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I
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C
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p
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g
,
Vo
l.
15
,
No
.
3
,
J
u
n
e
20
25
:
3
4
4
9
-
3
4
5
7
3450
ef
f
ec
tiv
e
lo
ad
b
alan
cin
g
.
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n
o
th
er
wo
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o
r
tr
an
s
f
er
o
f
a
c
o
m
p
u
tatio
n
al
task
f
r
o
m
o
n
e
s
y
s
tem
o
r
en
v
ir
o
n
m
e
n
t
to
an
o
th
er
.
T
h
is
tim
e
s
h
o
u
ld
b
e
m
in
im
al
to
im
p
r
o
v
e
th
e
p
er
f
o
r
m
an
ce
o
f
clo
u
d
co
m
p
u
tin
g
.
−
R
esp
o
n
s
e
tim
e:
I
t
is
th
e
m
in
im
u
m
tim
e
th
at
a
d
is
tr
ib
u
ted
s
y
s
tem
tak
es
to
r
esp
o
n
d
to
ex
e
cu
tin
g
a
s
p
ec
if
ic
lo
ad
b
alan
cin
g
alg
o
r
ith
m
.
−
R
eso
u
r
ce
u
tili
za
tio
n
:
I
t
is
th
e
lev
el
to
wh
ich
th
e
r
eso
u
r
ce
s
o
f
th
e
clo
u
d
ar
e
u
tili
ze
d
.
T
h
e
m
o
s
t
ef
f
ec
tiv
e
lo
ad
b
alan
cin
g
alg
o
r
ith
m
m
a
x
im
izes th
e
u
s
e
o
f
av
ailab
le
r
eso
u
r
ce
s
.
−
Scalab
ilit
y
:
Scalab
ilit
y
d
eter
m
in
es
th
e
ab
ilit
y
o
f
th
e
s
y
s
tem
t
o
ac
co
m
p
lis
h
a
lo
ad
b
alan
cin
g
alg
o
r
ith
m
with
a
lim
ited
n
u
m
b
e
r
o
f
p
r
o
ce
s
s
o
r
s
o
r
m
ac
h
in
es.
−
Po
wer
s
av
in
g
:
I
t
r
ep
r
esen
ts
t
h
e
m
ec
h
an
is
m
o
f
en
er
g
y
co
n
s
u
m
p
tio
n
to
m
ain
tain
g
o
o
d
q
u
ality
o
f
s
er
v
ice
(
Qo
S)
o
f
d
ata
ce
n
ter
s
.
Fo
r
e
x
am
p
le,
en
er
g
y
ca
n
b
e
co
n
s
er
v
ed
b
y
m
ak
in
g
u
s
e
o
f
v
ir
tu
al
m
ac
h
in
e
(
VM
)
m
ig
r
atio
n
s
.
−
Per
f
o
r
m
an
ce
:
I
t
r
e
p
r
esen
ts
th
e
ef
f
ec
tiv
en
ess
o
f
th
e
s
y
s
tem
a
f
ter
p
er
f
o
r
m
i
n
g
l
o
ad
b
alan
cin
g
.
Ob
v
i
o
u
s
ly
,
if
all
th
e
ab
o
v
e
p
ar
a
m
eter
s
ar
e
o
p
tim
ally
s
atis
f
ied
,
th
en
it
h
ig
h
ly
im
p
r
o
v
es
th
e
p
e
r
f
o
r
m
an
ce
o
f
clo
u
d
co
m
p
u
tin
g
.
T
h
is
p
ap
er
is
o
r
g
an
ized
as
f
o
llo
ws.
I
n
s
ec
tio
n
2
,
au
th
o
r
s
cite
th
e
p
r
ec
ed
in
g
liter
atu
r
e
o
n
lo
ad
b
alan
cin
g
alg
o
r
ith
m
s
.
Sectio
n
3
g
iv
es
th
e
ex
p
e
r
im
en
tal
m
e
th
o
d
o
lo
g
y
.
I
n
s
ec
tio
n
4
,
a
u
th
o
r
s
g
iv
e
th
e
r
esu
lts
in
clu
d
in
g
a
co
m
p
r
eh
en
s
iv
e
d
is
cu
s
s
io
n
o
f
th
e
o
u
tco
m
es
as
well
as
an
an
aly
s
is
o
f
th
e
o
b
tain
ed
r
esu
lts
.
I
n
s
ec
tio
n
5
,
au
th
o
r
s
p
r
o
v
id
e
lim
itatio
n
s
an
d
th
e
p
o
s
s
ib
ilit
y
o
f
ex
ten
d
in
g
.
Sectio
n
6
co
n
cl
u
d
es
th
is
p
ap
er
an
d
g
iv
es f
u
tu
r
e
r
esear
ch
.
2.
RE
L
AT
E
D
WO
RK
L
o
ad
b
alan
cin
g
alg
o
r
ith
m
s
h
av
e
b
ee
n
th
e
s
u
b
ject
o
f
r
ec
e
n
t
s
tu
d
ies
in
th
e
liter
atu
r
e.
Mish
r
a
et
a
l.
[
1
]
in
tr
o
d
u
ce
s
a
tax
o
n
o
m
y
f
o
r
clo
u
d
lo
a
d
b
alan
cin
g
alg
o
r
ith
m
s
,
ex
p
lo
r
in
g
k
ey
p
er
f
o
r
m
a
n
ce
p
ar
am
eter
s
an
d
th
eir
im
p
ac
ts
.
Per
f
o
r
m
an
ce
an
aly
s
is
o
f
h
eu
r
is
tic
-
b
ased
alg
o
r
ith
m
s
is
co
n
d
u
cted
u
s
in
g
th
e
C
lo
u
d
Sim
s
im
u
lato
r
,
with
a
d
etailed
p
r
esen
tatio
n
o
f
t
h
e
r
esu
lts
.
Usi
n
g
th
e
s
am
e
s
im
u
lato
r
,
E
ln
ag
a
r
et
a
l.
[
2
]
p
r
o
p
o
s
es
a
n
ew
alg
o
r
ith
m
th
at
r
ed
u
ce
s
r
esp
o
n
s
e
tim
e
a
n
d
p
r
o
ce
s
s
in
g
tim
e
m
etr
ics
c
o
m
p
ar
ed
to
th
e
co
m
m
o
n
alg
o
r
ith
m
s
tr
an
s
latio
n
lo
o
k
asid
e
b
u
f
f
er
(
TLB
)
,
r
o
u
n
d
r
o
b
in
(
RR
)
,
an
d
ap
p
r
o
x
im
ate
m
ax
im
u
m
lo
ad
b
ala
n
cin
g
(
AM
L
B
)
.
I
t
im
p
r
o
v
es
th
e
d
is
tr
ib
u
tio
n
o
f
task
s
b
etwe
en
d
if
f
er
e
n
t
VM
s
b
y
r
ed
u
cin
g
th
e
lo
ad
in
g
g
ap
b
etwe
en
th
e
h
ea
v
iest
lo
ad
ed
an
d
th
e
lig
h
test
lo
ad
ed
VM
s
wi
th
s
ig
n
if
ican
t v
alu
e.
J
u
n
aid
et
a
l.
[
3
]
p
r
o
p
o
s
e
th
e
d
ata
f
iles
ty
p
e
f
o
r
m
attin
g
(
DFTF)
lo
ad
b
alan
cin
g
alg
o
r
ith
m
,
in
te
g
r
atin
g
a
m
o
d
if
ied
ca
t
s
war
m
o
p
tim
izatio
n
(
C
SO)
an
d
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
es
(
SVM
)
class
if
ier
s
to
class
if
y
clo
u
d
d
ata.
Simu
latio
n
r
esu
lts
d
em
o
n
s
tr
ate
im
p
r
o
v
ed
p
e
r
f
o
r
m
a
n
ce
m
etr
ics
co
m
p
ar
ed
t
o
ex
is
tin
g
ap
p
r
o
ac
h
es.
T
h
e
r
ev
iew
in
s
tu
d
y
[
4
]
aim
s
to
cr
itically
an
aly
ze
ex
is
tin
g
lo
ad
b
alan
cin
g
tech
n
iq
u
es,
d
is
cu
s
s
in
g
p
ar
am
eter
s
lik
e
th
r
o
u
g
h
p
u
t,
m
ig
r
atio
n
tim
e,
an
d
s
ca
lab
ilit
y
.
I
t
h
ig
h
lig
h
ts
th
e
s
h
o
r
tco
m
in
g
s
o
f
tr
a
d
itio
n
al
lo
ad
b
alan
cin
g
(
L
B
)
alg
o
r
ith
m
s
in
clo
u
d
co
m
p
u
tin
g
an
d
ad
v
o
c
ates
f
o
r
in
teg
r
atin
g
f
au
lt
to
ler
an
ce
(
FT)
m
etr
ics,
p
r
o
p
o
s
in
g
a
n
o
v
el
FT
-
b
ased
L
B
alg
o
r
ith
m
to
ad
d
r
ess
th
is
n
ee
d
.
