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wh
e
n
f
o
g
r
eso
u
r
ce
s
ar
e
s
ca
tter
ed
,
r
eso
u
r
ce
-
co
n
s
tr
ain
ed
,
an
d
v
ar
io
u
s
,
ef
f
ec
ti
v
e
task
s
ch
e
d
u
lin
g
b
ec
o
m
es
ess
en
tial
f
o
r
e
n
h
an
ci
n
g
p
e
r
f
o
r
m
an
ce
[
3
]
.
I
t
is
th
e
clo
u
d
d
ata
ce
n
ter
th
at
is
r
esp
o
n
s
ib
le
f
o
r
d
if
f
icu
lt
d
u
ties
b
ec
au
s
e
o
f
its
en
o
r
m
o
u
s
p
r
o
ce
s
s
in
g
an
d
s
to
r
ag
e
ca
p
a
city
,
wh
er
ea
s
ac
tiv
ities
th
at
n
ee
d
r
ap
id
r
esp
o
n
s
e
esp
ec
ially
th
o
s
e
r
eq
u
ir
in
g
cr
u
c
ial
d
elay
s
ar
e
d
eliv
er
e
d
to
th
e
f
o
g
n
o
d
e
d
u
e
to
its
p
r
o
x
im
ity
[
4
]
.
Ov
er
al
l,
f
o
g
co
m
p
u
t
in
g
r
ef
er
s
to
a
d
e
ce
n
tr
a
l
ize
d
co
n
f
ig
u
r
at
i
o
n
th
at
co
m
b
in
e
s
a
s
y
s
t
em
o
f
m
at
er
i
al
s
s
t
r
ea
m
s
th
a
t
i
s
h
e
av
ily
au
to
m
at
ed
w
ith
cl
ien
t
s
an
d
c
lo
u
d
d
ata
ce
n
ter
s
s
h
ar
in
g
co
m
p
u
tin
g
r
e
s
o
u
r
c
e
s
to
im
p
r
o
v
e
th
eir
co
m
p
u
ta
ti
o
n
a
l
an
d
d
a
ta
p
r
o
ce
s
s
i
n
g
ca
p
ab
il
it
i
es
.
T
a
s
k
-
a
s
s
o
ci
at
ed
d
a
ta
an
a
l
y
ti
cs
ta
ck
l
e
s
ev
er
a
l
co
n
s
tr
ain
t
s
,
s
u
ch
as
in
s
u
f
f
i
ci
e
n
t b
an
d
w
id
th
an
d
la
ten
cy
,
an
d
ar
e
p
r
esu
m
ed
to
b
e
a
cc
o
m
p
li
s
h
ed
b
y
co
m
p
le
ti
n
g
d
ata
co
l
le
ct
io
n
ac
t
iv
i
ti
e
s
at
th
e
n
et
wo
r
k
e
d
g
e
[
5
]
.
Mu
lt
ip
l
e
m
e
th
o
d
s
h
av
e
b
ee
n
u
s
ed
f
o
r
o
p
ti
m
i
zin
g
b
an
d
w
id
th
in
ta
s
k
s
ch
ed
u
l
in
g
to
r
e
co
n
c
il
e
co
n
f
l
ic
tin
g
d
em
an
d
s
,
in
c
lu
d
in
g
q
u
an
t
ity
o
f
d
at
a,
p
r
o
ce
s
s
in
g
s
p
e
cif
ic
at
io
n
s
,
s
e
cu
r
i
ty
r
eq
u
ir
em
en
t
s
,
an
d
n
et
wo
r
k
r
eso
u
r
ce
ac
ce
s
s
ib
i
li
ty
.
E
f
f
ec
t
iv
e
l
y
co
m
b
in
in
g
t
ask
s
ch
ed
u
l
in
g
an
d
b
an
d
w
id
th
o
p
tim
iz
at
io
n
i
s
s
t
il
l
a
ch
al
len
g
i
n
g
p
r
o
b
lem
d
es
p
i
te
r
e
ce
n
t
ac
h
iev
em
en
t
s
[
6
]
.
