in
e
allo
ca
tio
n
an
d
m
in
im
al
co
s
t.
Ko
n
jaan
g
an
d
Xu
[
2
3
]
p
r
o
p
o
s
ed
a
m
u
lti
-
o
b
jectiv
e
wo
r
k
l
o
ad
o
p
tim
izatio
n
s
tr
ateg
y
(
M
OW
OS)
to
r
ed
u
ce
m
a
k
esp
an
a
n
d
c
o
s
t.
T
h
ey
in
t
r
o
d
u
ce
d
two
alg
o
r
ith
m
s
n
am
ely
m
a
x
im
u
m
v
ir
tu
al
m
ac
h
in
e
an
d
m
in
im
u
m
v
ir
tu
al
m
ac
h
in
e,
to
m
an
a
g
e
wo
r
k
lo
ad
task
s
.
T
h
e
MO
W
OS
ap
p
r
o
ac
h
ac
h
iev
ed
an
8
%
r
ed
u
ctio
n
in
co
s
t
an
d
a
1
0
%
d
ec
r
ea
s
e
in
m
a
k
esp
an
.
Ma
s
o
u
d
i
et
a
l.
[
2
4
]
tack
le
d
en
er
g
y
co
n
s
tr
ain
ts
th
r
o
u
g
h
ef
f
e
c
tiv
e
v
ir
tu
al
m
ac
h
in
e
allo
ca
tio
n
s
tr
ateg
ies.
Stu
d
ies
[
2
5
]
,
[
2
6
]
u
n
d
er
s
co
r
e
d
th
e
r
o
le
o
f
ed
g
e
co
m
p
u
tin
g
in
im
p
r
o
v
in
g
s
er
v
ice
q
u
ality
an
d
en
er
g
y
ef
f
icien
cy
.
Ma
n
g
a
lam
p
alli
et
a
l.
[
2
7
]
em
p
h
asize
d
th
e
n
ee
d
f
o
r
m
u
lti
-
o
b
jectiv
e
o
p
tim
izatio
n
u
s
in
g
DR
L
to
r
ed
u
ce
m
a
k
esp
an
a
n
d
en
er
g
y
c
o
n
s
u
m
p
tio
n
,
t
h
o
u
g
h
ef
f
ec
tiv
e
v
i
r
tu
al
m
ac
h
in
e
p
lac
em
en
t
ac
co
r
d
in
g
to
q
u
ality
-
of
-
s
er
v
ice
(
Qo
S)
r
eq
u
ir
em
en
ts
r
em
ain
s
an
a
r
ea
f
o
r
im
p
r
o
v
em
en
t,
lea
d
in
g
t
o
p
o
ten
tial
d
elay
s
an
d
in
cr
ea
s
ed
m
ak
esp
an
.
3.
P
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I
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3
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eter
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−
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.
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r
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tim
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f
(
3
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d
(
4
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,
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)
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in
(
5
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−
(
)
≜
∑
′
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1
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3
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=
1
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2
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(
5
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(
5
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,
′
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all
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ak
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o
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atin
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at
f
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.
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ar
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eter
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av
ailab
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.
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c
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m
1
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ee
t
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n
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ir
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k
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d
ea
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e
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o
n
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ig
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t
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m
ea
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r
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th
r
o
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g
h
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6
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−
(
1
;
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=
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1
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2
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−
2
…
(
6
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I
n
(
6
)
,
ℇ
/
2
ex
p
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th
e
co
m
p
u
ta
tio
n
al
co
s
t
f
o
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ch
a
n
g
in
g
th
e
f
r
eq
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en
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lev
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ac
co
r
d
in
g
to
task
d
ea
d
lin
e
r
eq
u
ir
em
en
ts
.
Du
r
in
g
th
e
r
ec
o
n
f
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r
atio
n
p
r
o
ce
s
s
,
ex
ten
s
iv
e
co
m
m
u
n
icati
o
n
co
s
t
is
in
v
o
lv
ed
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
38
,
No
.