I
n
th
e
s
am
e
way
,
Oy
ed
ir
an
et
a
l.
[
5
]
cite
co
m
m
o
n
c
h
allen
g
es
a
n
d
b
en
ef
its
o
f
th
e
m
o
s
t
c
o
m
m
o
n
tec
h
n
iq
u
es
o
f
lo
a
d
b
alan
cin
g
.
A
d
if
f
er
en
t
ap
p
r
o
ac
h
to
s
tu
d
y
in
g
lo
ad
b
alan
cin
g
in
clo
u
d
co
m
p
u
tin
g
i
n
v
o
lv
es
lev
er
a
g
i
n
g
s
o
f
twar
e
-
d
ef
i
n
ed
n
etwo
r
k
in
g
(
SDN)
.
Yzz
o
g
h
a
n
d
B
en
ab
o
u
d
[
6
]
f
o
c
u
s
es
o
n
r
ec
en
t
r
esear
ch
h
ig
h
lig
h
tin
g
th
e
u
s
e
o
f
SDN
t
o
en
h
an
ce
lo
ad
b
alan
cin
g
in
clo
u
d
en
v
ir
o
n
m
e
n
ts
.
Fu
r
th
er
m
o
r
e,
Halim
a
et
a
l.
[
7
]
g
iv
es
a
co
m
p
ar
ativ
e
s
tu
d
y
ex
p
lo
r
in
g
th
e
cr
itical
r
o
le
o
f
p
r
ed
ictiv
e
lo
a
d
b
alan
cin
g
.
I
n
th
e
s
am
e
co
n
tex
t,
Ar
o
n
a
n
d
Ab
r
ah
am
[
8
]
d
is
cu
s
s
th
e
p
er
f
o
r
m
a
n
ce
o
f
p
o
p
u
lar
lo
ad
b
alan
cin
g
alg
o
r
ith
m
s
an
d
tech
n
iq
u
es.
Ho
wev
er
,
wh
ile
th
ey
d
escr
ib
e
v
ar
io
u
s
lo
ad
b
alan
cin
g
s
ch
em
es
with
p
r
o
p
o
s
itio
n
s
an
d
co
n
d
itio
n
s
d
ep
en
d
i
n
g
o
n
th
e
clo
u
d
en
v
ir
o
n
m
en
t,
th
ey
o
n
ly
f
o
cu
s
o
n
a
f
ew
alg
o
r
ith
m
s
an
d
o
v
er
l
o
o
k
s
o
m
e
d
ev
el
o
p
ed
o
n
es,
s
u
ch
as
th
e
h
o
n
ey
-
b
ee
f
o
r
ag
in
g
alg
o
r
ith
m
[
9
]
an
d
th
e
o
p
tim
ized
g
e
n
etic
alg
o
r
ith
m
[
1
0
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
C
h
a
llen
g
es o
f lo
a
d
b
a
l
a
n
cin
g
a
lg
o
r
ith
ms in
clo
u
d
c
o
mp
u
tin
g
u
tili
z
in
g
…
(
A
n
o
u
a
r
B
en
H
a
lima
)
3451
Ma
n
y
a
lg
o
r
it
h
m
s
h
a
v
e
b
ee
n
i
m
p
le
m
e
n
te
d
,
b
u
t
t
h
e
r
es
u
lts
a
r
e
n
o
t
ef
f
i
cie
n
t
b
e
ca
u
s
e
r
ese
a
r
ch
er
s
h
a
v
e
f
o
c
u
s
e
d
o
n
s
o
m
e
m
et
r
i
cs
w
h
i
l
e
i
g
n
o
r
i
n
g
o
t
h
e
r
s
.
T
h
er
ef
o
r
e
,
a
ll
t
h
e
al
g
o
r
it
h
m
s
,
wh
et
h
e
r
m
e
n
ti
o
n
e
d
in
t
h
e
cit
e
d
ar
t
icl
e
o
r
n
o
t,
tr
ea
t
lo
a
d
b
ala
n
c
in
g
as
an
i
n
d
i
v
i
d
u
al
m
e
tr
ic
.
T
h
u
s
,
s
cie
n
t
is
ts
t
r
y
to
s
atis
f
y
o
n
ly
o
n
e
t
o
s
i
x
m
et
r
ics
at
m
o
s
t
.
T
h
e
r
e
f
o
r
e,
w
e
s
h
o
u
ld
atte
m
p
t
t
o
s
o
l
v
e
th
e
p
r
o
b
le
m
a
s
ass
o
c
iat
ed
s
u
b
-
p
r
o
b
l
em
s
o
f
m
et
r
i
cs.
U
n
li
k
e
th
e
m
aj
o
r
it
y
o
f
p
r
e
v
i
o
u
s
s
t
u
d
ies
th
a
t
m
e
n
t
io
n
t
h
e
l
o
a
d
b
ala
n
c
in
g
p
r
o
b
le
m
,
o
u
r
s
t
u
d
y
u
s
es
a
n
o
v
e
l
s
t
atis
tic
al
ap
p
r
o
ac
h
t
h
at
r
el
ies
o
n
d
at
a
m
i
n
i
n
g
t
o
o
ls
t
ak
in
g
i
n
t
o
ac
c
o
u
n
t
th
e
m
et
r
i
cs
o
f
l
o
a
d
b
ala
n
ci
n
g
.
I
n
o
t
h
e
r
w
o
r
d
s
,
th
e
p
r
io
r
cit
ati
o
n
s
w
er
e
f
o
u
n
d
e
d
o
n
t
h
ese
m
e
tr
i
cs
as
c
o
m
p
a
r
is
o
n
r
es
ea
r
c
h
.
C
o
n
v
er
s
el
y
,
we
wil
l
a
d
d
r
ess
t
h
e
m
a
n
d
tak
e
i
n
t
o
c
o
n
s
i
d
e
r
at
io
n
a
n
ew
s
tr
a
te
g
y
b
y
l
ev
er
a
g
i
n
g
th
ese
m
etr
ics
t
o
t
r
a
n
s
f
o
r
m
t
h
e
m
i
n
t
o
r
ele
v
a
n
t
in
f
o
r
m
a
ti
o
n
u
s
i
n
g
d
a
ta
m
i
n
i
n
g
to
d
et
e
r
m
i
n
e
t
h
e
c
o
m
p
le
x
i
ty
f
o
r
u
s
er
s
t
o
s
ele
ct
o
n
e
t
ec
h
n
i
q
u
e
o
f
lo
ad
b
al
an
ci
n
g
.
3.
M
E
T
H
O
D
Me
th
o
d
o
lo
g
ically
,
th
is
s
tu
d
y
co
llects
an
d
o
r
g
an
izes
p
r
ev
io
u
s
s
tu
d
ies
in
to
a
s
tr
u
ctu
r
ed
tab
le
f
o
r
m
at
b
ef
o
r
e
im
p
lem
en
tin
g
u
p
d
ates
d
u
e
to
r
ec
e
n
t
s
tu
d
ies.
Su
b
s
eq
u
en
tly
,
a
c
o
m
p
a
r
ativ
e
a
n
aly
s
is
,
em
p
lo
y
in
g
an
aly
tical
m
eth
o
d
o
lo
g
ies
an
d
d
ata
m
in
i
n
g
tech
n
iq
u
es,
is
u
n
d
er
tak
en
to
e
x
am
in
e
a
n
d
ev
alu
ate
th
ese
alg
o
r
ith
m
s
,
th
e
r
eb
y
d
em
o
n
s
tr
atin
g
th
e
c
h
allen
g
es
in
h
er
en
t
i
n
ac
h
iev
in
g
ef
f
icien
t
lo
ad
b
alan
cin
g
.
Data
m
i
n
in
g
is
th
e
p
r
o
ce
s
s
o
f
ex
t
r
ac
tin
g
v
alu
ab
le
in
f
o
r
m
atio
n
an
d
p
at
ter
n
s
f
r
o
m
m
ass
iv
e
am
o
u
n
ts
o
f
d
ata.
I
t
co
v
er
s
s
tatis
t
ical
m
eth
o
d
s
as we
l
l a
s
c
o
llectio
n
,
ex
tr
ac
tio
n
,
a
n
aly
s
is
,
an
d
s
tatis
tical
tech
n
iq
u
es.
I
t is also
k
n
o
wn
as th
e
k
n
o
wled
g
e
d
is
co
v
e
r
y
p
r
o
ce
s
s
,
k
n
o
wled
g
e
m
in
in
g
f
r
o
m
d
ata,
o
r
d
ata/p
atter
n
an
al
y
s
is
.
Data
m
in
in
g
is
a
lo
g
ical
p
r
o
ce
s
s
o
f
lo
ca
tin
g
p
er
ti
n
en
t
in
f
o
r
m
atio
n
to
u
n
d
e
r
s
tan
d
th
e
d
ata.
Ad
d
itio
n
ally
,
t
h
e
ter
m
d
ata
m
i
n
in
g
en
co
m
p
ass
es
m
an
y
tec
h
n
iq
u
e
s
an
d
p
r
o
ce
d
u
r
es
u
s
ed
to
ex
a
m
in
e
an
d
tr
an
s
f
o
r
m
d
ata.