An
ex
am
p
le
o
f
h
ig
h
ly
d
i
s
p
e
r
s
e
d
p
r
o
c
e
s
s
in
g
i
s
a
clo
u
d
-
f
o
g
f
r
am
ew
o
r
k
,
wh
ich
co
n
s
i
s
t
s
o
f
h
et
er
o
g
e
n
eo
u
s
p
r
o
v
id
e
r
s
w
ith
a
s
en
s
e
o
f
a
r
an
g
e
o
f
co
m
p
u
ta
tio
n
al
c
ap
ab
il
it
ie
s
.
Fo
r
th
i
s
r
e
a
s
o
n
,
clo
u
d
-
f
o
g
s
y
s
t
em
s
f
r
eq
u
en
t
ly
u
ti
li
ze
v
ir
tu
a
l
iza
ti
o
n
ex
p
e
r
t
i
s
e
to
p
r
o
v
id
e
clo
u
d
an
d
f
o
g
n
o
d
e
r
eso
u
r
c
es
in
th
e
f
o
r
m
o
f
v
ir
tu
a
l
m
ac
h
in
e
s
(
V
Ms
)
.
I
t
e
l
im
i
n
a
te
s
s
e
r
v
er
h
et
er
o
g
en
e
ity
,
co
n
s
o
l
id
a
te
s
s
e
r
v
er
s
,
an
d
in
c
r
ea
s
e
s
r
eso
u
r
ce
u
t
il
iz
at
io
n
r
at
es
[
7
]
.
Ho
wev
er
,
th
e
d
ep
lo
y
m
en
t
o
f
n
ew
v
ir
t
u
a
li
za
t
io
n
te
ch
n
o
lo
g
ie
s
in
f
o
g
co
m
p
u
t
in
g
t
ask
s
ch
ed
u
lin
g
an
d
r
es
o
u
r
c
e
a
l
lo
ca
t
io
n
i
s
a
f
f
e
ct
e
d
b
y
lo
w
l
at
en
cy
s
er
v
ic
es
an
d
s
ca
r
ce
r
e
s
o
u
r
ce
s
.
I
n
clo
u
d
co
m
p
u
t
in
g
,
d
e
ta
i
led
co
n
s
id
e
r
a
tio
n
h
a
s
b
ee
n
g
iv
en
to
s
ch
ed
u
li
n
g
an
d
lo
ad
b
al
a
n
cin
g
.
an
a
ly
z
ed
[
8
]
.
Al
lo
ca
t
i
o
n
an
d
s
ch
ed
u
l
in
g
h
av
e
a
d
i
r
ec
t
im
p
ac
t
o
n
th
e
s
y
s
t
em
's
q
u
al
ity
o
f
s
er
v
ic
e,
wh
i
ch
in
c
lu
d
es
en
er
g
y
u
s
ag
e
an
d
s
er
v
ic
e
t
im
e.
I
n
ef
f
ic
ien
c
ie
s
c
an
lea
d
p
e
r
f
o
r
m
an
c
e
to
d
r
o
p
.
T
h
u
s
,
s
el
ec
t
in
g
an
ef
f
ec
tiv
e
f
o
g
co
m
p
u
tin
g
n
o
d
e
wh
i
le
p
r
e
s
er
v
in
g
Qo
S
i
s
c
r
u
c
ia
l
[
9
]
.
C
o
n
v
er
s
ely
,
v
ar
ia
ti
o
n
in
en
er
g
y
co
n
s
u
m
p
tio
n
r
e
s
u
lt
s
f
r
o
m
d
i
s
ti
n
c
t
t
as
k
s
ch
ed
u
l
in
g
s
tr
a
te
g
i
es
o
n
in
t
el
lig
en
t
p
r
o
d
u
c
t
io
n
lin
e
s
wh
er
e
n
u
m
er
o
u
s
f
o
g
n
o
d
e
s
ar
e
p
o
wer
ed
b
y
b
at
te
r
i
es
;
th
i
s
in
v
ar
i
ab
ly
g
i
v
e
s
r
i
s
e
to
a
m
u
lt
itu
d
e
o
f
co
m
p
l
ic
at
io
n
s
.