1
,
Ap
r
il
20
25
:
5
4
6
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5
4
550
to
p
er
f
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d
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ate
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f
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c
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m
m
u
n
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m
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s
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r
ed
in
(
7
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:
−
≡
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+
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(
7
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wh
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ex
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ar
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eter
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in
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witch
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e
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ete
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ef
i
n
in
g
co
m
m
u
n
icatio
n
en
e
r
g
y
.
T
h
e
n
etwo
r
k
m
ay
in
d
u
ce
a
ce
r
tain
lo
ad
a
n
d
d
ela
y
;
h
o
wev
er
,
co
n
s
id
er
in
g
o
p
tim
al
co
m
m
u
n
icatio
n
th
e
t
o
tal
co
m
p
u
tatio
n
co
s
t
is
m
ea
s
u
r
ed
in
(
8
)
.
−
(
)
=
(
̅
∗
)
2
+
,
=
1
(
8
)
I
n
(
8
)
,
th
e
p
ar
am
eter
is
m
ea
s
u
r
e
d
in
(
9
)
:
≜
(
)
−
1
∗
(
−
1
∗
√
2
∗
3
)
2
,
=
1
(
9
)
wh
er
e
p
ar
am
eter
d
ef
in
es
th
e
m
ax
im
u
m
s
eg
m
en
tatio
n
lev
el
co
n
s
id
er
in
g
n
o
is
y
co
d
in
g
g
a
in
.
T
h
u
s
,
co
n
s
id
er
in
g
t
h
e
tr
an
s
f
er
d
elay
is
m
ea
s
u
r
ed
in
(
1
0
)
:
(
)
=
∑
∕
=
1
(
1
0
)
Usi
n
g
(
1
0
)
,
th
e
(
)
,
th
e
co
m
m
u
n
i
ca
tio
n
co
s
t c
an
b
e
f
in
ally
esta
b
lis
h
ed
in
(
1
1
)
.
−
(
)
≜
−
(
)
∗
(
∑
⁄
=
1
)
(
1
1
)
3
.
3
.
E
nerg
y
a
nd
co
s
t
-
a
wa
re
wo
rk
lo
a
d schedu
ler
m
o
del
T
h
is
s
ec
tio
n
in
tr
o
d
u
ce
s
a
n
o
v
el
E
C
A
W
S
em
p
lo
y
in
g
m
u
lti
-
o
b
jectiv
e
o
p
tim
izatio
n
.
T
h
e
c
o
m
p
u
tatio
n
co
s
t
in
(
5
)
,
r
ec
o
n
f
ig
u
r
atio
n
c
o
s
t
in
(
6
)
,
an
d
c
o
m
m
u
n
icatio
n
co
s
t
in
(
1
1
)
ar
e
o
p
tim
ized
th
r
o
u
g
h
th
e
b
elo
w
m
in
im
izatio
n
f
u
n
ctio
n
d
ef
in
e
d
in
(
1
2
)
.
=
min
[
−
(
)
+
−
(
1
;
2
)
+
−
(
)
]
(
1
2
)
T
h
e
d
en
o
tes
th
e
m
u
lti
-
o
b
ject
iv
e
o
p
tim
izatio
n
p
a
r
am
eter
;
t
h
e
o
v
er
all
co
s
t
o
f
th
e
c
o
m
p
u
tatio
n
in
a
h
eter
o
g
en
o
u
s
clo
u
d
p
latf
o
r
m
th
r
o
u
g
h
th
e
u
s
ag
e
o
f
a
m
a
ch
in
e
lea
r
n
in
g
m
o
d
el
[
2
8
]
,
[
2
9
]
ad
o
p
tin
g
d
ee
p
lear
n
in
g
e
v
o
lu
tio
n
ar
y
o
p
tim
i
za
tio
n
m
o
d
el
[
3
0
]
,
[
3
1
]
n
a
m
ely
th
e
en
h
an
ce
d
DR
L
m
o
d
e
l
[
2
7
]
f
o
r
ef
f
icien
t
s
ch
ed
u
lin
g
o
f
wo
r
k
lo
a
d
task
s
an
d
ac
h
iev
in
g
b
etter
p
e
r
f
o
r
m
an
ce
an
d
r
e
d
u
cin
g
co
s
t
a
s
s
h
o
wn
in
th
e
r
esu
lt
s
ec
tio
n
.