T
h
i
s
p
ap
er
f
o
cu
s
es
o
n
two
im
p
o
r
tan
t
m
eth
o
d
s
:
class
i
f
icatio
n
an
d
clu
s
ter
in
g
.
T
o
a
n
aly
ze
d
atasets
o
f
th
e
cr
iter
ia
c
ited
in
T
ab
le
1
,
we
in
tr
o
d
u
ce
s
o
m
e
im
p
o
r
tan
t
d
ata
m
in
in
g
tech
n
iq
u
es.
T
h
ese
t
o
o
ls
h
elp
u
s
b
etter
u
n
d
e
r
s
tan
d
th
e
p
r
o
b
lem
o
f
th
e
co
m
p
ar
ativ
e
s
tu
d
y
o
f
th
e
m
o
s
t
p
o
p
u
la
r
alg
o
r
ith
m
s
m
en
tio
n
e
d
in
T
ab
le
1
.
C
las
s
if
icatio
n
(
also
k
n
o
wn
as
class
if
icatio
n
tr
ee
s
o
r
d
ec
is
io
n
tr
ee
s
)
is
a
d
ata
m
i
n
in
g
al
g
o
r
ith
m
th
at
cr
ea
tes
a
s
tep
-
by
-
s
tep
g
u
id
e
f
o
r
d
eter
m
in
in
g
th
e
o
u
t
p
u
t
o
f
a
n
ew
d
ata
in
s
tan
ce
.
T
h
e
tr
ee
it
cr
ea
tes
r
ep
r
esen
ts
a
d
ec
is
io
n
-
m
ak
in
g
p
r
o
ce
s
s
,
wh
e
r
e
ea
ch
n
o
d
e
in
th
e
tr
ee
r
e
p
r
esen
ts
a
s
p
o
t
wh
e
r
e
a
d
ec
is
io
n
m
u
s
t
b
e
m
a
d
e
b
ased
o
n
th
e
in
p
u
t.
Mo
v
in
g
to
t
h
e
n
ex
t
n
o
d
e
d
ep
en
d
s
o
n
th
e
d
ec
i
s
io
n
,
an
d
we
c
o
n
tin
u
e
u
n
til
w
e
r
ea
ch
a
leaf
th
at
p
r
ed
icts
th
e
o
u
tp
u
t.
I
n
o
u
r
ex
p
er
ien
ce
,
we
u
s
e
J
4
8
,
wh
ich
ca
n
b
u
ild
a
m
o
d
el
an
d
cr
ea
te
d
e
cisi
o
n
tr
ee
s
o
f
d
ata
s
ets
b
ased
o
n
th
ei
r
attr
ib
u
tes.
T
h
e
o
b
jectiv
e
o
f
d
ec
is
io
n
tr
ee
s
is
to
p
r
o
g
r
ess
iv
ely
g
e
n
er
aliz
e
th
e
d
ec
is
io
n
tr
ee
u
n
til
it
r
ea
ch
es
a
b
alan
ce
b
etwe
en
f
lex
ib
ilit
y
an
d
ac
cu
r
ac
y
.
J
4
8
is
an
ex
ten
s
io
n
o
f
iter
a
tiv
e
Dich
o
to
m
i
z
er
(
I
D3
)
t
h
at
ac
co
u
n
ts
f
o
r
m
is
s
in
g
v
alu
es,
d
ec
is
io
n
tr
ee
p
r
u
n
in
g
,
co
n
tin
u
o
u
s
attr
ib
u
te
v
alu
e
r
a
n
g
es,
an
d
d
er
iv
atio
n
o
f
r
u
les.
C
lu
s
ter
in
g
is
an
o
th
er
to
o
l
f
o
r
an
aly
zin
g
d
ata.
Giv
en
a
s
et
o
f
d
ata
p
o
in
ts
,
we
ca
n
u
s
e
a
clu
s
ter
in
g
alg
o
r
ith
m
to
class
if
y
ea
ch
d
ata
p
o
in
t
in
to
ce
r
tain
g
r
o
u
p
s
.
K
-
m
ea
n
s
is
am
o
n
g
th
e
m
o
s
t
well
-
k
n
o
wn
clu
s
ter
in
g
alg
o
r
ith
m
s
.
T
h
at
i
s
tau
g
h
t
in
m
an
y
in
tr
o
d
u
cto
r
y
d
ata
s
cien
ce
an
d
m
ac
h
i
n
e
lear
n
in
g
p
a
tter
n
s
,
b
u
t
t
h
e
o
n
e
d
is
ad
v
an
tag
e
o
f
K
-
m
ea
n
s
is
th
at
we
m
u
s
t c
h
o
o
s
e
th
e
v
alu
e
o
f
K
b
ef
o
r
e
r
u
n
n
in
g
th
e
alg
o
r
ith
m
.
Fo
r
o
u
r
ca
s
e,
K
r
ep
r
esen
ts
th
e
n
u
m
b
e
r
o
f
d
ata
g
r
o
u
p
s
cr
ea
ted
u
s
in
g
th
e
cr
iter
ia
k
ey
s
.
T
h
e
n
we
v
ar
y
it
to
c
o
n
tr
o
l
th
e
n
u
m
b
er
o
f
cr
ea
ted
g
r
o
u
p
s
as
n
ee
d
ed
.
I
n
o
th
er
wo
r
d
s
,
“
K
”
r
ep
r
esen
ts
th
e
g
r
o
u
p
s
o
f
alg
o
r
it
h
m
s
th
at
h
a
v
e
th
e
s
am
e
cr
iter
ia
attr
ib
u
tes.
C
lu
s
ter
in
g
allo
ws
u
s
er
s
to
m
ak
e
g
r
o
u
p
s
o
f
d
ata
to
d
eter
m
in
e
p
atter
n
s
f
r
o
m
th
e
d
ata.
C
lu
s
ter
in
g
h
as
its
ad
v
an
tag
es
wh
en
th
e
d
ata
s
et
is
d
ef
in
ed
,
an
d
a
g
en
er
al
p
atter
n
n
ee
d
s
to
b
e
d
eter
m
in
e
d
f
r
o
m
t
h
e
d
ata.
On
e
d
ef
in
in
g
b
en
e
f
it
o
f
clu
s
ter
in
g
o
v
er
-
class
if
icatio
n
is
th
at
e
v
er
y
attr
ib
u
te
in
th
e
d
ata
s
et
is
u
s
ed
to
an
aly
ze
th
e
d
ata.
A
m
ajo
r
d
is
ad
v
an
ta
g
e
o
f
u
s
in
g
clu
s
ter
in
g
is
th
at
th
e
u
s
er
is
r
eq
u
ir
e
d
to
k
n
o
w
a
h
ea
d
o
f
tim
e
h
o
w
m
an
y
g
r
o
u
p
s
h
e
wan
ts
to
cr
ea
te.
T
o
im
p
lem
en
t
th
ese
two
m
en
tio
n
ed
to
o
ls
,
we
u
tili
ze
a
co
m
m
o
n
to
o
l
ca
lled
W
E
KA1
,
wh
ich
s
tan
d
s
f
o
r
“
W
aik
ato
e
n
v
ir
o
n
m
en
t
f
o
r
k
n
o
wled
g
e
a
n
aly
s
is
.
”
W
E
KA
is
a
co
llectio
n
o
f
m
ac
h
in
e
-
lear
n
in
g
al
g
o
r
ith
m
s
f
o
r
d
ata
m
i
n
in
g
.
I
t
in
clu
d
es
to
o
ls
f
o
r
d
ata
p
r
ep
ar
atio
n
,
class
if
icatio
n
,
r
eg
r
ess
io
n
,
clu
s
ter
in
g
,
ass
o
ciatio
n
r
u
les
m
in
in
g
,
an
d
v
is
u
aliza
tio
n
.
W
ek
a
also
i
n
clu
d
es
a
m
etr
ic
k
n
o
wn
as
s
q
u
ar
ed
er
r
o
r
,
ty
p
ic
ally
u
s
ed
to
ass
ess
r
eg
r
ess
io
n
m
o
d
els.
T
h
is
m
etr
ic
q
u
an
tifie
s
th
e
d
e
g
r
ee
to
w
h
ich
a
r
eg
r
ess
io
n
m
o
d
el
ac
c
u
r
ately
f
its
th
e
d
ata.
L
o
wer
s
q
u
ar
e
d
er
r
o
r
v
alu
es
i
n
d
icate
s
u
p
er
io
r
m
o
d
el
p
e
r
f
o
r
m
an
ce
.
Ad
d
itio
n
ally
,
ac
c
u
r
ate
m
o
d
el
e
v
alu
atio
n
u
s
in
g
W
ek
a
n
ec
ess
itates th
e
co
n
s
id
er
atio
n
o
f
co
r
r
ec
tly
class
if
ied
in
s
tan
ce
s
.
3
.
1
.