Fo
r
in
s
tan
ce
,
r
e
s
ea
r
c
h
d
is
co
v
er
ed
th
at
f
r
eq
u
en
t
d
ata
in
ter
ch
an
g
e,
tr
an
s
m
i
s
s
io
n
,
an
d
p
r
o
ce
s
s
in
g
m
ig
h
t
r
es
u
lt
in
a
co
n
s
id
er
ab
le
r
ed
u
c
t
i
o
n
in
b
a
tt
er
y
lif
e
th
r
o
u
g
h
r
ap
id
c
o
n
s
u
m
p
t
io
n
,
h
en
ce
cr
ea
t
in
g
a
s
ec
u
r
i
ty
r
i
s
k
f
o
r
d
at
a
le
ak
ag
e
wh
en
a
d
ev
ic
e
i
s
n
o
t
ch
ar
g
ed
i
n
en
o
u
g
h
ti
m
e
[
1
0
]
.
T
h
e
o
p
t
im
iza
t
io
n
p
r
o
b
le
m
o
f
n
o
n
d
e
ter
m
in
i
s
ti
c
p
o
ly
n
o
m
ia
l
(
NP
)
i
s
th
e
ta
s
k
s
ch
ed
u
l
in
g
m
eth
o
d
(
n
o
n
d
e
ter
m
in
i
s
t
ic
p
o
l
y
n
o
m
ia
l
t
im
e)
-
h
ar
d
.
T
o
p
r
o
d
u
ce
ef
f
ec
ti
v
e
s
ch
ed
u
l
e
s
in
an
ac
ce
p
t
ab
l
e
p
er
io
d
,
m
an
y
m
et
ah
eu
r
i
s
ti
cs
h
av
e
b
ee
n
u
s
ed
,
in
c
lu
d
in
g
th
e
m
o
t
h
-
f
la
m
e
o
p
t
im
iza
t
io
n
(
M
FO)
alg
o
r
i
th
m
,
g
en
et
ic
alg
o
r
it
h
m
(
G
A)
,
an
d
b
e
e
s
’
l
if
e
a
lg
o
r
ith
m
(
B
L
A)
[
7
]
.
NP
-
c
o
m
p
l
et
en
e
s
s
ch
a
r
ac
te
r
ize
s
th
e
r
e
s
o
u
r
ce
m
an
ag
em
en
t
a
lg
o
r
it
h
m
,
an
d
i
ts
co
m
p
lex
it
y
v
ar
ie
s
wi
th
t
h
e
tim
e
co
m
p
l
ex
i
ty
.
I
n
f
o
g
co
m
p
u
t
in
g
,
th
er
e
ar
e
th
r
e
e
way
s
to
m
an
ag
e
r
es
o
u
r
ce
s
as
e
f
f
i
ci
en
t
ly
a
s
p
o
s
s
ib
l
e
:
h
eu
r
i
s
ti
c,
m
e
ta
-
h
eu
r
i
s
t
ic,
an
d
h
y
b
r
id
m
et
h
o
d
s
.
L
ar
g
e
s
ea
r
ch
s
p
a
ce
s
m
a
y
b
e
h
an
d
l
ed
b
y
m
et
a
-
h
eu
r
i
s
ti
c
t
ec
h
n
iq
u
e
s
,
wh
ich
c
an
a
l
s
o
f
i
n
d
b
et
ter
r
e
s
o
u
r
ce
m
an
ag
em
en
t
s
o
lu
t
io
n
s
in
an
a
cc
ep
tab
le
l
en
g
th
o
f
ti
m
e
[
1
1
]
.
Op
t
im
iza
t
io
n
,
a
f
u
n
d
a
m
en
ta
l
co
n
ce
p
t,
p
er
m
ea
te
s
v
ar
io
u
s
a
s
p
ec
t
s
o
f
ev
er
y
d
ay
l
if
e
in
wh
i
c
h
th
e
ter
m
o
p
ti
m
i
za
tio
n
in
co
m
p
u
t
in
g
r
ef
er
s
t
o
m
ax
i
m
i
zin
g
th
e
n
et
wo
r
k
o
r
p
er
f
o
r
m
an
ce
o
f
ap
p
l
ic
at
i
o
n
s
wh
il
e
u
s
in
g
a
s
f
ew
r
e
s
o
u
r
c
e
s
a
s
p
o
s
s
ib
l
e.