4.
RE
SU
L
T
AND
ANA
L
YS
I
S
T
h
e
p
r
o
p
o
s
ed
E
C
AW
S
was
test
ed
u
s
in
g
th
e
I
n
s
p
ir
al
s
ce
n
a
r
io
to
ass
ess
its
p
er
f
o
r
m
an
ce
r
eg
ar
d
in
g
m
ak
esp
an
,
en
e
r
g
y
c
o
n
s
u
m
p
ti
o
n
,
an
d
co
s
t.
T
h
e
E
C
AW
S
al
g
o
r
ith
m
was
ev
alu
ated
a
g
ain
s
t
two
o
th
er
m
o
d
els:
th
e
en
er
g
y
-
m
in
im
ized
s
ch
ed
u
lin
g
(
E
MS)
[
1
1
]
an
d
th
e
m
u
lti
-
o
b
jectiv
e
DR
L
-
b
ased
wo
r
k
lo
ad
s
ch
ed
u
ler
(
MO
DR
L
W
S)
[
2
7
]
.
T
h
e
ev
al
u
atio
n
in
v
o
lv
ed
f
o
u
r
task
s
f
r
o
m
th
e
I
n
s
p
ir
al
d
ataset,
in
clu
d
i
n
g
I
n
s
p
ir
al
3
0
a
n
d
I
n
s
p
ir
al
1
0
0
.
All
ex
p
er
im
e
n
ts
wer
e
co
n
d
u
cted
o
n
a
s
y
s
tem
e
q
u
ip
p
e
d
with
an
I
n
tel®
co
r
e
i
7
p
r
o
ce
s
s
o
r
,
1
6
GB
o
f
R
AM
,
an
d
r
u
n
n
in
g
W
in
d
o
ws
1
0
(
6
4
-
b
it).
T
h
e
clo
u
d
s
im
p
latf
o
r
m
was
u
tili
ze
d
to
s
im
u
late
an
d
ass
ess
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
p
r
o
p
o
s
ed
E
C
AW
S m
o
d
el
alo
n
g
s
id
e
th
e
s
tate
-
of
-
th
e
-
ar
t sch
ed
u
lin
g
alg
o
r
ith
m
s
.
4
.
1
.
M
a
k
esp
a
n per
f
o
rm
a
nce
Fig
u
r
es
3
an
d
4
illu
s
tr
ate
th
e
m
ak
esp
an
f
o
r
I
n
s
p
ir
al
3
0
an
d
I
n
s
p
ir
al
1
0
0
,
r
esp
ec
tiv
ely
.
T
h
e
r
esu
lts
r
ev
ea
l
th
at
th
e
E
MS
m
o
d
el
r
es
u
lted
in
a
lo
n
g
e
r
m
ak
esp
an
c
o
m
p
ar
ed
to
th
e
MO
DR
L
W
S
m
o
d
el.
T
h
e
E
C
AW
S
m
o
d
el
d
em
o
n
s
tr
ated
a
s
u
b
s
ta
n
tial
r
ed
u
ctio
n
in
m
ak
esp
an
—
4
2
.
1
2
%
f
o
r
I
n
s
p
i
r
al
3
0
an
d
6
1
.
4
4
%
f
o
r
I
n
s
p
ir
al
100
—
wh
en
c
o
m
p
a
r
ed
to
MO
DR
L
W
S.