E
x
perim
ent
s
I
n
th
is
s
ec
tio
n
,
a
s
er
ies
o
f
ex
p
er
im
e
n
ts
h
av
e
b
ee
n
ar
r
an
g
ed
t
o
in
v
esti
g
ate
th
e
p
e
r
f
o
r
m
a
n
ce
ch
ar
ac
ter
is
tics
o
f
v
ar
io
u
s
alg
o
r
ith
m
s
.
T
h
e
alg
o
r
ith
m
s
u
n
d
er
co
n
s
id
er
atio
n
in
clu
d
e:
R
R
,
Dy
n
am
icR
R
,
Sh
o
r
test
J
o
b
Sch
ed
u
lin
g
,
Mi
n
-
Min
,
Ma
x
-
Min
,
o
p
p
o
r
tu
n
is
tic
lo
ad
b
ala
n
cin
g
(
OL
B
+
)
,
lo
ad
b
alan
cin
g
m
in
-
m
i
n
(
L
B
MM
)
,
co
s
t
lo
ad
b
alan
cin
g
with
v
ir
tu
al
m
ac
h
in
es
(
C
L
B
VM
)
,
p
r
ed
ictiv
e
ad
ap
tiv
e
lo
ad
b
alan
cin
g
(
PALB
)
,
f
au
lt
-
awa
r
e
m
in
-
m
in
lo
ad
b
alan
cin
g
(
FAML
B
)
,
th
r
o
ttled
,
Ho
n
ey
B
ee
Fo
r
ag
in
g
an
d
Activ
eCl
u
s
ter
in
g
.
Mo
r
e
Alg
o
r
ith
m
s
ar
e
d
ep
icted
in
T
a
b
le
1
.
T
h
ese
alg
o
r
ith
m
s
will
r
ep
r
esen
t
th
e
p
r
e
d
icted
class
o
f
o
u
r
cr
ea
te
d
m
o
d
el.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
3
,
J
u
n
e
20
25
:
3
4
4
9
-
3
4
5
7
3452
Fu
r
th
er
m
o
r
e
,
th
e
ev
alu
atio
n
o
f
th
ese
alg
o
r
ith
m
s
was
co
n
d
u
cte
d
b
ased
o
n
9
d
is
tin
ct
m
etr
ics,
n
am
ely
:
p
er
f
o
r
m
an
ce
,
th
r
o
u
g
h
p
u
t,
o
v
e
r
h
ea
d
,
to
ler
an
t,
m
ig
r
atio
n
tim
e,
r
esp
o
n
s
e
tim
e,
r
eso
u
r
ce
u
til
izatio
n
,
s
ca
lab
ilit
y
,
an
d
p
o
wer
s
av
in
g
.
W
e
r
an
o
u
r
ex
p
er
i
m
en
tal
s
tu
d
y
ag
ain
s
t
t
h
e
3
2
alg
o
r
ith
m
s
as
p
r
o
o
f
o
f
co
n
ce
p
t
d
u
r
in
g
th
e
tr
ain
in
g
s
et.
T
h
ese
m
etr
ics ar
e
co
n
s
id
er
ed
as in
p
u
ts
o
f
o
u
r
m
o
d
el
.
T
ab
le
1
.
C
o
m
p
a
r
ativ
e
tab
le
o
f
th
e
m
o
s
t im
p
o
r
ta
n
t a
lg
o
r
ith
m
s
A
l
g
o
r
i
t
h
m
P
e
r
f
o
r
m
a
n
c
e
T
h
r
o
u
g
h
p
u
t
O
v
e
r
h
e
a
d
T
o
l
e
r
a
n
t
M
i
g
r
a
t
i
o
n
t
i
m
e
R
e
s
p
o
n
s
e
t
i
m
e
R
e
s
o
u
r
c
e
u
t
i
l
i
z
a
t
i
o
n
S
c
a
l
a
b
i
l
i
t
y
P
o
w
e
r
s
a
v
i
n
g
RR
[
1
1
]
Y
e
s
Y
e
s
Y
e
s
N
o
N
o
Y
e
s
Y
e
s
Y
e
s
No
D
y
n
a
m
i
c
R
R
[
1
1
]
N
o
Y
e
s
Y
e
s
Y
e
s
Y
e
s
N
o
Y
e
s
N
o
No
S
h
o
r
t
e
s
t
J
o
b
S
h
e
d
u
l
i
n
g
[
1
2
]
N
o
N
o
N
o
N
o
N
o
N
o
Y
e
s
N
o
No
Min
-
M
i
n
[
1
3
]
Y
e
s
Y
e
s
Y
e
s
N
o
N
o
Y
e
s
Y
e
s
N
o
No
M
a
x
-
M
i
n
[
1
4
]
Y
e
s
Y
e
s
Y
e
s
N
o
N
o
Y
e
s
Y
e
s
N
o
No
O
L
B
+
L
B
M
M
[
1
5
]
Y
e
s
N
o
N
o
N
o
N
o
N
o
Y
e
s
N
o
No
C
L
B
V
M
[
1
6
]
Y
e
s
Y
e
s
N
o
N
o
N
o
Y
e
s
Y
e
s
N
o
No
P
A
L
B
[
1
7
]
N
o
Y
e
s
Y
e
s
Y
e
s
Y
e
s
Y
e
s
Y
e
s
N
o
Y
e
s
F
A
M
L
B
[
1
8
]
,
[
1
9
]
N
o
Y
e
s
Y
e
s
N
o
Y
e
s
Y
e
s
Y
e
s
Y
e
s
No
T
h
r
o
t
t
l
e
d
[
2
0
]
Y
e
s
N
o
N
o
Y
e
s
Y
e
s
Y
e
s
Y
e
s
Y
e
s
No
H
o
n
e
y
B
e
e
F
o
r
a
g
i
n
g
[
2
1
]
N
o
N
o
N
o
N
o
N
o
N
o
Y
e
s
N
o
No
A
c
t
i
v
e
C
l
u
s
t
e
r
i
n
g
[
2
2
]
N
o
N
o
Y
e
s
N
o
Y
e
s
N
o
Y
e
s
N
o
No
B
i
a
s
e
d
R
a
n
d
o
m
S
a
p
m
l
i
n
g
[
2
3
]
Y
e
s
Y
e
s
Y
e
s
N
o
N
o
N
o
N
o
Y
e
s
No
G
e
n
e
r
a
l
i
z
e
d
P
r
i
o
r
i
t
y
A
l
g
o
[
2
3
]
N
o
Y
e
s
N
o
N
o
Y
e
s
N
o
Y
e
s
N
o
No
J
o
i
n
I
d
l
e
Q
u
e
u
e
[
2
4
]
Y
e
s
N
o
Y
e
s
N
o
N
o
Y
e
s
N
o
N
o
No
G
e
n
e
t
e
c
A
l
g
o
r
i
t
h
m
[
2
5
]
Y
e
s
N
o
N
o
N
o
N
o
N
o
Y
e
s
N
o
No
A
n
t
C
o
l
o
n
y
[
2
6
]
Y
e
s
N
o
N
o
N
o
Y
e
s
N
o
Y
e
s
N
o
No
S
t
o
c
h
a
s
t
i
c
H
i
l
l
C
l
i
m
b
i
n
g
T
e
c
h
[
2
7
]
Y
e
s
Y
e
s
N
o
N
o
N
o
Y
e
s
Y
e
s
N
o
No
D
e
c
e
n
t
r
a
l
i
z
e
C
o
n
t
e
n
t
A
w
a
r
e
[
2
8
]
Y
e
s
N
o
Y
e
s
N
o
N
o
Y
e
s
Y
e
s
Y
e
s
No
S
e
r
v
e
r
-
b
a
s
e
d
L
B
F
o
r
I
D
S
e
r
v
i
c
e
s
[
2
9
]
Y
e
s
N
o
N
o
N
o
N
o
Y
e
s
N
o
N
o
No
L
o
c
k
-
f
r
e
e
M
u
l
t
i
-
p
r
o
c
e
s
s
i
n
g
[
3
0
]
Y
e
s
Y
e
s
N
o
N
o
N
o
N
o
N
o
N
o
No
S
c
h
e
d
u
l
i
n
g
[
3
1
]
N
o
N
o
Y
e
s
N
o
N
o
N
o
Y
e
s
N
o
No
L
o
a
d
b
a
l
a
n
c
i
n
g
v
i
r
t
u
a
l
s
t
o
r
a
g
e
s
t
r
a
t
e
g
y
(
L
B
V
S
)
[
3
2
]
Y
e
s
N
o
N
o
Y
e
s
N
o
Y
e
s
N
o
Y
e
s
No
T
a
s
k
S
h
e
d
u
l
i
n
g
b
a
s
e
d
O
n
L
B
[
3
3
]
Y
e
s
N
o
N
o
N
o
N
o
Y
e
s
Y
e
s
N
o
No
A
n
t
c
o
l
o
n
y
a
n
d
c
o
m
p
l
e
x
n
e
t
w
o
r
k
t
h
e
o
r
y
b
a
s
e
d
l
o
a
d
b
a
l
a
n
c
i
n
g
(
A
C
C
L
B
)
[
3
3
]
Y
e
s
N
o
N
o
N
o
N
o
Y
e
s
Y
e
s
N
o
No
E
v
e
n
t
D
r
i
v
e
n
[
3
4
]
N
o
N
o
N
o
N
o
N
o
N
o
Y
e
s
Y
e
s
No
C
A
R
T
O
N
[
3
5
]
Y
e
s
N
o
Y
e
s
N
o
N
o
N
o
Y
e
s
N
o
Y
e
s
C
e
n
t
r
a
l
l
o
a
d
b
a
l
a
n
c
e
r
(
C
A
B
)
[
3
6
]
N
o
N
o
Y
e
s
N
o
Y
e
s
N
o
Y
e
s
N
o
No
V
e
c
t
o
r
d
o
t
[
3
7
]
N
o
N
o
N
o
N
o
N
o
N
o
Y
e
s
N
o
No
S
i
m
u
l
a
t
e
d
a
n
n
e
a
l
i
n
g
(
S
A
)
[
3
8
]
N
o
N
o
N
o
N
o
N
o
Y
e
s
Y
e
s
N
o
Y
e
s
L
o
a
d
f
o
r
e
c
a
s
t
i
n
g
a
n
d
c
a
p
a
c
i
t
y
-
b
a
s
e
d
(
L
F
C
B
)
[
3
6
]
Y
e
s
Y
e
s
N
o
N
o
N
o
N
o
N
o
N
o
No
G
l
o
b
a
l
l
o
a
d
b
a
l
a
n
c
i
n
g
s
t
r
a
t
e
g
y
(
G
L
B
S
)
[
3
9
]
Y
e
s
N
o
N
o
N
o
N
o
Y
e
s
N
o
N
o
No
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
4
.