W
h
en
i
t
co
m
e
s
to
o
p
t
im
iz
at
io
n
,
p
o
p
u
la
ti
o
n
-
l
ev
e
l
tac
ti
c
s
m
o
d
if
y
a
s
et
o
f
r
e
s
u
l
ts
a
s
th
e
p
r
o
ce
d
u
r
e
p
r
o
g
r
e
s
s
es
,
b
u
t
m
et
a
-
h
eu
r
i
s
ti
c
ap
p
r
o
ac
h
e
s
a
r
e
m
o
r
e
ef
f
ec
t
iv
e
a
t
s
o
l
v
in
g
r
e
a
l
-
wo
r
ld
i
s
s
u
e
s
in
a
r
an
g
e
o
f
f
ie
ld
s
,
s
u
ch
a
s
c
o
m
p
u
ter
s
ci
en
c
e
an
d
en
g
in
e
er
in
g
[
1
2
]
.
M
et
a
-
h
eu
r
i
s
ti
c
s
ar
e
c
r
u
c
ia
l
to
o
p
t
im
iz
at
io
n
.
T
h
e
s
e
a
lg
o
r
ith
m
s
ar
e
u
s
u
al
ly
in
s
p
ir
ed
b
y
n
a
tu
r
a
l
f
o
r
ag
in
g
a
n
d
co
l
lec
t
iv
e
i
n
te
l
lig
en
c
e.
T
h
e
“Af
r
i
ca
n
v
u
ltu
r
e
o
p
ti
m
i
za
tio
n
a
lg
o
r
ith
m
”
(
AV
OA)
is
o
n
e
o
f
th
e
n
o
v
el
m
e
t
a
-
h
eu
r
i
s
t
ic
s
av
ai
lab
le
.
T
h
e
A
VO
A
i
s
s
ti
l
l
in
i
t
s
ea
r
ly
s
tag
e
s
o
f
d
ev
e
lo
p
m
en
t
s
i
n
ce
it
is
a
n
o
v
e
l
s
wa
r
m
in
t
el
lig
en
c
e
te
ch
n
i
q
u
e.
T
h
e
o
p
t
i
m
iz
at
io
n
p
r
o
c
e
s
s
i
s
u
n
p
r
ed
i
ct
ab
le,
wh
i
ch
m
ak
e
s
it
ch
al
len
g
in
g
f
o
r
th
e
AV
OA
to
b
al
an
c
e
th
e
p
h
a
s
e
s
o
f
d
ev
e
lo
p
m
e
n
t
an
d
ex
p
lo
r
at
io
n
.
A
s
a
r
e
s
u
lt,
i
s
s
u
e
s
lik
e
a
lo
c
al
o
p
t
im
u
m
s
o
lu
tio
n
an
d
a
l
im
i
ted
n
u
m
b
e
r
o
f
p
o
p
u
la
tio
n
s
ta
te
s
m
a
y
ar
i
s
e
f
o
r
th
e
m
eth
o
d
[
1
3
]
.
On
e
“
i
n
f
r
a
s
tr
u
c
tu
r
e
a
s
a
s
e
r
v
i
ce
”
(
I
a
aS)
cl
o
u
d
s
ch
e
d
u
l
in
g
p
r
e
s
en
ta
ti
o
n
i
s
g
iv
en
.
Fo
l
lo
win
g
t
h
at
a
tt
en
tio
n
i
s
d
ir
ec
ted
to
war
d
s
m
e
th
o
d
s
th
a
t
ar
e
h
eu
r
i
s
ti
c,
m
e
ta
-
,
an
d
h
y
p
er
-
h
eu
r
i
s
ti
c.
Nu
m
e
r
o
u
s
p
r
o
b
l
em
s
th
a
t
ar
e
co
m
p
u
t
at
io
n
a
ll
y
d
if
f
icu
lt
m
ay
b
e
ef
f
ec
t
iv
ely
s
o
lv
ed
b
y
u
s
in
g
h
eu
r
i
s
t
ic
an
d
m
et
a
-
h
eu
r
i
s
ti
c
te
ch
n
iq
u
e
s
.