T
h
e
o
v
er
all
m
ak
esp
an
o
f
e
x
ec
u
tio
n
is
r
ed
u
ce
d
em
p
lo
y
in
g
(
5
)
an
d
later
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
E
n
erg
y
a
n
d
co
s
t
-
a
w
a
r
e
w
o
r
klo
a
d
s
ch
ed
u
ler
fo
r
h
etero
g
en
eo
u
s
clo
u
d
… (
Ma
n
ju
n
a
t
h
a
S
h
iv
a
n
a
n
d
a
p
p
a
)
551
th
e
p
ar
am
eter
is
o
p
tim
ized
u
s
in
g
an
en
h
a
n
ce
d
DR
L
m
o
d
el
co
n
tr
ib
u
tin
g
to
a
s
ig
n
i
f
ican
t
r
ed
u
ctio
n
o
f
m
ak
esp
an
u
s
in
g
E
C
AW
S
in
co
m
p
ar
is
o
n
with
E
MS
an
d
MO
DR
L
W
S.
T
h
is
r
ed
u
ctio
n
is
attr
ib
u
ted
to
th
e
en
h
an
ce
d
o
p
tim
izatio
n
p
r
o
v
id
ed
b
y
t
h
e
DR
L
m
o
d
el
u
s
ed
i
n
E
C
AW
S.
Fig
u
r
e
3
.
Ma
k
esp
a
n
f
o
r
in
s
p
ir
al
3
0
Fig
u
r
e
4
.
Ma
k
esp
a
n
f
o
r
in
s
p
ir
al
1
0
0
4
.
2
.
E
nerg
y
c
o
ns
um
ptio
n
perf
o
rma
nce
Fig
u
r
es
5
an
d
6
d
is
p
lay
th
e
e
n
er
g
y
co
n
s
u
m
p
tio
n
f
o
r
I
n
s
p
ir
al
3
0
an
d
I
n
s
p
ir
al
1
0
0
.
T
h
e
E
MS
m
o
d
el
s
h
o
wed
h
ig
h
er
en
er
g
y
co
n
s
u
m
p
tio
n
th
an
b
o
th
MO
DR
L
W
S
an
d
E
C
AW
S.
A
lth
o
u
g
h
M
ODRL
W
S
co
n
s
u
m
ed
less
en
er
g
y
th
an
E
MS,
th
e
E
C
AW
S
m
o
d
el
ac
h
iev
ed
a
r
e
d
u
ctio
n
o
f
3
.
8
%
f
o
r
I
n
s
p
ir
al
3
0
an
d
3
.
1
5
%
f
o
r
I
n
s
p
ir
al
1
0
0
in
e
n
er
g
y
c
o
n
s
u
m
p
tio
n
c
o
m
p
ar
e
d
to
MO
DR
L
W
S.
T
h
e
o
v
er
all
en
er
g
y
o
f
ex
ec
u
tio
n
is
r
ed
u
ce
d
b
y
em
p
lo
y
in
g
(
6
)
an
d
later
t
h
e
p
ar
am
eter
is
o
p
tim
ized
u
s
in
g
an
e
n
h
an
ce
d
DR
L
m
o
d
e
l
co
n
tr
ib
u
tin
g
to
a
s
ig
n
if
ican
t
r
ed
u
ctio
n
o
f
e
n
er
g
y
u
s
in
g
E
C
AW
S
in
co
m
p
ar
is
o
n
with
E
MS
an
d
MO
DR
L
W
S.
T
h
e
im
p
r
o
v
em
en
ts
ar
e
attr
ib
u
ted
to
th
e
ef
f
icien
t
o
p
tim
izatio
n
tech
n
iq
u
es e
m
p
lo
y
ed
in
E
C
AW
S.
Fig
u
r
e
5
.
E
n
er
g
y
co
n
s
u
m
p
tio
n
f
o
r
in
s
p
ir
al
3
0
Fig
u
r
e
6
.
E
n
er
g
y
co
n
s
u
m
p
tio
n
f
o
r
in
s
p
ir
al
1
0
0
4
.
3
.
Co
m
pu
t
a
t
io
n c
o
s
t
Fig
u
r
es
7
an
d
8
d
ep
ict
th
e
co
m
p
u
tatio
n
co
s
ts
f
o
r
I
n
s
p
ir
al
3
0
an
d
I
n
s
p
ir
al
1
0
0
.
T
h
e
r
esu
lts
in
d
icate
th
at
th
e
p
r
o
p
o
s
ed
E
C
AW
S
m
o
d
el
o
f
f
e
r
s
a
s
ig
n
if
ican
t
co
s
t
ad
v
an
tag
e
o
v
er
e
x
is
tin
g
m
o
d
els.