1
.
Resul
t
s
Ach
iev
in
g
ef
f
ec
tiv
e
lo
ad
b
ala
n
cin
g
is
a
p
a
r
am
o
u
n
t
ch
allen
g
e
in
m
an
a
g
in
g
r
eso
u
r
ce
s
wit
h
in
clo
u
d
co
m
p
u
tin
g
en
v
ir
o
n
m
en
ts
.
Desp
ite
im
p
lem
en
tin
g
v
a
r
io
u
s
alg
o
r
ith
m
s
,
th
e
o
u
tco
m
es
o
f
ten
f
all
s
h
o
r
t
o
f
m
ee
tin
g
th
e
co
m
p
r
eh
e
n
s
iv
e
m
etr
ic
o
u
t
lin
ed
in
th
e
co
m
p
ar
ativ
e
T
a
b
l
e
1
.
T
h
e
tab
u
lated
d
ata
u
n
d
er
s
co
r
es
th
at
n
o
t
all
alg
o
r
ith
m
s
s
atis
f
y
all
s
p
ec
if
ied
m
etr
ics.
I
n
s
tan
ce
s
wh
er
e
th
e
tab
le
en
tr
ies
ar
e
m
ar
k
ed
with
“
NO
”
d
en
o
te
u
n
f
av
o
r
ab
le
o
u
tco
m
es
in
t
h
is
m
etr
ic,
w
h
ile
“
y
es
”
s
ig
n
if
ies
th
at
th
e
alg
o
r
ith
m
i
n
co
r
p
o
r
at
es
th
is
m
etr
ic.
Fo
r
in
s
tan
ce
,
co
s
t
lo
ad
b
alan
ci
n
g
with
d
y
n
a
m
ic
m
ig
r
atio
n
(
C
L
B
DM
)
ex
ce
ls
in
th
r
o
u
g
h
p
u
t
b
u
t
ex
h
i
b
its
s
h
o
r
tco
m
in
g
s
in
te
r
m
s
o
f
s
p
e
ed
an
d
c
o
m
p
lex
ity
.
An
o
th
e
r
il
lu
s
tr
ativ
e
ex
am
p
le
is
L
B
Min
-
Ma
x
,
wh
ich
m
ee
ts
s
ev
en
m
etr
ics
s
atis
f
ac
to
r
ily
;
h
o
wev
er
,
its
r
eq
u
est
tim
e
is
ex
ce
s
s
iv
ely
lo
w,
lead
i
n
g
t
o
p
r
o
lo
n
g
e
d
u
s
er
wait
tim
es
f
o
r
r
esp
o
n
s
e
r
ec
ep
tio
n
.
I
t
is
cr
u
cial
to
em
p
h
asize
th
at
ce
r
tain
m
etr
ics
ar
e
d
ee
m
e
d
m
o
r
e
p
i
v
o
tal
f
o
r
th
e
u
s
er
’
s
n
ee
d
s
th
an
o
th
er
s
in
th
e
ev
alu
atio
n
p
r
o
ce
s
s
.
T
ak
in
g
th
is
tab
u
lated
d
ata,
we
p
r
esen
t
th
e
o
u
tco
m
es
o
f
o
u
r
ex
p
er
im
e
n
ts
f
o
r
b
o
th
class
if
icatio
n
an
d
clu
s
ter
in
g
.
T
h
is
p
r
esen
tatio
n
d
r
aws
u
p
o
n
th
e
in
f
o
r
m
atio
n
in
T
ab
le
1
,
illu
s
tr
atin
g
th
e
m
o
s
t
ef
f
ec
tiv
e
alg
o
r
ith
m
s
b
ased
o
n
cr
u
cial
m
etr
ics.
T
o
elab
o
r
ate,
we
tak
e
th
e
d
ata
f
r
o
m
th
e
tab
le
an
d
tr
an
s
f
o
r
m
it
in
to
a
s
tan
d
ar
d
d
atab
ase
f
o
r
m
at
f
o
r
c
o
n
d
u
ctin
g
th
e
ex
p
er
im
en
ts
.
E
f
f
o
r
ts
wer
e
d
ir
ec
ted
to
war
d
in
teg
r
at
in
g
two
d
ata
m
in
i
n
g
to
o
ls
class
if
icatio
n
an
d
clu
s
ter
in
g
to
tr
an
s
f
o
r
m
th
e
ac
cu
m
u
lat
ed
d
ata
in
to
e
x
p
lo
itab
le
in
f
o
r
m
atio
n
.
T
h
e
tab
le
h
as
th
ir
teen
-
two
r
o
ws
o
f
d
ata
i
n
s
tan
ce
s
an
d
te
n
m
etr
ics
as
attr
ib
u
tes.
T
h
e
i
n
itial
p
h
ase
o
f
th
e
ex
p
e
r
im
en
tal
p
r
o
ce
s
s
in
v
o
lv
es
d
elin
ea
tin
g
th
e
in
p
u
ts
an
d
o
u
tp
u
ts
o
f
t
h
e
d
ataset
s
.
T
h
e
d
ata
in
p
u
ts
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
C
h
a
llen
g
es o
f lo
a
d
b
a
l
a
n
cin
g
a
lg
o
r
ith
ms in
clo
u
d
c
o
mp
u
tin
g
u
tili
z
in
g
…
(
A
n
o
u
a
r
B
en
H
a
lima
)
3453
en
co
m
p
ass
m
etr
ics
s
u
ch
as
s
c
alab
ilit
y
,
p
er
f
o
r
m
a
n
ce
,
a
n
d
th
r
o
u
g
h
p
u
t,
wh
ile
th
e
s
in
g
u
lar
o
u
tp
u
t
tar
g
eted
f
o
r
p
r
ed
ictio
n
p
e
r
tain
s
to
th
e
p
r
i
m
ar
y
co
lu
m
n
in
th
e
tab
le
d
e
n
o
ted
as
“
alg
o
r
ith
m
”
.
T
h
is
s
er
v
es
as
th
e
ce
n
tr
al
q
u
an
d
a
r
y
th
at
r
esear
ch
er
s
en
d
ea
v
o
r
to
a
d
d
r
ess
.
4
.
1
.
1
.
Cla
s
s
if
ica
t
io
n
Star
tin
g
with
th
e
class
if
icatio
n
to
o
l
wh
e
n
b
u
il
d
in
g
a
m
o
d
e
l,
th
e
ex
p
e
r
im
en
tal
class
if
icatio
n
s
h
o
ws
th
at
twelv
e
d
ata
in
s
tan
ce
s
(
r
o
ws)
ar
e
c
o
r
r
ec
tly
class
if
ied
,
wh
ile
twen
ty
ar
e
n
o
t.
T
h
e
c
r
ea
ted
m
o
d
el
h
as
a
s
q
u
ar
ed
er
r
o
r
r
ate
o
f
m
o
r
e
th
an
s
ix
teen
-
two
p
er
ce
n
t.
Ad
d
it
io
n
ally
,
th
e
d
ec
is
io
n
tr
ee
g
en
e
r
ated
u
s
in
g
th
e
J
4
8
alg
o
r
ith
m
h
as
twelv
e
leav
es
o
f
alg
o
r
ith
m
s
as
o
u
tco
m
es,
with
th
e
“
p
er
f
o
r
m
an
ce
”
m
etr
ic
attr
ib
u
te
r
ep
r
esen
ted
at
th
e
r
o
o
t
o
f
th
e
tr
ee
,
ex
p
lain
in
g
th
at
th
e
f
lo
w
o
f
d
ec
is
io
n
s
m
u
s
t
s
tar
t
with
th
e
“
Per
f
o
r
m
an
ce
”
m
etr
ic.
