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Vo
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20
,
No
.
1
,
Feb
r
u
ar
y
20
22
,
p
p
.
8
1~
88
I
SS
N:
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Dep
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th
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ed
s
u
c
h
as
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ec
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ity
an
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alan
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[1
]
,
[
2
]
.
T
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e
n
am
e
“c
lo
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d
”
r
e
f
er
s
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ir
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ices
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h
ig
h
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o
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m
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ce
at
a
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t
[
3
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s
th
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astru
ctu
r
e,
to
b
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o
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id
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as
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[
4
]
.
As
th
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er
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lly
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s
[
5
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,
[
6
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.
T
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g
o
al
o
f
ap
p
ly
in
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th
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alg
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m
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o
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all
th
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er
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s
ar
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y
with
a
s
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ec
if
ied
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r
k
.
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is
will im
p
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ices b
y
m
in
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izin
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r
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tim
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tili
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tio
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th
e
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m
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ce
[
7
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.
T
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ain
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g
a
s
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eso
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ith
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ed
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o
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ea
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if
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s
ed
m
e
m
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an
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tim
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[
8
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[
9
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.
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it
r
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o
n
its
p
r
ev
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o
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s
in
f
o
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m
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[
1
0
]
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Dy
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ith
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p
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m
[
1
1
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Evaluation Warning : The document was created with Spire.PDF for Python.
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TEL
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Vo
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20
,
No
.
1
,
Feb
r
u
ar
y
20
22
:
8
1
-
88
8
2
b
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ith
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t
h
as
ad
v
a
n
tag
es
r
elate
d
to
s
ec
u
r
ity
wh
e
n
h
u
n
d
r
ed
s
o
f
attac
k
er
s
s
tar
t
to
s
en
d
th
e
u
n
wan
ted
tr
af
f
ic
p
ac
k
ets
in
o
r
d
er
to
ac
q
u
ir
e
th
e
m
em
o
r
y
,
n
et
wo
r
k
r
eso
u
r
ce
s
an
d
co
m
p
letely
d
ep
lete
th
e
m
th
is
attac
k
is
k
n
o
wn
as
d
is
tr
ib
u
ted
d
en
ial
o
f
s
er
v
ice
(
DDo
S
)
atta
ck
[
1
4
]
.
On
e
o
f
th
e
m
o
s
t
cr
u
cial
attac
k
is
DDo
S
a
ttack
s
th
at
ar
e
b
ased
o
n
a
jo
in
t
attac
k
p
latf
o
r
m
th
at
in
ten
d
s
to
s
en
d
in
co
m
p
lete
r
eq
u
ests
tr
af
f
i
c
th
at
e
x
au
s
t
n
et
wo
r
k
b
an
d
wid
t
h
o
r
s
y
s
tem
r
eso
u
r
ce
s
.
r
esu
lted
in
p
r
e
v
en
tio
n
o
f
leg
itima
te
u
s
er
s
’
r
eq
u
ests
.
Ad
d
itio
n
lay
er
o
f
s
ec
u
r
ity
ca
n
b
e
ad
d
e
d
b
y
b
alan
c
in
g
th
e
lo
ad
as
a
r
esu
lt
o
f
its
ef
f
ec
t
in
c
o
n
tr
o
llin
g
an
d
a
v
o
id
in
g
DDo
S
attac
k
s
.
Dy
n
am
ic
allo
ca
tio
n
o
f
r
eso
u
r
ce
s
ca
n
b
e
u
s
ed
t
o
m
iti
g
ate
DDo
S
attac
k
ef
f
ec
tiv
ily
[
1
5
]
.
Ma
n
asra
h
an
d
Ali
[1
6
]
h
av
e
c
o
m
b
in
ed
th
e
g
en
etic
a
lg
o
r
ith
m
(
GA
)
a
n
d
p
ar
ticle
s
war
m
o
p
tim
izatio
n
s
(
PSO
)
o
p
tim
izatio
n
alg
o
r
ith
m
.
At
f
ir
s
t
th
e
p
r
elim
in
ar
y
s
o
lu
ti
o
n
is
f
o
u
n
d
b
y
GA
alg
o
r
ith
m
th
at
is
p
ass
ed
to
th
e
PS
O
alg
o
r
ith
m
to
p
r
o
d
u
ce
th
e
f
in
al
s
o
lu
tio
n
.
R
esu
lts
s
h
o
w
i
m
p
r
o
v
e
m
en
t
in
th
e
p
e
r
f
o
r
m
an
ce
(
1
6
%
o
v
er
GA
an
d
4
%
a
b
o
v
e
PS
O)
b
y
r
ed
u
ci
n
g
th
e
o
v
e
r
all
ex
ec
u
t
i
o
n
tim
e
an
d
co
s
t.
Fab
r
izio
et
a
l
.
[
1
7
]
u
s
ed
asy
m
m
etr
ically
clip
p
ed
o
p
tical
(
AC
O
)
h
eu
r
is
tic
alg
o
r
ith
m
to
b
alan
ce
h
eter
o
g
en
e
o
u
s
lo
ad
b
y
p
er
f
o
r
m
in
g
s
ch
ed
u
lin
g
s
im
u
ltan
eo
u
s
ly
.
T
h
e
task
s
g
r
a
p
h
is
m
ap
p
e
d
a
n
d
p
lace
d
o
n
a
h
eter
o
g
en
eo
u
s
r
ec
o
n
f
ig
u
r
ab
le
d
ev
ices
t
o
s
u
p
p
o
r
t
PDR
b
ased
f
ield
-
p
r
o
g
r
am
m
a
b
le
g
ate
ar
r
ay
s
(
FP
GAs
)
.
T
h
e
p
er
f
o
r
m
an
ce
co
m
p
ar
is
o
n
s
h
o
ws
an
im
p
r
o
v
em
en
t
o
f
1
6
.