T
h
e
m
ain
o
b
je
ct
iv
e
o
f
th
e
s
u
g
g
e
s
t
ed
t
ec
h
n
iq
u
e
is
to
s
h
o
w
th
e
b
en
ef
it
s
o
f
h
eu
r
i
s
t
ic
m
eth
o
d
s
,
es
p
ec
ia
l
ly
f
o
r
p
r
o
b
lem
s
in
v
o
lv
in
g
t
a
s
k
s
ch
ed
u
lin
g
an
d
r
e
s
o
u
r
c
e
al
lo
c
at
io
n
in
th
e
co
n
tex
t
o
f
co
m
p
u
t
er
s
[
1
4
]
.
I
n
th
is
s
tu
d
y
,
a
s
y
s
tem
atic
r
ev
iew
is
co
n
d
u
cted
b
y
f
o
c
u
s
in
g
o
n
th
e
h
eu
r
is
tic
an
d
m
et
a
-
h
eu
r
is
tic
m
eth
o
d
s
em
p
lo
y
ed
in
d
y
n
am
i
c
task
s
ch
ed
u
lin
g
with
in
a
f
o
g
co
m
p
u
tin
g
en
v
ir
o
n
m
en
t.
T
h
e
s
tu
d
y
en
co
m
p
ass
es
an
in
-
d
e
p
th
a
n
aly
s
is
o
f
task
s
ch
ed
u
lin
g
ap
p
r
o
ac
h
es,
al
g
o
r
it
h
m
s
,
an
d
p
iv
o
tal
f
ac
to
r
s
,
d
r
a
win
g
f
r
o
m
a
r
an
g
e
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
.
6
,
Decem
b
e
r
20
25
:
5
9
8
6
-
6
0
0
0
5988
o
f
p
u
b
lis
h
ed
s
tu
d
ies
s
p
an
n
i
n
g
th
e
p
e
r
io
d
f
r
o
m
2
0
2
1
to
2
0
2
3
.
T
h
e
p
r
im
ar
y
o
b
jectiv
es
o
f
th
is
r
esear
ch
a
r
e
o
u
tlin
ed
as,
a.
Pro
v
id
in
g
a
n
ex
ten
s
iv
e
o
v
er
v
iew
o
f
task
s
ch
ed
u
lin
g
m
eth
o
d
s
with
in
f
o
g
co
m
p
u
tin
g
en
v
ir
o
n
m
en
ts
.
b.
R
ev
iew
th
e
tech
n
ical
asp
ec
ts
,
s
u
ch
as
ar
ch
itectu
r
e,
b
e
n
ef
it
s
,
an
d
d
r
awb
ac
k
s
,
as
well
as
th
e
o
p
tim
ized
m
eth
o
d
s
an
d
alg
o
r
ith
m
s
u
tili
ze
d
in
p
r
i
o
r
s
tu
d
ies co
n
ce
r
n
in
g
d
y
n
am
ic
task
s
ch
ed
u
lin
g
in
f
o
g
co
m
p
u
tin
g
.
c.
I
n
th
e
co
n
tex
t
o
f
th
e
f
o
g
co
m
p
u
tin
g
p
ar
ad
ig
m
,
we
f
o
c
u
s
o
n
h
eu
r
is
tic
an
d
m
etah
eu
r
is
ti
c
m
eth
o
d
o
lo
g
ies,
with
an
ex
am
in
atio
n
o
f
t
h
eir
a
p
p
licab
ilit
y
an
d
u
s
ef
u
ln
ess
in
d
y
n
am
ic
task
s
ch
ed
u
lin
g
p
r
o
c
ed
u
r
es.
d.
T
h
e
g
o
al
is
to
i
d
en
tify
a
n
d
o
u
tlin
e
th
e
cu
r
r
en
t
d
if
f
icu
ltie
s
an
d
ch
allen
g
es
th
at
a
r
e
co
n
n
ec
ted
to
task
s
ch
ed
u
lin
g
ap
p
r
o
ac
h
es
in
f
o
g
co
m
p
u
tin
g
s
ettin
g
s
.
T
h
is
will
b
e
ac
co
m
p
lis
h
ed
b
y
id
en
tif
y
in
g
an
d
d
e
f
in
in
g
th
e
ex
is
tin
g
is
s
u
es.