Als
o
,
th
e
d
ec
is
io
n
tr
ee
o
f
t
h
e
m
o
d
e
l
g
en
er
ates
ju
s
t
twelv
e
r
esu
lts
th
at
ca
n
n
o
t
b
e
ac
h
ie
v
ed
to
co
v
er
all
th
e
o
th
e
r
alg
o
r
ith
m
s
m
e
n
tio
n
ed
in
T
ab
l
e
1
.
T
h
ese
m
etr
ics
a
r
e
r
e
p
r
ese
n
ted
in
g
r
a
y
c
o
lo
r
i
n
Fig
u
r
e
1
,
an
d
alg
o
r
it
h
m
s
ar
e
d
is
p
lay
ed
in
b
lu
e
in
th
e
s
am
e
f
ig
u
r
e.
T
h
er
ef
o
r
e,
t
h
e
f
lo
w
o
f
th
e
d
ec
is
io
n
tr
ee
h
as
m
an
y
am
b
ig
u
ities
as
we
ca
n
cite;
f
o
r
ex
am
p
le,
th
e
n
o
d
e
m
e
n
tio
n
ed
“
th
r
o
u
g
h
p
u
t
”
led
t
o
an
“
o
v
er
h
ea
d
”
n
o
d
e
ev
en
if
th
e
d
ec
is
io
n
is
“
y
es
”
o
r
“
no
”
;
th
u
s
,
th
e
y
led
to
t
h
e
s
am
e
m
etr
ic.
Fig
u
r
e
1
.
View
o
f
th
e
tr
ee
u
s
in
g
th
e
J
4
8
alg
o
r
ith
m
m
eth
o
d
to
an
aly
ze
d
ata
in
s
tan
ce
s
f
r
o
m
th
e
tab
le
r
esu
lt
4
.
1
.
2
.
Clus
t
er
ing
Fo
r
th
e
clu
s
ter
in
g
a
n
aly
s
is
,
we
u
s
e
th
e
K
-
m
ea
n
s
alg
o
r
ith
m
,
an
d
th
u
s
we
v
ar
y
th
e
v
al
u
e
o
f
“
K
”
th
at
r
em
ain
s
to
s
ev
er
al
d
esire
d
al
g
o
r
ith
m
s
th
at
h
av
e
t
h
e
s
am
e
m
etr
ic.
Fo
r
in
s
tan
ce
,
with
K
=
5
,
we
o
b
tain
a
m
o
d
el
with
a
s
q
u
ar
ed
e
r
r
o
r
o
f
4
2
,
w
h
ile
f
o
r
K
=
1
0
,
we
o
b
tain
te
n
cl
u
s
ter
ed
in
s
tan
ce
s
with
a
s
q
u
ar
ed
er
r
o
r
eq
u
al
to
2
4
.
As
we
in
cr
ea
s
e
th
e
v
alu
e
o
f
K,
th
e
s
q
u
ar
ed
er
r
o
r
d
ec
r
ea
s
es.
F
in
ally
,
f
o
r
K
eq
u
al
to
o
r
g
r
ea
ter
th
an
2
3
,
th
e
s
q
u
ar
ed
er
r
o
r
s
tay
s
ze
r
o
,
wh
ich
m
ea
n
s
a
clea
n
an
d
ac
cr
u
e
d
m
o
d
el
b
u
t
u
n
lik
ely
with
an
im
p
o
r
tan
t
v
al
u
e
o
f
K
th
at
in
d
icate
s
s
ev
er
al
alg
o
r
ith
m
g
r
o
u
p
s
th
at
we
ar
e
wo
r
k
in
g
to
ad
d
r
ess
.
T
ab
le
2
s
h
o
ws
t
h
e
v
ar
iatio
n
o
f
th
e
s
q
u
ar
ed
er
r
o
r
s
with
th
e
d
i
f
f
er
en
t
v
alu
es
o
f
K
(
n
u
m
b
er
o
f
d
esire
d
alg
o
r
ith
m
s
)
,
an
d
th
e
ass
o
ciate
d
g
r
ap
h
is
s
h
o
wn
in
Fig
u
r
e
2
r
ep
r
esen
t
s
th
e
v
ar
iatio
n
o
f
s
q
u
ar
ed
er
r
o
r
d
u
r
in
g
ch
a
n
g
in
g
K
v
alu
e
th
at
co
n
tr
o
ls
th
e
in
s
tan
ce
s
h
av
in
g
th
e
s
am
e
m
et
r
ics o
r
in
o
th
e
r
wo
r
d
s
,
n
u
m
b
er
o
f
alg
o
r
ith
m
s
h
av
in
g
th
e
s
am
e
m
etr
ics.
T
ab
le
2
.
Var
iatio
n
o
f
s
q
u
ar
ed
er
r
o
r
d
ep
en
d
in
g
o
n
K
v
al
u
e
V
a
l
u
e
o
f
K
(
n
u
m
b
e
r
o
f
a
l
g
o
r
i
t
h
ms)
S
q
u
a
r
e
d
e
r
r
o
r
2
68
5
42
7
10
20
21
32
33
24
5
4
0
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
3
,
J
u
n
e
20
25
:
3
4
4
9
-
3
4
5
7
3454
Fig
u
r
e
2
.
Gr
a
p
h
v
iew
o
f
th
e
v
ar
iatio
n
o
f
s
q
u
ar
ed
e
r
r
o
r
s
d
ep
e
n
d
in
g
o
n
K
(
n
u
m
b
er
o
f
alg
o
r
it
h
m
s
g
r
o
u
p
)
v
alu
e
4
.2
.
Dis
cu
s
s
io
n
I
n
g
e
n
er
al,
p
r
ev
i
o
u
s
s
tu
d
ies
h
av
e
ad
d
r
ess
ed
th
e
l
o
ad
b
alan
cin
g
is
s
u
e
b
y
d
is
cu
s
s
in
g
th
e
ad
v
an
tag
es
an
d
d
is
ad
v
a
n
tag
es
o
f
ea
ch
tech
n
iq
u
e
o
r
b
y
cr
ea
tin
g
a
h
ier
ar
ch
ical
tax
o
n
o
m
y
.
Ad
d
itio
n
a
lly
,
th
e
p
r
ev
ailin
g
ap
p
r
o
ac
h
in
ex
is
tin
g
s
tu
d
ies
i
n
v
o
lv
es
c
o
m
p
ilin
g
an
d
ca
te
g
o
r
izin
g
alg
o
r
ith
m
s
i
n
to
ta
b
les
with
o
u
t
ef
f
e
ctiv
ely
lev
er
ag
in
g
t
h
em
to
d
er
iv
e
m
e
an
in
g
f
u
l
i
n
s
ig
h
ts
o
r
ac
tio
n
a
b
l
e
in
f
o
r
m
atio
n
.
I
n
co
n
t
r
ast,
o
u
r
s
tu
d
y
in
tr
o
d
u
ce
s
a
n
ew
ap
p
r
o
ac
h
to
elu
cid
ate
th
e
co
m
p
le
x
ity
o
f
lo
a
d
b
alan
ci
n
g
in
ch
o
o
s
in
g
th
e
d
esire
d
te
ch
n
iq
u
e
to
ac
h
iev
e
u
s
er
s
’
o
b
jectiv
es
th
r
o
u
g
h
d
at
a
m
in
in
g
to
o
ls
.
T
o
t
h
is
en
d
,
t
h
e
ex
p
er
im
en
tal
e
x
p
lo
r
atio
n
,
en
co
m
p
ass
in
g
b
o
th
class
if
icatio
n
an
d
clu
s
ter
in
g
,
r
ev
ea
led
p
er
v
asiv
e
am
b
ig
u
ity
in
d
eter
m
in
in
g
th
e
m
o
s
t
s
u
it
ab
le
alg
o
r
ith
m
s
to
ac
h
iev
e
u
s
er
n
ee
d
s
b
ased
o
n
v
ar
io
u
s
m
etr
ics.
4
.
2
.
1
.
Cla
s
s
if
ica
t
io
n
Desp
ite
em
p
lo
y
in
g
m
u
ltip
le
m
etr
ics
f
o
r
s
elec
tio
n
,
th
e
o
u
tco
m
e
r
em
ain
ed
in
co
n
clu
s
iv
e,
m
ar
k
ed
b
y
s
ig
n
if
ican
t
er
r
o
r
s
en
co
u
n
ter
e
d
d
u
r
in
g
th
e
u
tili
za
tio
n
o
f
W
ek
a
f
o
r
ex
p
er
im
en
tatio
n
.