Sp
ec
if
ically
,
E
C
AW
S
r
ed
u
ce
d
co
m
p
u
tatio
n
co
s
ts
b
y
6
4
.
9
5
%
an
d
7
0
.
6
6
%
co
m
p
ar
ed
t
o
MO
DR
L
W
S
f
o
r
I
n
s
p
ir
al
wo
r
k
lo
a
d
s
o
f
s
izes
3
0
an
d
1
0
0
,
r
esp
ec
tiv
ely
.
T
h
e
o
v
er
all
co
s
t
o
f
ex
ec
u
tio
n
is
r
ed
u
ce
d
b
y
e
m
p
lo
y
i
n
g
(
1
0
)
an
d
later
t
h
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
38
,
No
.
1
,
Ap
r
il
20
25
:
5
4
6
-
5
5
4
552
p
ar
am
eter
is
o
p
tim
ize
d
u
s
in
g
an
en
h
a
n
ce
d
D
RL
m
o
d
el
co
n
tr
ib
u
tin
g
to
a
s
ig
n
if
ican
t
r
ed
u
ctio
n
o
f
c
o
s
t
u
s
in
g
E
C
AW
S
in
co
m
p
ar
is
o
n
with
E
MS
an
d
MO
DR
L
W
S.
T
h
is
co
s
t
r
ed
u
ctio
n
is
a
r
esu
lt
o
f
th
e
ef
f
ec
tiv
e
o
p
tim
izatio
n
s
tr
ateg
ies in
co
r
p
o
r
ated
in
to
E
C
AW
S.
Fig
u
r
e
7
.
C
o
m
p
u
tatio
n
c
o
s
t f
o
r
in
s
p
ir
al
3
0
Fig
u
r
e
8
.
C
o
m
p
u
tatio
n
c
o
s
t f
o
r
in
s
p
ir
al
1
0
0
5.
CO
NCLU
SI
O
N
I
n
s
u
m
m
ar
y
,
th
e
E
C
AW
S
m
o
d
el
s
h
o
ws
s
u
p
er
io
r
p
er
f
o
r
m
an
ce
in
r
ed
u
ci
n
g
m
ak
esp
an
,
en
er
g
y
co
n
s
u
m
p
tio
n
,
an
d
c
o
s
t
co
m
p
ar
ed
to
E
MS
an
d
MO
DR
L
W
S.
E
MS,
wh
ile
f
o
cu
s
in
g
o
n
en
er
g
y
an
d
co
s
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Ac
a
d
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ic
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p
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c
e
.
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rd
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d
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h
.
D
.
i
n
2
0
2
3
fro
m
VTU,
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lag
a
v
i,
Ka
rn
a
tak
a
.
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r
re
se
a
rc
h
in
tere
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in
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a
g
e
P
ro
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ss
in
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,
M
a
c
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Lea
rn
in
g
.
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h
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is
wo
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k
i
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g
a
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a
n
As
so
c
iate
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rtme
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t
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n
g
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t
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g
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lu
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h
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h
a
s
p
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).
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h
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c
a
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o
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tac
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m
a
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:
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th
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m
d
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m
it
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in
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shm
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rg
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a
t
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u
.
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r
m
a
in
re
se
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l
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d
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m
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h
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IOT,
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a
c
h
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g
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d
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h
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s
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rn
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d
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m
p
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ter
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g
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rin
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fr
o
m
VTU,
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lg
a
u
m
fo
r
h
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r
wo
rk
in
t
h
e
a
re
a
o
f
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u
d
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o
m
p
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ti
n
g
,
in
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n
2
0
1
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.
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h
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is
a
n
IEE
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e
n
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r
m
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m
b
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h
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p
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d
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n
g
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h
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p
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th
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e
a
r
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h
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h
a
s
p
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b
li
sh
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d
2
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d
ian
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a
ten
ts an
d
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+
re
fe
rre
d
p
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b
li
c
a
ti
o
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s.
e
m
a
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:
ra
sh
m
i
n
e
h
a
.
s@
g
m
a
il
.
c
o
m
.