5
%
o
v
er
o
th
e
r
h
eu
r
is
tic
alg
o
r
ith
m
s
.
J
ain
[1
8
]
ex
a
m
in
ed
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
es
b
ased
o
n
m
u
lti
-
lev
el
s
wam
o
p
tim
izatio
n
to
allo
ca
te
s
u
itab
le
r
e
s
o
u
r
ce
s
th
at
ca
n
u
tili
ze
co
n
tin
u
o
u
s
d
ata
s
tr
ea
m
s
,
m
in
im
ize
tim
e
co
s
t,
an
d
in
cr
ea
s
e
l
o
ad
b
alan
ce
d
eg
r
ee
.
H
o
n
g
[
1
9
]
in
tr
o
d
u
ce
d
g
e
n
etic
an
t
co
lo
n
y
alg
o
r
it
h
m
to
s
o
lv
e
v
ir
tu
al
m
ac
h
in
e
(
VM
)
p
r
o
b
lem
b
y
an
aly
zin
g
an
t
p
lace
m
en
t
b
etwe
e
n
p
air
o
f
v
ir
tu
al
m
ac
h
i
n
es
(
VM
s
)
wh
ich
is
d
o
n
e
b
y
m
o
n
ito
r
in
g
th
e
p
h
e
r
o
m
o
n
e
d
u
r
i
n
g
th
e
an
t
m
o
v
em
e
n
ts
.
T
h
ese
r
esu
lts
will
b
e
o
p
tim
i
ze
d
u
s
in
g
a
g
e
n
etic
alg
o
r
ith
m
.
T
h
e
e
v
alu
atio
n
s
h
o
ws
th
at
th
e
p
h
y
s
ical
s
er
v
er
s
ar
e
ch
o
s
en
ef
f
icien
tly
r
esu
lted
in
r
eso
u
r
ce
u
tili
za
tio
n
im
p
r
o
v
e
m
en
t.
Dav
e
et
a
l
.
[
20
]
p
r
esen
ted
PS
O
f
o
r
b
alan
cin
g
th
e
l
o
ad
r
u
n
n
in
g
in
clo
u
d
en
v
ir
o
n
m
en
t
u
s
in
g
d
if
f
er
en
t
ap
p
licatio
n
s
to
g
en
er
ate
th
e
lo
ad
.
T
h
e
co
m
p
ar
is
o
n
s
h
o
ws
co
n
s
id
er
ab
le
im
p
r
o
v
em
e
n
t
in
th
e
p
er
f
o
r
m
an
ce
o
f
VM
s
r
u
n
n
in
g
ap
p
licatio
n
s
.
Awa
d
et
a
l
[
21
]
d
ev
elo
p
e
d
l
o
ad
b
ala
n
ce
m
u
tat
io
n
p
ar
ticle
s
war
m
o
p
tim
izatio
n
(
L
B
MPSO)
th
at
r
ea
s
s
ig
n
ed
f
ailed
task
an
d
f
in
i
s
h
ed
s
ch
ed
u
lin
g
o
f
th
e
d
is
tr
ib
u
ted
task
s
as
ea
r
lier
as
p
o
s
s
ib
le.
R
e
s
u
lts
p
r
esen
t
i
m
p
r
o
v
e
m
en
t
in
r
o
u
n
d
tr
ip
tim
e
an
d
ex
ec
u
tio
n
tim
e
o
v
er
o
th
er
alg
o
r
ith
m
s
.
J
en
a
[2
2
]
p
r
o
p
o
s
ed
m
u
lti
-
o
b
jectiv
e
PS
O
f
r
am
ewo
r
k
(
MO
PS
O)
f
o
r
s
ch
ed
u
lin
g
task
b
y
co
m
b
in
in
g
PS
O
an
d
an
ev
o
lu
tio
n
ar
y
alg
o
r
ith
m
s
u
ch
as
a
m
u
tatio
n
o
p
er
ato
r
with
co
n
ce
p
ts
co
m
m
o
n
ly
u
s
ed
in
m
u
ltio
b
jectiv
e
ev
o
lu
tio
n
ar
y
alg
o
r
ith
m
s
(
MO
E
As
)
b
ased
o
n
Par
eto
d
o
m
in
an
ce
with
b
etter
m
ec
h
an
is
m
f
o
r
s
p
r
ea
d
in
g
th
e
s
o
lu
tio
n
s
.
T
h
e
ex
p
er
im
en
tal
r
esu
lts
s
h
o
w
im
p
r
o
v
em
en
t
in
r
eso
u
r
ce
u
tili
za
tio
n
an
d
r
ed
u
c
tio
n
in
en
er
g
y
a
n
d
m
ak
e
s
p
an
.
X
.
L
u
a
n
d
Z
.
Gu
[2
3
]
a
p
p
lied
a
d
ap
tiv
e
g
lo
b
al
ex
p
a
n
s
io
n
f
ac
t
o
r
o
n
a
n
t
c
o
lo
n
y
o
p
tim
izatio
n
t
o
s
p
ee
d
u
p
th
e
co
n
v
er
g
e
n
ce
p
r
o
ce
s
s
.
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
m
o
n
ito
r
s
th
e
n
o
d
es
to
d
etec
t
th
e
o
v
er
lo
ad
ed
VM
th
e
n
a
p
p
ly
in
g
AC
O
to
d
is
tr
ib
u
te
lo
ad
o
n
th
e
id
le
o
n
ce
.
T
h
e
r
esu
lts
s
h
o
wed
th
at
ad
ap
tiv
e
clo
u
d
r
eso
u
r
ce
elim
i
n
a
te
h
o
t
s
p
o
ts
an
d
b
alan
ce
th
e
lo
a
d
ef
f
icien
tly
w
h
ich
r
esu
lts
in
h
ig
h
C
PU
u
tili
za
tio
n
.