T
h
is
s
u
g
g
ests
th
at
th
e
class
if
icatio
n
m
o
d
el
f
ails
to
m
ee
t
th
e
a
m
b
itio
n
s
o
f
u
s
er
s
an
d
r
esear
c
h
er
s
to
s
atis
f
y
v
ar
io
u
s
m
etr
ics
d
u
e
to
s
ig
n
if
ican
t
er
r
o
r
s
d
u
r
in
g
its
cr
ea
tio
n
,
p
r
im
ar
ily
s
tem
m
in
g
f
r
o
m
in
ac
cu
r
ate
co
n
ten
t.
Su
b
s
e
q
u
en
tly
,
th
is
d
ir
ec
tly
af
f
ec
ts
u
s
er
s
in
s
elec
tin
g
o
n
e
o
r
a
g
r
o
u
p
o
f
s
u
itab
le
alg
o
r
ith
m
s
.
No
tab
ly
,
th
e
g
r
ap
h
ical
r
ep
r
esen
tatio
n
Fig
u
r
e
1
o
f
th
e
d
ec
is
io
n
tr
ee
u
n
d
e
r
s
co
r
es
th
e
n
ec
ess
ity
to
i
n
itiate
th
e
p
r
o
ce
s
s
o
f
s
elec
tin
g
m
e
tr
ics
p
r
ed
o
m
in
an
tly
f
o
cu
s
in
g
o
n
th
e
“
Per
f
o
r
m
a
n
ce
”
m
etr
ic,
w
h
ich
m
i
g
h
t
n
o
t
alw
ay
s
alig
n
with
th
e
co
m
p
r
eh
en
s
iv
e
co
n
s
id
er
atio
n
s
r
eq
u
ir
ed
f
o
r
clo
u
d
u
s
er
s
.
Fu
r
th
er
m
o
r
e
,
th
e
f
lo
w
o
f
s
elec
ted
m
etr
ics
p
r
esen
ts
s
ig
n
if
ican
t
am
b
ig
u
ity
in
m
an
y
m
etr
ics d
u
r
in
g
t
h
e
p
r
o
ce
s
s
o
f
s
elec
tin
g
th
e
d
esire
d
lo
a
d
b
alan
cin
g
alg
o
r
ith
m
.
4
.
2
.
2
.
Clus
t
er
ing
Similar
ly
,
in
th
e
clu
s
ter
in
g
to
o
l,
th
e
cr
ea
ted
clu
s
ter
in
g
m
o
d
el
y
ield
s
p
o
o
r
r
esu
lts
,
f
ailin
g
t
o
m
ee
t
th
e
u
s
er
’
s
o
b
jectiv
es.
Un
f
o
r
t
u
n
ate
ly
,
d
u
e
to
th
e
ze
r
o
e
d
v
alu
e
er
r
o
r
o
f
th
e
m
o
d
el,
th
e
o
u
tc
o
m
e
s
b
ec
o
m
e
illeg
ib
le
d
esp
ite
cr
ea
tin
g
m
an
y
ch
o
ic
es
o
f
s
u
itab
le
alg
o
r
ith
m
s
.
T
h
is
co
n
f
u
s
io
n
c
o
n
s
is
ten
tly
tr
o
u
b
les
u
s
er
s
wh
e
n
attem
p
tin
g
to
s
elec
t
th
e
d
esire
d
alg
o
r
ith
m
s
with
a
p
p
r
o
p
r
iate
m
etr
ics.
I
n
o
th
e
r
wo
r
d
s
,
in
th
e
r
ea
lm
o
f
clu
s
ter
in
g
,
th
e
m
o
d
el
f
ailed
to
alig
n
with
o
u
r
o
b
jectiv
es
o
f
p
in
p
o
i
n
tin
g
a
s
in
g
u
lar
alg
o
r
ith
m
s
u
itab
le
f
o
r
clu
s
ter
in
g
.
T
h
is
d
is
cr
ep
an
c
y
f
u
r
th
er
c
o
m
p
licates th
e
s
elec
tio
n
p
r
o
ce
s
s
.
M
o
r
e
o
v
e
r
,
t
h
e
v
a
l
i
d
a
t
i
o
n
p
r
o
c
e
s
s
h
i
g
h
l
i
g
h
t
s
t
h
e
i
n
h
e
r
e
n
t
c
h
a
l
l
e
n
g
e
s
i
n
s
e
l
e
ct
i
n
g
l
o
a
d
b
a
l
a
n
c
i
n
g
a
l
g
o
r
i
t
h
m
s
,
es
p
e
c
ia
l
l
y
w
h
e
n
c
o
n
f
r
o
n
t
e
d
w
i
t
h
a
n
e
x
p
a
n
d
i
n
g
a
r
r
a
y
o
f
c
r
i
t
e
r
i
a
.
T
h
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s
r
ea
f
f
i
r
m
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h
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f
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p
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b
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m
o
d
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m
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l
y
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r
a
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ew
u
p
d
a
t
e
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o
r
a
d
d
i
t
i
o
n
a
l
al
g
o
r
i
t
h
m
s
,
e
n
s
u
r
i
n
g
its
c
o
n
t
i
n
u
a
l
r
e
l
e
v
a
n
ce
a
n
d
u
s
a
b
i
l
i
t
y
.
T
h
i
s
p
r
o
p
o
s
e
d
m
o
d
e
l
p
r
o
m
i
s
e
s
t
o
b
e
a
r
o
b
u
s
t
f
r
a
m
e
w
o
r
k
c
a
p
a
b
l
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o
f
a
d
d
r
e
s
s
i
n
g
t
h
e
c
o
m
p
l
e
x
i
ti
e
s
in
h
e
r
e
n
t
i
n
a
l
g
o
r
i
t
h
m
s
e
l
e
c
t
i
o
n
w
i
t
h
i
n
d
a
t
a
m
i
n
i
n
g
p
r
a
c
t
i
ce
s
.
T
h
e
r
e
f
o
r
e
,
t
h
i
s
e
n
d
e
a
v
o
r
u
n
v
e
i
l
e
d
t
h
e
p
e
r
s
is
t
e
n
t
c
h
a
ll
e
n
g
e
o
f
s
e
le
c
t
i
n
g
a
n
o
p
t
i
m
a
l
i
n
d
i
v
i
d
u
a
l
o
r
g
r
o
u
p
o
f
a
l
g
o
r
i
t
h
m
s
,
a
c
c
e
n
t
u
a
t
i
n
g
t
h
e
c
o
m
p
l
e
x
i
ti
e
s
as
s
o
ci
a
t
e
d
wi
t
h
t
h
is
t
as
k
.
C
o
n
s
eq
u
e
n
t
l
y
,
a
f
o
u
n
d
a
t
i
o
n
a
l
m
o
d
e
l
f
o
r
d
a
t
a
m
i
n
i
n
g
w
as
d
e
v
e
l
o
p
e
d
,
w
i
t
h
t
h
e
p
o
t
e
n
ti
a
l
to
e
v
o
l
v
e
a
n
d
a
c
c
o
m
m
o
d
a
t
e
f
u
t
u
r
e
u
p
d
a
t
e
s
o
r
t
h
e
i
n
c
o
r
p
o
r
a
t
i
o
n
o
f
n
e
w
a
l
g
o
r
i
t
h
m
s
.
5.
L
I
M
I
T
AT
I
O
NS
AN
D
E
X
T
E
NSI
O
N
Ou
r
m
eth
o
d
co
n
s
id
er
s
a
n
o
v
e
l
m
eth
o
d
f
o
r
lo
ad
b
alan
ci
n
g
a
lg
o
r
ith
m
ev
alu
atio
n
b
y
em
p
lo
y
in
g
d
ata
m
in
in
g
to
o
ls
,
wh
ich
is
r
ar
e
in
cu
r
r
en
t
liter
atu
r
e.
W
e
i
g
n
o
r
e
alter
n
ativ
e
s
tatis
tical
tech
n
iq
u
es
in
o
u
r
ex
p
er
im
en
ts
an
d
lim
it
o
u
r
s
elv
es
to
o
n
ly
two
m
eth
o
d
s
:
class
if
icatio
n
an
d
clu
s
ter
in
g
.
Similar
ly
,
we
o
n
l
y
p
r
o
v
id
e
th
e
m
o
s
t
s
ig
n
if
ican
t
l
o
ad
b
alan
cin
g
m
ea
s
u
r
es
r
at
h
er
th
an
all
o
f
th
em
b
ec
au
s
e
o
f
th
eir
co
m
p
lex
ity
an
d
v
ar
iety
.
Fu
r
th
er
m
o
r
e,
to
s
atis
f
y
th
e
u
s
er
s
th
e
p
a
r
am
eter
s
an
d
m
etr
ics
o
f
th
is
m
o
d
el
ca
n
b
e
d
ev
elo
p
ed
in
th
e
f
u
tu
r
e
d
ep
en
d
in
g
o
n
u
s
er
s
o
f
c
lo
u
d
co
m
p
u
tin
g
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
C
h
a
llen
g
es o
f lo
a
d
b
a
l
a
n
cin
g
a
lg
o
r
ith
ms in
clo
u
d
c
o
mp
u
tin
g
u
tili
z
in
g
…
(
A
n
o
u
a
r
B
en
H
a
lima
)
3455
6.