T
h
is
p
ap
er
is
o
r
g
an
ized
as
f
o
llo
ws.
I
n
Sectio
n
2
,
s
p
ec
if
ics
o
f
th
e
task
s
ch
ed
u
lin
g
s
y
s
tem
u
s
ed
in
th
e
f
o
g
en
v
ir
o
n
m
e
n
t
is
d
is
cu
s
s
ed
.
T
ec
h
n
ical
class
if
icatio
n
o
n
ta
s
k
s
ch
ed
u
lin
g
in
a
f
o
g
c
o
m
p
u
t
in
g
e
n
v
ir
o
n
m
en
t
is
d
ef
in
ed
in
Sectio
n
3
.
Sectio
n
4
p
r
esen
ts
a
th
eo
r
etica
l
ex
a
m
in
atio
n
an
d
co
m
p
r
ess
io
n
.
T
h
e
co
n
clu
s
io
n
is
f
in
all
y
p
r
esen
ted
in
s
ec
tio
n
5
.
2.
T
ASK
SCH
E
DU
L
I
NG
AR
C
H
I
T
E
CT
U
RE
I
N
F
O
G
CO
M
P
UT
I
NG
Fo
g
co
m
p
u
tin
g
was
in
v
e
n
ted
as
a
d
ec
en
tr
alize
d
c
o
m
p
u
tin
g
p
ar
a
d
ig
m
to
a
d
d
r
ess
is
s
u
es
in
th
e
s
tan
d
ar
d
clo
u
d
c
o
m
p
u
tin
g
s
y
s
tem
s
s
u
ch
as
h
ig
h
laten
cy
,
b
an
d
wid
t
h
r
estrictio
n
s
,
an
d
r
ed
u
ce
d
en
er
g
y
ef
f
icien
cy
b
y
b
r
i
n
g
in
g
n
etwo
r
k
in
g
,
p
r
o
ce
s
s
in
g
,
an
d
s
to
r
ag
e
clo
s
er
to
th
e
in
ter
n
et
o
f
th
in
g
s
d
ev
ices.
No
r
m
ally
,
th
e
th
r
ee
la
y
er
s
o
f
th
e
f
o
g
co
m
p
u
tin
g
ar
ch
itectu
r
e
in
clu
d
e
th
e
I
o
T
/
ed
g
e
lay
e
r
,
th
e
f
o
g
la
y
er
,
a
n
d
th
e
clo
u
d
lay
er
[
1
5
]
.
T
h
e
in
ter
n
et
o
f
th
i
n
g
s
lay
er
is
co
n
s
titu
ted
b
y
th
e
s
en
s
o
r
s
,
ac
tu
ato
r
s
,
an
d
o
th
er
in
ter
n
et
o
f
th
in
g
s
en
d
p
o
i
n
ts
th
at
d
eliv
e
r
d
ata
i
n
r
ea
l
-
tim
e.
Pre
-
p
r
o
ce
s
s
in
g
a
n
d
lo
w
-
laten
cy
r
ep
lies
f
o
r
r
e
s
o
u
r
ce
-
co
n
s
tr
ain
e
d
d
ev
ices
ar
e
p
r
o
v
id
e
d
b
y
th
e
f
o
g
lay
er
.
T
h
e
in
ter
m
e
d
iar
y
la
y
er
h
o
u
s
es
Fo
g
n
o
d
es
s
u
ch
as
g
atew
ay
s
,
r
o
u
ter
s
,
an
d
e
d
g
e
s
er
v
er
s
,
t
h
u
s
r
eliev
i
n
g
th
e
clo
u
d
f
r
o
m
th
e
s
tr
ess
o
f
p
r
o
ce
s
s
in
g
an
d
s
to
r
i
n
g
th
e
d
ata
lo
ca
lly
f
o
r
tim
e
-
s
en
s
itiv
e
o
p
er
atio
n
s
.
Fo
g
n
o
d
es
ty
p
ically
e
m
p
lo
y
v
ir
tu
al
izatio
n
tech
n
o
lo
g
ies
lik
e
v
ir
tu
al
m
ac
h
in
es
o
r
co
n
tain
er
s
f
o
r
p
r
o
v
i
d
in
g
s
ca
lab
ilit
y
as we
ll a
s
ef
f
ec
tiv
e
co
n
s
u
m
p
tio
n
o
f
r
eso
u
r
ce
s
.