Dh
in
esh
a
n
d
Ven
k
ata
[
2
4
]
a
p
p
lied
h
o
n
e
y
b
ee
b
eh
av
i
o
r
to
b
alan
ce
th
e
lo
ad
b
y
class
if
y
in
g
VM
s
in
to
o
v
er
lo
ad
ed
a
n
d
u
n
d
er
l
o
ad
ed
n
o
d
es
th
en
r
em
o
v
in
g
th
e
lo
ad
f
r
o
m
t
h
e
h
o
t sp
o
t
n
o
d
e
(
o
v
er
lo
ad
e
d
)
n
o
d
e
to
th
e
id
l
e
(
u
n
d
er
lo
a
d
ed
)
n
o
d
e
ac
c
o
r
d
i
n
g
to
th
e
p
r
io
r
ity
o
f
ea
ch
task
.
T
h
is
wo
r
k
im
p
r
o
v
e
s
th
e
task
ex
ec
u
tio
n
an
d
waitin
g
tim
e
th
at
ca
n
b
e
p
r
o
v
ed
b
y
th
e
s
im
u
latio
n
.
L
ili
an
d
Xu
et
a
l
.
[2
5
]
p
r
o
d
u
ce
d
a
g
r
ee
n
c
l
o
u
d
task
alg
o
r
ith
m
b
ased
o
n
b
in
ar
y
p
a
r
ticle
s
war
m
o
p
tim
izatio
n
(
B
PS
O)
.
T
h
is
wo
r
k
u
s
es
p
ip
elin
e
n
u
m
b
er
f
o
r
VM
s
an
d
r
ea
s
s
ig
n
s
th
e
p
ar
ticle
p
o
s
itio
n
an
d
v
elo
city
in
s
tead
o
f
u
s
in
g
m
atr
ix
o
p
er
atio
n
s
.
R
esu
lts
s
h
o
wed
im
p
r
o
v
em
e
n
t
in
th
e
p
er
f
o
r
m
a
n
ce
b
y
m
in
im
izin
g
ex
ec
u
tio
n
tim
e
a
n
d
r
eso
u
r
ce
co
n
s
u
m
p
tio
n
in
VM
s
.
Xu
e
et
a
l.
[2
6
]
h
as
p
r
o
p
o
s
ed
l
o
ad
b
alan
ce
b
ased
o
n
An
t
c
o
lo
n
y
o
p
tim
izatio
n
b
y
co
n
s
id
er
in
g
t
h
e
av
e
r
ag
e
v
ir
tu
al
m
ac
h
in
e
lo
ad
.
T
h
e
s
tan
d
ar
d
AC
O
p
h
er
o
m
o
n
e
v
alu
e
is
u
p
d
ate
d
ac
co
r
d
in
g
to
th
e
d
is
tan
ce
wh
ile
in
t
h
e
p
r
o
p
o
s
ed
al
g
o
r
ith
m
,
th
e
p
h
er
o
m
o
n
e
v
alu
e
is
s
elec
te
d
ac
co
r
d
in
g
to
th
e
co
m
p
u
tatio
n
al
ca
p
a
b
ilit
ies.
T
h
e
tr
an
s
f
o
r
m
ed
p
r
o
b
ab
ilit
y
p
r
o
b
lem
o
f
an
ts
is
s
o
lv
e
d
b
y
u
s
in
g
th
e
r
o
u
lette
alg
o
r
ith
m
.
W
h
en
task
s
h
a
v
e
s
elec
ted
th
e
s
am
e
v
ir
t
u
al
m
a
ch
in
e,
th
e
alg
o
r
ith
m
will
s
el
ec
t
o
th
er
id
le
v
ir
t
u
al
m
ac
h
in
es
to
m
in
im
ize
th
e
wa
itin
g
p
er
io
d
.
Na
k
r
an
i
an
d
T
o
v
ey
[
2
7
]
h
av
e
b
ee
n
in
s
p
ir
e
d
b
y
th
e
b
eh
a
v
io
r
o
f
s
o
m
e
k
in
d
o
f
b
ee
s
k
n
o
wn
as
f
o
r
a
g
er
h
o
n
ey
b
ee
s
.
T
h
is
te
ch
n
iq
u
e
is
b
ased
o
n
th
e
n
atu
r
al
p
r
o
ce
d
u
r
e
th
at
allo
ca
tes
s
u
itab
le
s
er
v
er
s
to
t
h
e
r
eq
u
ested
task
ef
f
icien
tly
.
R
esu
lts
s
h
o
wed
im
p
r
o
v
em
en
t
in
th
e
p
er
f
o
r
m
an
ce
o
v
er
s
tatic
o
r
g
r
ee
d
y
al
g
o
r
ith
m
f
o
r
h
ig
h
r
e
q
u
est lo
ad
s
,
b
u
t
in
lo
w
v
ar
iab
ilit
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g
r
ee
d
y
alg
o
r
i
th
m
ca
n
o
u
tp
er
f
o
r
m
.
Aln
u
s
air
i
et
a
l.
[2
8
]
h
as
co
m
b
in
ed
p
ar
ticle
s
war
m
alg
o
r
ith
m
with
g
r
av
itatio
n
al
s
ea
r
ch
alg
o
r
ith
m
.
T
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
u
s
es
PS
O
ex
p
lo
itatio
n
f
o
r
g
lo
b
al
s
ea
r
ch
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d
g
r
a
v
itatio
n
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s
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ch
alg
o
r
ith
m
(
GSA
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Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
Op
timiz
ed
lo
a
d
b
a
la
n
ce
s
ch
e
d
u
lin
g
a
lg
o
r
ith
m
…
(
R
a
w
a
a
M
o
h
a
mme
d
A
b
d
u
l
-
Hu
s
s
ein
)
8
3
ex
p
lo
r
atio
n
f
o
r
lo
ca
l
s
ea
r
c
h
.