CO
NCLU
SI
O
N
E
f
f
icien
t
lo
ad
b
alan
cin
g
is
ess
en
tial
in
clo
u
d
co
m
p
u
tin
g
en
v
ir
o
n
m
en
ts
,
wh
e
r
e
d
iv
e
r
s
e
wo
r
k
lo
ad
s
an
d
ch
an
g
in
g
d
em
a
n
d
s
r
eq
u
ir
e
o
p
t
im
al
r
eso
u
r
ce
u
tili
za
tio
n
.
E
f
f
e
ctiv
e
r
eso
u
r
ce
m
an
ag
em
e
n
t
an
d
o
p
tim
izatio
n
ar
e
cr
u
cial
f
o
r
e
n
h
an
cin
g
p
er
f
o
r
m
an
ce
,
s
ca
lab
ilit
y
,
an
d
co
s
t
-
ef
f
e
ctiv
en
ess
in
th
is
r
ap
id
ly
ev
o
lv
i
n
g
f
ield
.
T
h
e
p
ap
e
r
co
n
d
u
cte
d
a
co
m
p
ar
ativ
e
an
al
y
s
is
o
f
lo
ad
b
alan
cin
g
alg
o
r
it
h
m
s
in
clo
u
d
c
o
m
p
u
ti
n
g
,
em
p
lo
y
in
g
d
ata
m
in
in
g
tech
n
iq
u
es.
I
t
h
ig
h
lig
h
ted
th
e
ch
allen
g
es
in
al
g
o
r
ith
m
s
elec
tio
n
d
u
e
to
v
ar
ie
d
cr
iter
ia,
em
p
h
asizin
g
th
e
n
ee
d
f
o
r
f
u
r
th
e
r
r
esear
ch
a
n
d
p
r
ac
ti
ca
l
ap
p
licatio
n
s
.
W
e
h
av
e
s
h
o
wn
th
at
ac
h
iev
in
g
co
m
p
lete
s
a
tis
f
ac
tio
n
with
lo
ad
b
alan
cin
g
al
g
o
r
ith
m
s
i
n
clo
u
d
co
m
p
u
tin
g
wh
ile
co
n
s
id
er
in
g
all
m
etr
ics
is
d
if
f
icu
lt.
I
n
o
u
r
f
u
tu
r
e
wo
r
k
,
we
p
r
o
p
o
s
e
a
n
ew
s
tr
ateg
y
to
ac
h
iev
e
well
lo
a
d
b
alan
cin
g
,
b
y
t
ak
in
g
ad
v
a
n
tag
e
o
f
th
e
b
en
ef
it
s
an
d
d
r
awb
ac
k
s
o
f
s
u
ch
a
tech
n
iq
u
e.
I
n
ad
d
itio
n
,
we
will e
x
p
lo
it th
e
cr
ea
ted
m
o
d
el
to
h
elp
cl
o
u
d
u
s
er
s
s
elec
t s
u
itab
le
m
etr
ics.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
Au
th
o
r
s
s
tate
n
o
f
u
n
d
in
g
in
v
o
lv
ed
.
AUTHO
R
CO
NT
RI
B
UT
I
O
NS ST
A
T
E
M
E
N
T
T
h
is
jo
u
r
n
al
u
s
es
th
e
C
o
n
tr
ib
u
to
r
R
o
les
T
ax
o
n
o
m
y
(
C
R
ed
iT)
to
r
ec
o
g
n
ize
in
d
iv
id
u
al
au
th
o
r
co
n
tr
ib
u
tio
n
s
,
r
ed
u
ce
au
th
o
r
s
h
ip
d
is
p
u
tes,
an
d
f
ac
ilit
ate
co
llab
o
r
atio
n
.
Na
m
e
o
f
Aut
ho
r
C
M
So
Va
Fo
I
R
D
O
E
Vi
Su
P
Fu
An
o
u
ar
B
en
Halim
a
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
Haf
s
s
a
B
en
ab
o
u
d
✓
✓
✓
✓
✓
C
:
C
o
n
c
e
p
t
u
a
l
i
z
a
t
i
o
n
M
:
M
e
t
h
o
d
o
l
o
g
y
So
:
So
f
t
w
a
r
e
Va
:
Va
l
i
d
a
t
i
o
n
Fo
:
Fo
r
mal
a
n
a
l
y
s
i
s
I
:
I
n
v
e
s
t
i
g
a
t
i
o
n
R
:
R
e
so
u
r
c
e
s
D
:
D
a
t
a
C
u
r
a
t
i
o
n
O
:
W
r
i
t
i
n
g
-
O
r
i
g
i
n
a
l
D
r
a
f
t
E
:
W
r
i
t
i
n
g
-
R
e
v
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e
w
&
E
d
i
t
i
n
g
Vi
:
Vi
su
a
l
i
z
a
t
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o
n
Su
:
Su
p
e
r
v
i
s
i
o
n
P
:
P
r
o
j
e
c
t
a
d
mi
n
i
st
r
a
t
i
o
n
Fu
:
Fu
n
d
i
n
g
a
c
q
u
i
si
t
i
o
n
CO
NF
L
I
C
T
O
F
I
N
T
E
R
E
S
T
ST
A
T
E
M
E
NT
(
m
a
nd
a
to
r
y)
(
1
0
P
T
)
Au
th
o
r
s
s
tate
n
o
co
n
f
lict o
f
in
t
er
est.
DATA AV
AI
L
AB
I
L
I
T
Y
T
h
e
d
ata
th
at
s
u
p
p
o
r
t
th
e
f
in
d
in
g
s
o
f
th
is
s
tu
d
y
ar
e
av
aila
b
le
o
n
r
eq
u
est
f
r
o
m
th
e
co
r
r
esp
o
n
d
in
g
au
th
o
r
,
An
o
u
ar
B
en
Halim
a.
T
h
e
d
ata,
wh
ich
c
o
n
tain
in
f
o
r
m
atio
n
th
at
c
o
u
ld
c
o
m
p
r
o
m
is
e
th
e
p
r
iv
ac
y
o
f
r
esear
ch
p
ar
ticip
an
ts
,
ar
e
n
o
t
p
u
b
licly
av
ailab
le
d
u
e
to
ce
r
tain
r
estrictio
n
s
.
RE
F
E
R
E
NC
E
S
[
1
]
S
.
K
.
M
i
s
h
r
a
,
B
.
S
a
h
o
o
,
a
n
d
P
.
P
.
P
a
r
i
d
a
,
“
L
o
a
d
b
a
l
a
n
c
i
n
g
i
n
c
l
o
u
d
c
o
m
p
u
t
i
n
g
:
a
b
i
g
p
i
c
t
u
r
e
,
”
J
o
u
r
n
a
l
o
f
K
i
n
g
S
a
u
d
U
n
i
v
e
rs
i
t
y
-
C
o
m
p
u
t
e
r
a
n
d
I
n
f
o
rm
a
t
i
o
n
S
c
i
e
n
c
e
s
,
v
o
l
.
3
2
,
n
o
.
2
,
p
p
.
1
4
9
–
1
5
8
,
F
e
b
.
2
0
2
0
,
d
o
i
:
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0
.
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0
1
6
/
j
.
j
k
s
u
c
i
.
2
0
1
8
.
0
1
.
0
0
3
.
[
2
]
N
.
G
.
El
n
a
g
a
r
,
G
.
F
.
El
k
a
b
b
a
n
y
,
A
.
A
.
A
l
-
A
w
a
mr
y
,
a
n
d
M
.
B
.
A
b
d
e
l
h
a
l
i
m,
“
S
i
mu
l
a
t
i
o
n
a
n
d
p
e
r
f
o
r
ma
n
c
e
a
ssessm
e
n
t
o
f
a
mo
d
i
f
i
e
d
t
h
r
o
t
t
l
e
d
l
o
a
d
b
a
l
a
n
c
i
n
g
a
l
g
o
r
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t
h
m
i
n
c
l
o
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d
c
o
m
p
u
t
i
n
g
e
n
v
i
r
o
n
m
e
n
t
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
El
e
c
t
ri
c
a
l
a
n
d
C
o
m
p
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t
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En
g
i
n
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ri
n
g
,
v
o
l
.
1
2
,
n
o
.
2
,
p
p
.
2
0
8
7
–
2
0
9
6
,
A
p
r
.
2
0
2
2
,
d
o
i
:
1
0
.
1
1
5
9
1
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j
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c
e
.
v
1
2
i
2
.
p
p
2
0
8
7
-
2
0
9
6
.
[
3
]
M
.
J
u
n
a
i
d
e
t
a
l
.
,
“
M
o
d
e
l
i
n
g
a
n
o
p
t
i
m
i
z
e
d
a
p
p
r
o
a
c
h
f
o
r
l
o
a
d
b
a
l
a
n
c
i
n
g
i
n
c
l
o
u
d
,
”
I
EEE
A
c
c
e
ss
,
v
o
l
.
8
,
p
p
.
1
7
3
2
0
8
–
1
7
3
2
2
6
,
2
0
2
0
,
d
o
i
:
1
0
.
1
1
0
9
/
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