Hig
h
-
ca
p
ac
ity
d
ata
s
to
r
ag
e,
s
o
p
h
is
ticated
an
aly
tics
,
an
d
wo
r
ld
wid
e
co
o
r
d
in
atio
n
a
r
e
h
an
d
led
b
y
th
e
clo
u
d
lay
er
f
o
r
jo
b
s
th
at
d
o
not
n
ee
d
as
m
u
ch
d
elay
[
1
6
]
.
in
ter
n
et
o
f
th
in
g
s
d
ev
ices
in
itiates
th
e
f
o
g
co
m
p
u
tin
g
p
r
o
ce
s
s
b
y
f
o
r
war
d
in
g
d
ata
to
n
ea
r
b
y
f
o
g
n
o
d
es
f
o
r
o
n
-
s
ite
p
r
ep
r
o
ce
s
s
in
g
an
d
an
aly
s
is
.
W
o
r
k
th
at
r
e
q
u
ir
es
lar
g
e
p
r
o
c
ess
in
g
o
r
lo
n
g
-
ter
m
s
to
r
ag
e
is
s
h
if
ted
to
th
e
clo
u
d
lay
er
.
I
n
t
h
is
tier
ed
s
etu
p
,
th
e
h
ier
ar
ch
ical
co
n
f
ig
u
r
atio
n
b
r
in
g
s
in
th
e
b
e
n
ef
its
o
f
b
an
d
wid
th
s
av
in
g
,
laten
cy
r
ed
u
ctio
n
,
a
n
d
r
ea
l
-
tim
e
d
ec
is
io
n
-
m
ak
i
n
g
,
t
h
er
eb
y
b
r
in
g
in
g
a
b
o
u
t
Qo
S.
T
h
is
f
o
g
co
m
p
u
tin
g
also
s
av
es
e
n
er
g
y
as
it
av
o
id
s
u
n
n
ec
ess
ar
y
d
ata
t
r
an
s
f
er
s
to
t
h
e
clo
u
d
.
I
t
also
al
lo
ws
f
o
r
s
ca
lab
ilit
y
d
u
e
to
th
e
in
s
tallatio
n
o
f
m
o
r
e
n
o
d
es
in
th
e
f
o
g
as
d
esire
d
.
Du
e
to
th
ese
ch
ar
ac
ter
is
tics
,
s
u
c
h
f
ea
tu
r
es
ar
e
v
er
y
u
s
ef
u
l
f
o
r
laten
cy
-
cr
itical
ap
p
licatio
n
s
s
u
ch
as
in
d
u
s
tr
ial
a
u
to
m
atio
n
,
s
m
ar
t
cities,
an
d
h
ea
lth
ca
r
e
[
1
1
]
.
An
ex
p
licitly
clo
u
d
-
f
o
g
-
s
p
ec
if
ic
s
em
i
-
d
y
n
am
ic
r
ea
l
-
tim
e
jo
b
s
ch
ed
u
lin
g
f
r
am
ewo
r
k
.
B
y
ef
f
icien
tly
allo
ca
tin
g
task
s
,
th
is
alg
o
r
ith
m
r
e
d
u
ce
s
co
s
ts
,
m
ak
esp
an
,
an
d
en
e
r
g
y
u
s
e.
An
ad
a
p
tatio
n
o
f
th
e
g
r
e
y
wo
lf
o
p
tim
izer
is
s
h
o
wn
to
o
p
tim
ize
task
s
ch
ed
u
lin
g
b
y
tak
in
g
in
to
ac
co
u
n
t
s
ev
er
al
f
ac
to
r
s
,
in
clu
d
in
g
ex
ec
u
tio
n
tim
e,
r
eso
u
r
ce
n
ee
d
s
,
an
d
wo
r
k
d
u
r
atio
n
[
1
7
]
.
r
eso
u
r
ce
awa
r
e
p
r
io
r
itize
d
task
s
ch
ed
u
lin
g
(
R
APTS)
is
a
task
s
ch
ed
u
lin
g
m
eth
o
d
u
s
ed
i
n
a
h
eter
o
g
en
e
o
u
s
f
o
g
co
m
p
u
tin
g
en
v
ir
o
n
m
en
t.