T
h
e
ex
p
e
r
im
en
tal
r
esu
lts
s
h
o
w
th
at
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
b
alan
ce
s
th
e
lo
ad
o
v
er
tim
e
an
d
e
n
h
an
ce
s
th
e
o
v
er
all
u
tili
za
tio
n
.
T
h
e
co
n
tr
ib
u
tio
n
o
f
th
is
p
ap
e
r
was
in
:
p
r
o
p
o
s
in
g
h
y
b
r
id
al
g
o
r
ith
m
,
t
h
e
f
ir
s
t
o
n
e
is
B
AT
alg
o
r
ith
m
th
at
h
as th
e
ca
p
ab
ilit
y
o
f
f
ast c
o
n
v
er
g
en
ce
an
d
th
e
s
ec
o
n
d
al
g
o
r
ith
m
is
C
u
ck
o
o
th
at
o
v
er
co
m
es th
e
p
r
o
b
lem
o
f
tr
ap
p
in
g
in
lo
ca
l o
p
tim
u
m
s
o
lu
tio
n
.
Seco
n
d
ly
,
t
h
is
alg
o
r
ith
m
co
u
ld
b
e
u
s
ed
to
m
itig
ate
D
Do
S
attac
k
th
at
aim
s
to
ca
u
s
e
en
d
less
lo
ad
o
n
th
e
s
er
v
er
s
an
d
s
to
p
th
e
s
er
v
ice.
B
alan
cin
g
th
e
lo
ad
is
th
e
b
e
s
t
way
to
p
r
o
h
ib
it
attac
k
er
s
f
r
o
m
DDo
S a
ttack
b
y
f
air
ly
d
is
tr
ib
u
tio
n
o
f
wo
r
k
lo
ad
s
.
2.
P
RO
P
O
SE
D
H
YB
R
I
D
O
P
T
I
M
I
Z
A
T
I
O
N
A
L
G
O
RI
T
H
M
A
n
ew
h
y
b
r
id
alg
o
r
ith
m
h
as
b
ee
n
p
r
o
p
o
s
ed
b
y
co
m
b
in
in
g
th
e
B
AT
an
d
C
u
ck
o
o
m
etah
eu
r
is
tic
Sear
ch
alg
o
r
ith
m
.
B
AT
alg
o
r
it
h
m
ca
n
r
ea
c
h
t
o
war
d
s
o
p
tim
u
m
g
lo
b
al
r
e
g
io
n
q
u
ick
ly
(
f
ast
ex
p
lo
r
atio
n
)
f
o
r
th
e
b
est
s
o
lu
tio
n
o
v
er
a
lar
g
e
p
o
p
u
latio
n
.
Ho
wev
er
,
af
ter
a
s
eq
u
en
ce
o
f
iter
atio
n
s
,
th
e
lo
ca
l
s
ea
r
ch
s
tr
ateg
y
o
f
B
AT
alg
o
r
ith
m
ca
n
tr
a
p
i
n
t
o
th
e
lo
ca
l
o
p
tim
u
m
.
C
o
n
s
eq
u
e
n
tly
,
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
u
s
es
C
u
ck
o
o
s
ea
r
ch
alg
o
r
ith
m
to
r
ep
lace
th
e
lo
ca
l sear
ch
o
f
B
AT
alg
o
r
ith
m
b
y
p
ass
in
g
th
e
B
AT
b
est
s
o
lu
tio
n
to
cu
ck
o
o
alg
o
r
ith
m
wh
ich
u
s
es L
év
y
flig
h
ts
s
tr
ateg
y
to
o
v
er
co
m
e
B
AT
lo
ca
l o
p
t
im
u
m
p
r
o
b
lem
.
T
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
s
tar
t
s
to
s
ea
r
ch
f
o
r
a
n
ew
s
o
lu
tio
n
,
wh
ich
is
g
en
er
ate
d
in
th
r
e
e
s
tag
es:
i)
B
AT
alg
o
r
ith
m
is
ap
p
lied
to
f
in
d
th
e
f
ir
s
t
s
o
lu
tio
n
u
s
in
g
g
lo
b
al
s
ea
r
ch
;
ii)
C
u
ck
o
o
alg
o
r
ith
m
g
en
er
ates
a
n
ew
s
o
lu
tio
n
ar
o
u
n
d
th
e
b
est
B
AT
s
o
lu
tio
n
;
an
d
ii
i)
T
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
ch
o
o
s
es
th
e
b
est
s
o
lu
tio
n
b
etwe
en
B
AT
an
d
C
u
ck
o
o
alg
o
r
ith
m
.
2
.
1
.
B
AT
o
ptim
iza
t
io
n
a
lg
o
r
it
hm
B
AT
co
u
ld
b
e
class
if
ied
in
to
th
r
ee
t
y
p
es
af
ter
s
tu
d
in
g
1
0
0
0
s
p
ec
ies
o
f
th
em
:
m
icr
o
B
AT
,
m
eg
a
B
AT
,
g
h
o
s
t
B
AT
.
Mic
r
o
B
A
T
u
s
es
a
s
o
n
ar
ca
lled
ec
h
o
lo
c
atio
n
b
y
g
en
er
atin
g
a
lo
u
d
s
o
u
n
d
wav
e
with
lo
w
f
r
eq
u
e
n
cy
an
d
lo
w
p
u
ls
e
r
ate.
W
h
en
th
e
B
AT
f
o
u
n
d
th
e
p
r
e
y
,
th
e
lo
u
d
n
ess
d
ec
r
ea
s
es
wh
ile
th
e
f
r
eq
u
en
c
y
an
d
p
u
ls
e
r
ate
in
cr
ea
s
e
with
a
s
h
o
r
t tim
e
(
f
r
eq
u
en
cy
tu
n
in
g
)
to
d
e
tect
th
e
lo
ca
tio
n
o
f
p
r
ey
ac
cu
r
ately
.
T
h
is
s
tr
ateg
y
is
u
s
ed
in
th
e
g
lo
b
al
B
AT
s
e
ar
ch
.