Dr
.
Va
d
iv
e
l
Ra
m
a
sa
m
y
is
wo
rk
i
n
g
a
s
a
n
As
so
c
iate
P
r
o
fe
ss
o
r
in
t
h
e
De
p
a
rtme
n
t
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f
Artifi
c
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I
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telli
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e
n
c
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n
d
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ta
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ien
c
e
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t
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n
a
k
sh
i
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st
it
u
te
o
f
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h
n
o
l
o
g
y
,
Ba
n
g
a
lo
re
,
Ka
rn
a
tak
a
,
In
d
ia.
He
r
e
c
e
iv
e
d
Ba
c
h
e
lo
r
o
f
E
n
g
i
n
e
e
rin
g
De
g
re
e
in
An
n
a
Un
i
v
e
rsity
,
Ch
e
n
n
a
i,
In
d
ia.
He
re
c
e
iv
e
d
M
a
ste
r
o
f
E
n
g
i
n
e
e
rin
g
De
g
re
e
in
S
a
th
y
a
b
a
m
a
Un
iv
e
rsit
y
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e
n
n
a
i,
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n
d
ia.
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re
c
e
iv
e
d
P
h
.
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.
De
g
re
e
in
Hi
n
d
u
sta
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In
stit
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te
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o
lo
g
y
a
n
d
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c
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c
e
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e
n
n
a
i,
In
d
ia.
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h
a
s
p
u
b
li
s
h
e
d
m
o
re
th
a
n
1
5
p
u
b
li
c
a
ti
o
n
s
in
th
e
re
p
u
te
d
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d
e
x
e
d
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o
u
r
n
a
ls
a
n
d
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ter
n
a
ti
o
n
a
l
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n
fe
re
n
c
e
s.
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re
se
a
rc
h
a
re
a
s
a
r
e
c
lo
u
d
c
o
m
p
u
t
in
g
,
n
e
two
rk
i
n
g
a
n
d
a
d
v
a
n
c
e
d
c
o
m
p
u
ti
n
g.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
v
a
d
i
v
e
l.
r@n
m
it
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c
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i
n
,
v
ad
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elcse@
g
m
ail.
co
m
.
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u
b
r
a
m
a
n
i
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u
r
y
a
k
u
m
a
r
Pra
b
h
u
Vija
y
is
th
e
re
se
a
rc
h
a
n
a
ly
st
a
n
d
se
n
i
o
r
so
ftwa
re
d
e
v
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lo
p
e
r
a
t
n
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v
sh
y
a
tec
h
n
o
lo
g
ies
.
Are
a
s
o
f
i
n
tere
st
in
c
l
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d
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s
wire
les
s
se
n
so
r
n
e
two
rk
s,
in
ter
n
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t
-
of
-
t
h
in
g
s,
c
l
o
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d
c
o
m
p
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in
g
,
n
e
two
rk
se
c
u
rit
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,
wire
les
s
c
o
m
m
u
n
ica
ti
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n
n
e
two
rk
,
ima
g
e
p
ro
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ss
in
g
,
ima
g
e
fo
re
n
sic
.
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h
a
s
a
ss
isted
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n
tec
h
n
ica
l
c
o
n
te
n
t
wri
teu
p
a
n
d
so
f
twa
re
d
e
v
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lo
p
m
e
n
t
v
a
ri
o
u
s
a
c
a
d
e
m
ics
a
n
d
in
d
u
strial
p
ro
jec
ts.
Atte
n
ted
a
s
a
s
a
g
u
e
st
lec
tu
re
r
a
n
d
train
e
r
i
n
v
a
rio
u
s
wo
r
k
sh
o
p
s.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
n
a
v
sh
y
a
tec
h
n
o
l
o
g
ies
@g
m
a
il
.
c
o
m
a
n
d
p
ra
b
h
u
.
v
ij
a
y
2
3
@
g
m
a
il
.
c
o
m
.
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