T
h
e
B
AT
alg
o
r
ith
m
is
b
ased
o
n
m
icr
o
B
AT
b
eh
av
io
r
,
s
o
ev
er
y
B
AT
is
ass
ig
n
ed
:
−
Fre
q
u
en
cy
:
n
u
m
b
er
o
f
wav
es
in
p
ar
ticu
lar
u
n
it
tim
e
d
en
o
ted
b
y
f
i
,
th
e
m
in
im
u
m
f
r
eq
u
en
cy
f
min
an
d
m
ax
im
u
m
f
r
eq
u
e
n
cy
f
max
o
f
th
e
s
o
u
n
d
wav
e
will b
e
u
s
ed
to
c
alcu
late
th
e
f
r
eq
u
en
cy
at
ea
c
h
iter
atio
n
.
−
2
-
Po
s
itio
n
: th
e
lo
ca
tio
n
o
f
ea
c
h
B
AT
in
th
e
p
o
p
u
latio
n
d
en
o
t
ed
b
y
x
i
.
−
Velo
city
: sp
ee
d
o
f
B
AT
to
war
d
th
e
p
r
e
y
v
i
.
−
L
o
u
d
n
ess
:
th
e
in
ten
s
ity
o
f
s
o
u
n
d
wav
e
A
i
,
th
e
lo
u
d
n
ess
v
alu
e
r
an
g
e
b
etwe
en
m
ax
im
u
m
lo
u
d
n
ess
v
alu
e
A
1
an
d
m
in
im
u
m
lo
u
d
n
ess
v
alu
e
A
0
,
as th
e
B
AT
b
ec
o
m
es c
lo
s
er
to
th
e
tar
g
et,
th
e
lo
u
d
n
ess
is
m
in
im
ized
.
−
Pu
ls
e
r
ate:
th
e
v
ib
r
atio
n
o
f
s
o
u
n
d
d
en
o
te
d
b
y
r
i
,
as
th
e
B
AT
b
ec
o
m
es
cl
o
s
er
to
th
e
p
r
ey
,
th
e
p
u
ls
e
r
ate
is
in
cr
ea
s
ed
.
I
n
th
e
B
AT
alg
o
r
ith
m
th
e
s
ea
r
ch
in
g
s
tr
ateg
ies
ar
e
class
if
ied
in
to
two
ty
p
es:
i)
L
o
ca
l
s
ea
r
c
h
:
is
u
s
ed
f
o
r
g
en
e
r
atin
g
a
n
ew
s
o
lu
tio
n
ar
o
u
n
d
th
e
p
o
s
itio
n
o
f
th
e
c
u
r
r
en
t
b
est
s
o
lu
tio
n
,
b
y
ch
ec
k
in
g
if
th
e
r
a
n
d
o
m
v
alu
e
is
g
r
ea
ter
th
an
th
e
p
u
ls
e
r
ate
; a
n
d
ii)
G
lo
b
al
s
ea
r
ch
:
a
n
ew
s
o
lu
tio
n
is
g
en
er
ated
b
y
f
l
y
in
g
r
an
d
o
m
ly
t
h
en
ch
ec
k
in
g
if
th
e
f
itn
ess
o
f
th
e
n
ew
s
o
l
u
tio
n
is
lo
wer
t
h
an
th
e
f
itn
ess
o
f
th
e
cu
r
r
en
t
b
es
t
s
o
lu
tio
n
an
d
th
e
lo
u
d
n
ess
v
alu
e
is
g
r
ea
ter
t
h
a
n
a
r
an
d
o
m
v
al
u
e,
th
en
ac
ce
p
t
th
e
n
ew
s
o
lu
tio
n
,
I
n
cr
ea
s
e
th
e
p
u
ls
e
r
ate,
an
d
d
ec
r
ea
s
e
th
e
lo
u
d
n
ess
.
2
.
2
.
Cucko
o
o
ptim
iza
t
i
o
n a
l
g
o
rit
hm
T
h
is
alg
o
r
ith
m
is
a
m
eta
-
h
eu
r
i
s
tic
alg
o
r
ith
m
p
r
o
p
o
s
ed
b
y
Ya
n
g
an
d
Deb
in
2
0
0
9
b
y
a
n
aly
z
in
g
s
o
m
e
cu
ck
o
o
b
ir
d
b
eh
av
io
r
th
at
lay
th
eir
eg
g
s
in
th
e
n
ests
o
f
o
t
h
e
r
b
ir
d
s
.
T
h
is
s
tr
an
g
e
b
r
ee
d
in
g
b
eh
av
io
r
i
n
cr
ea
s
es
th
eir
s
u
r
v
iv
al
an
d
p
r
o
d
u
ctiv
ity
.
Ma
n
y
h
o
s
t
b
ir
d
s
ca
n
n
o
t
d
if
f
e
r
en
tiate
C
u
ck
o
o
b
ir
d
f
r
o
m
t
h
eir
b
ir
d
s
,
an
d
t
h
ese
h
o
s
t
b
ir
d
s
will
b
r
o
o
d
cu
ck
o
o
eg
g
s
u
n
til
th
e
y
h
atc
h
an
d
f
i
n
ally
f
ee
d
in
g
th
em
u
n
ti
l
ch
ick
s
g
r
o
w
u
p
.
Alth
o
u
g
h
m
an
y
c
u
ck
o
o
b
i
r
d
s
s
u
r
v
iv
e
f
r
o
m
t
h
e
d
is
co
v
e
r
y
o
f
h
o
s
t
b
ir
d
,
th
e
p
o
s
s
ib
ilit
y
th
at
th
e
h
o
s
t
b
ir
d
r
ea
lizes
C
u
ck
o
o
eg
g
co
u
ld
h
ap
p
e
n
,
th
en
h
o
s
t
b
ir
d
m
ay
th
r
o
w
awa
y
th
e
cu
ck
o
o
eg
g
f
r
o
m
th
e
n
est
o
r
leav
e
th
at
n
est.
T
h
r
ee
r
u
les
s
h
o
u
ld
b
e
im
p
o
s
ed
in
th
e
im
p
lem
en
tatio
n
o
f
th
e
C
u
ck
o
o
alg
o
r
ith
m
:
−
E
ac
h
cu
ck
o
o
ch
o
o
s
es r
an
d
o
m
n
est an
d
p
u
ts
o
n
e
eg
g
at
a
tim
e.
−
B
est n
est r
ep
r
esen
ts
b
est s
o
lu
ti
o
n
th
at
is
u
s
ed
to
p
r
ed
ict
th
e
n
ex
t g
en
er
atio
n
o
f
s
o
lu
tio
n
s
.
−
T
h
er
e
ar
e
a
f
ix
ed
n
u
m
b
er
o
f
n
ests
.
T
h
e
B
est
s
o
lu
tio
n
r
ep
r
esen
ts
a
h
ig
h
-
q
u
ality
eg
g
(
s
im
ilar
to
h
o
s
t
b
ir
d
e
g
g
s
)
,
s
o
it
h
as
th
e
o
p
p
o
r
tu
n
ity
to
d
ev
elo
p
an
d
b
ec
o
m
e
a
m
atu
r
e
C
u
ck
o
o
.
W
h
er
ea
s
wo
r
s
e
s
o
lu
tio
n
r
ep
r
esen
ts
eg
g
s
th
at
co
u
l
d
b
e
d
is
tin
g
u
is
h
ed
b
y
h
o
s
t b
ir
d
s
o
it sh
o
u
l
d
b
e
r
e
p
lace
d
.
T
h
e
g
en
er
atio
n
o
f
n
ew
s
o
lu
ti
o
n
is
d
o
n
e
b
y
lev
y
f
lig
h
t
wh
i
ch
is
a
s
et
o
f
s
tr
aig
h
t
p
ath
s
tu
r
n
ed
b
y
9
0
d
eg
r
ee
s
ea
ch
tim
e
as th
e
(
1
)
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6
9
3
0
TEL
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
,
Vo
l.
20
,
No
.
1
,
Feb
r
u
ar
y
20
22
:
8
1
-
88
8
4
X
i
(
t+1
)
=x
i
(
t)
+ α
⊕
Levy
(
λ)
(
1
)
W
h
er
e
i r
ep
r
ese
n
t c
u
c
k
o
o
n
est
α
=
s
tep
s
ize
=
1
λ
=
lev
y
ex
p
o
n
en
t =
1
.
5
⊕
=
en
tr
y
wis
e
m
u
ltip
licatio
n
X
i
(
t+1
)
r
ep
r
esen
t a
n
ew
s
o
lu
ti
o
n
,
X
i
(
t
)
r
ep
r
esen
t th
e
cu
r
r
en
t
lo
ca
tio
n
(
s
o
lu
tio
n
)
.
T
h
e
ev
o
lu
tio
n
p
r
o
ce
s
s
o
f
cu
c
k
o
o
s
ea
r
ch
d
e
f
in
es
th
r
ee
d
if
f
e
r
en
t
s
tag
es:
i)
T
h
e
f
ir
s
t
o
n
e
is
L
ev
y
(
λ
)
f
lig
h
t th
at
is
u
s
ed
to
f
in
d
a
n
e
w
p
o
s
itio
n
d
en
o
te
d
b
y
X
i
(
t+1
)
b
ased
o
n
th
e
c
u
r
r
e
n
t lo
ca
tio
n
o
f
cu
ck
o
o
X
i
(
t
)
an
d
r
an
d
o
m
s
tep
s
ize
g
en
e
r
ated
f
r
o
m
L
ev
y
f
lig
h
t
(
λ
)
;
an
d
ii)
T
h
e
s
ec
o
n
d
s
tr
ateg
y
in
v
o
lv
es
r
ep
lacin
g
th
e
n
est
o
f
th
e
d
is
c
o
v
er
ed
eg
g
with
an
o
t
h
er
n
est.
T
h
e
last
s
tr
ateg
y
i
s
Gr
ee
d
y
(
elitis
t)
s
elec
tio
n
b
y
ap
p
ly
in
g
th
e
f
itn
ess
f
u
n
ctio
n
t
o
ex
tr
ac
t th
e
b
est s
o
lu
tio
n
.
T
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
ca
n
b
e
p
r
esen
ted
in
F
ig
u
r
e
1
.
Fig
u
r
e
1
.
Flo
wch
ar
t
o
f
p
r
o
p
o
s
ed
alg
o
r
ith
m
T
h
is
f
lo
wch
ar
t
ca
n
b
e
s
u
m
m
a
r
ized
in
th
e
f
o
llo
win
g
s
tep
s
:
i)
Po
p
u
latio
n
:
th
e
to
tal
n
u
m
b
e
r
o
f
B
AT
s
s
ea
r
ch
in
g
f
o
r
a
p
r
ey
a
n
d
to
tal
n
u
m
b
er
o
f
iter
atio
n
s
;
ii)
Ass
ig
n
r
an
d
o
m
v
alu
e
o
f
f
r
e
q
u
en
c
y
,
v
elo
city
,
p
o
s
itio
n
,
lo
u
d
n
ess
,
an
d
p
u
ls
e
r
ate
f
o
r
ea
ch
B
AT
;
an
d
iii)
C
h
ec
k
,
if
th
e
n
u
m
b
e
r
s
o
f
iter
atio
n
lo
wer
th
an
th
e
to
tal
n
u
m
b
e
r
o
f
iter
atio
n
s
th
en
g
en
er
ate
a
n
ew
s
o
lu
tio
n
b
y
u
p
d
atin
g
th
e
f
r
eq
u
en
cy
,
v
elo
city
,
an
d
p
o
s
itio
n
.
Use
(
1
)
,
(
2
)
,
(
3
)
.
=
+
(
−
)
(
2
)
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
Op
timiz
ed
lo
a
d
b
a
la
n
ce
s
ch
e
d
u
lin
g
a
lg
o
r
ith
m
…
(
R
a
w
a
a
M
o
h
a
mme
d
A
b
d
u
l
-
Hu
s
s
ein
)
8
5
=
−
1
+
(
−
1
−
∗
)
(
3
)
=
−
1
+
(
4
)
−
C
h
ec
k
if
th
e
r
an
d
o
m
v
alu
e
is
g
r
ea
ter
th
an
th
e
p
u
ls
e
r
ate
th
e
n
s
elec
ts
th
is
s
o
lu
tio
n
am
o
n
g
th
e
b
est s
o
lu
tio
n
.
−
Ap
p
ly
cu
ck
o
o
al
g
o
r
ith
m
t
o
g
e
n
er
ate
a
n
ew
s
o
lu
tio
n
ar
o
u
n
d
t
h
is
s
o
lu
tio
n
.
−
Use th
e
B
AT
b
est s
o
lu
tio
n
as t
h
e
in
itial b
est C
u
ck
o
o
s
o
lu
tio
n
an
d
ca
lcu
late
its
f
itn
ess
.
−
Use L
´
ev
y
f
lig
h
t t
o
g
en
e
r
ate
n
ew
s
o
lu
tio
n
an
d
ca
lc
u
late
its
f
itn
ess
.
−
C
o
m
p
ar
e
th
e
n
ew
f
itn
ess
v
alu
e
with
cu
r
r
en
t
f
itn
ess
v
alu
e
a
n
d
c
h
o
o
s
e
t
h
e
n
est
with
less
f
itn
ess
as
a
n
ew
s
o
lu
t
io
n
.
−
R
ep
lace
th
e
wo
r
s
e
cu
ck
o
o
s
o
lu
tio
n
b
y
a
n
ew
s
o
lu
tio
n
b
y
f
ly
in
g
r
an
d
o
m
ly
,
ch
o
o
s
e
th
is
s
o
lu
tio
n
if
it
s
f
itn
ess
v
alu
e
is
less
th
an
th
e
cu
r
r
en
t f
itn
ess
.
−
r
etu
r
n
to
B
AT
alg
o
r
ith
m
an
d
C
h
ec
k
if
th
e
f
itn
ess
o
f
th
e
n
e
w
cu
ck
o
o
s
o
lu
tio
n
is
lo
wer
th
an
th
e
f
itn
ess
o
f
B
AT
b
est s
o
lu
tio
n
an
d
th
e
lo
u
d
n
ess
v
alu
e
is
g
r
ea
ter
th
a
n
a
r
a
n
d
o
m
v
alu
e
th
e
n
:
−
Dec
r
ea
s
e
th
e
lo
u
d
n
ess
an
d
in
c
r
ea
s
e
th
e
p
u
ls
e
r
ate
ac
co
r
d
in
g
to
th
e
f
o
llo
win
g
eq
u
atio
n
s
(
+
1
)
=
(
)
(
5
)
(
)
=
(
0
)
[
1
−
(
−
)
]
(
6
)
is
a
r
an
d
o
m
n
u
m
b
er
f
r
o
m
[
−1
,
1
]
,
(
)
is
th
e
av
er
ag
e
lo
u
d
n
ess
o
f
th
e
p
o
p
u
latio
n
.
−
R
an
k
th
e
B
AT
an
d
ac
ce
p
t th
e
b
est n
ew
s
o
lu
tio
n
with
h
ig
h
er
f
r
eq
u
e
n
cy
an
d
p
u
ls
e
r
ate.
−
R
ep
ea
t th
e
ab
o
v
e
s
tep
s
u
n
til te
r
m
in
atio
n
cr
iter
ia
s
atis
f
ied
.
As a
r
esu
lt,
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
ca
n
ex
p
lo
r
e
wid
e
r
an
g
e
o
f
p
r
o
b
lem
s
p
ac
e
wh
ile
av
o
id
in
g
g
ettin
g
s
tu
ck
i
n
lo
ca
l o
p
tim
u
m
.
3.
RE
SU
L
T
S
A
ND
D
I
SCU
SS
I
O
NS
C
lo
u
d
s
im
h
as
b
ee
n
u
s
ed
to
i
n
v
esti
g
ate
lar
g
e
-
s
ca
le
clo
u
d
e
n
v
ir
o
n
m
en
t.it
is
d
e
v
elo
p
e
d
b
y
th
e
Gr
id
b
u
s
p
r
o
ject
team
o
f
Me
lb
o
u
r
n
e
Un
iv
er
s
ity
in
A
u
s
tr
alia.
T
h
e
im
p
l
em
en
tatio
n
o
f
t
h
e
p
r
o
p
o
s
ed
al
g
o
r
ith
m
is
d
o
n
e
b
y
u
s
in
g
J
av
a
p
r
o
g
r
a
m
m
in
g
lan
g
u
ag
e,
I
DE
:
E
clip
s
e.
T
h
e
e
v
alu
atio
n
is
m
ea
s
u
r
e
d
b
y
c
o
m
p
ar
in
g
a
s
et
o
f
p
ar
am
eter
s
lik
e
ex
ec
u
tio
n
tim
e
an
d
av
e
r
ag
e
u
tili
za
tio
n
o
v
er
5
an
d
1
0
VM
s
f
o
r
v
ar
y
in
g
n
u
m
b
er
o
f
clo
u
d
lets
(
task
s
)
.
T
h
e
p
r
o
p
o
s
ed
alg
o
r
it
h
m
is
co
m
p
a
r
ed
with
R
o
u
n
d
R
o
b
in
Alg
o
r
ith
m
(
R
R
)
,
f
ir
s
t
c
o
m
e
f
ir
s
t
s
er
v
e
d
(F
C
FS
)
an
d
s
tan
d
ar
d
B
AT
alg
o
r
ith
m
.
R
o
u
n
d
R
o
b
in
ass
ig
n
task
to
ea
c
h
v
ir
t
u
al
m
ac
h
i
n
e
in
Seq
u
en
tial
o
r
d
er
,
s
o
wh
en
th
e
f
ir
s
t
task
ar
r
iv
e,
i
t
will
b
e
s
en
t
to
th
e
f
ir
s
t
v
ir
tu
al
m
ac
h
in
e
th
en
th
e
s
ec
o
n
d
ta
s
k
is
p
as
s
ed
to
th
e
s
ec
o
n
d
v
ir
tu
al
m
ac
h
in
e
a
n
d
s
o
o
n
.
FC
FS
a
lg
o
r
ith
m
allo
ca
tes
th
e
lo
ad
in
a
s
eq
u
en
tial
o
r
d
er
ac
co
r
d
in
g
to
its
p
r
ec
ed
en
ce
o
f
ar
r
iv
al.
T
h
e
ev
alu
atio
n
is
b
ased
o
n
co
m
p
ar
in
g
Ma
k
e
Sp
an
,
wh
ich
is
k
n
o
w
n
as
jo
b
co
m
p
letio
n
tim
e.
I
t is m
ea
s
u
r
ed
in
n
an
o
s
e
co
n
d
s
.
T
h
e
ex
p
er
im
e
n
tal
r
esu
l
t c
an
b
e
ca
teg
o
r
ized
in
t
o
f
iv
e
c
ases
:
3
.
1
.
E
x
ec
utio
n t
i
m
e
f
o
r
VM
s
As
s
h
o
wn
in
F
ig
u
r
e
2
,
th
er
e
is
s
m
all
d
if
f
er
e
n
ce
b
etwe
en
th
e
ex
ec
u
tio
n
tim
e
wh
en
th
e
r
an
g
e
o
f
task
is
b
etwe
en
1
0
to
2
0
.
W
h
en
t
h
e
n
u
m
b
er
o
f
task
s
ex
ce
ed
s
3
0
task
s
,
th
e
d
if
f
er
en
ce
s
in
e
x
ec
u
tio
n
tim
e
in
cr
ea
s
es
g
r
ad
u
all
y
.
T
h
e
im
p
r
o
v
em
en
t
o
f
th
e
p
r
o
p
o
s
ed
B
AT
cu
c
k
o
o
s
ea
r
ch
(
B
AT
CS
)
h
as
r
ea
ch
ed
to
ab
o
u
t
1
4
%
o
v
e
r
FC
F
S a
lg
o
r
ith
m
,
6
% o
v
er
R
R
,
an
d
4
% o
v
er
s
tan
d
ar
d
B
AT
alg
o
r
ith
m
.
Fo
r
1
0
VM
s
,
th
e
ex
ec
u
tio
n
ti
m
e
is
p
r
esen
ted
in
F
ig
u
r
e
3
.
I
t
ca
n
b
e
o
b
s
er
v
e
d
f
r
o
m
F
ig
u
r
e
2
th
at
t
h
e
p
r
o
p
o
s
ed
B
AT
CS
alg
o
r
ith
m
h
as
m
in
im
u
m
e
x
ec
u
tio
n
tim
e
o
v
er
all
o
th
er
al
g
o
r
ith
m
s
.
T
h
e
p
r
o
p
o
s
ed
B
AT
C
S
ac
h
iev
es
ab
o
u
t
1
8
%
less
ex
ec
u
tio
n
tim
e
th
an
R
R
alg
o
r
ith
m
an
d
9
%
o
v
er
b
o
th
FC
FS
an
d
s
tan
d
ar
d
B
AT
alg
o
r
ith
m
.
3
.
2
.
CP
U
utiliza
t
io
n f
o
r
VM
s
As
p
r
esen
ted
in
Fig
u
r
e
4
,
wh
en
ap
p
ly
in
g
f
iv
e
v
i
r
tu
al
m
ac
h
in
e
to
th
e
s
im
u
latio
n
,
th
e
r
es
u
lts
p
r
o
v
e
th
at
B
AT
C
S
alg
o
r
ith
m
im
p
r
o
v
e
th
e
av
er
a
g
e
r
eso
u
r
ce
u
tili
z
atio
n
wh
en
c
o
m
p
ar
e
d
to
FC
FS
,
R
R
,
an
d
s
tan
d
ar
d
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ith
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d
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tio
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e
th
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d
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er
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S
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d
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ith
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t
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n
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l
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m
s
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y
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n
d
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d
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h
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n
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t
h
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3
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n
d
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r
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n
d
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v
e
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s
t
a
n
d
a
r
d
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A
T
a
l
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o
r
it
h
m
s
.
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i
n
a
ll
y
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t
h
e
p
e
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o
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m
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n
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e
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s
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o
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e
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t
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a
t
t
h
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m
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t
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d
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n
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d
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o
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ith
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d
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itig
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at
aim
s
to
ca
u
s
e
en
d
less
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t
h
e
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er
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d
s
to
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th
e
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er
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ice.
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alan
cin
g
t
h
e
lo
ad
is
th
e
b
est
way
to
p
r
o
h
ib
it
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k
er
s
f
r
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m
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k
b
y
d
is
tr
ib
u
tin
g
th
e
wo
r
k
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ad
f
air
ly
.
As
a
f
u
t
u
r
e
wo
r
k
th
e
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ata
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en
e
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ated
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n
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ase
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f
n
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m
al
s
tate
(
with
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t
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n
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e
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ak
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m
p
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ac
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s
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g
u
is
h
b
etwe
en
leg
itima
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d
a
g
g
r
es
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iv
e
leg
itima
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u
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er
s
.
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Un
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