T
h
e
g
o
al
is
to
c
o
m
p
lete
ac
tiv
ities
with
d
ea
d
lin
e
co
n
s
tr
ain
ts
o
n
tim
e,
r
ed
u
ce
r
e
ac
tio
n
tim
e
an
d
ex
p
en
s
e,
as
well
as
m
ak
esp
an
,
an
d
m
ax
im
ize
f
o
g
lay
er
r
eso
u
r
ce
u
s
e
[
1
8
]
.
T
ask
s
ch
ed
u
lin
g
is
a
wid
ely
ac
k
n
o
wled
g
e
d
is
s
u
e
in
f
o
g
co
m
p
u
tin
g
en
v
ir
o
n
m
en
ts
,
an
d
d
if
f
er
en
t
tech
n
iq
u
es
e
x
is
t
to
a
d
d
r
ess
it.
T
ask
s
ch
ed
u
lin
g
alg
o
r
ith
m
s
i
n
clu
d
e,
f
o
r
in
s
tan
ce
,
g
en
etic
alg
o
r
ith
m
s
a
n
d
o
th
er
ev
o
lu
tio
n
ar
y
tech
n
i
q
u
es,
as
well
as
s
war
m
in
tellig
en
ce
-
b
ased
s
tr
ateg
ies
lik
e
an
t
co
lo
n
y
o
p
tim
izatio
n
(
AC
O)
.
T
h
ese
m
etah
eu
r
is
tic
an
d
ap
p
r
o
x
im
ated
s
o
lu
tio
n
s
d
r
aw
in
s
p
ir
atio
n
f
r
o
m
n
atu
r
e.
Stated
d
i
f
f
er
en
tly
,
th
e
y
u
s
e
non
-
d
eter
m
in
is
tic
an
d
ef
f
icie
n
t
m
eth
o
d
s
to
s
ea
r
ch
th
e
s
ea
r
ch
s
p
ac
e
an
d
id
en
tif
y
th
e
id
e
al
p
ar
am
eter
s
[
1
9
]
.
Ov
er
all,
p
r
o
b
lem
s
o
f
task
s
ch
ed
u
lin
g
ca
n
b
e
class
if
ied
in
to
two
d
is
tin
ct
class
if
icatio
n
s
:
d
y
n
am
ic
s
ch
ed
u
lin
g
an
d
s
tatic
s
ch
ed
u
lin
g
.
Static
s
ch
ed
u
lin
g
p
r
ev
e
n
ts
th
e
d
is
clo
s
u
r
e
o
f
all
ap
p
licatio
n
task
d
etails
ea
r
lier
th
an
th
ei
r
ex
ec
u
tio
n
,
wh
ile
d
y
n
am
ic
s
c
h
ed
u
lin
g
r
estricts
ac
ce
s
s
to
in
f
o
r
m
atio
n
lik
e
th
is
to
r
u
n
ti
m
e.
T
wo
ty
p
es
o
f
alg
o
r
ith
m
s
ar
e
u
s
ed
f
o
r
s
tatic
s
ch
ed
u
lin
g
:
h
eu
r
is
tic
-
b
ased
m
eth
o
d
s
an
d
s
u
p
er
v
is
ed
s
to
ch
asti
c
s
ea
r
ch
-
b
ased
tech
n
iq
u
es.
T
h
e
h
e
u
r
is
tic
-
b
ased
alg
o
r
ith
m
s
ar
e
with
in
t
h
r
ee
ca
teg
o
r
ies:
lis
t
-
b
ased
,
clu
s
ter
in
g
,
an
d
jo
b
d
u
p
licatio
n
-
b
ased
[
2
0
]
.
T
h
e
id
ea
l
lo
ca
tio
n
f
o
r
th
e
f
o
g
-
to
-
cl
o
u
d
d
ep
l
o
y
m
en
t
o
f
s
ep
ar
ate
s
er
v
ice
co
m
p
o
n
en
ts
.
T
o
s
u
p
p
o
r
t
u
s
er
m
o
b
ilit
y
a
n
d
th
e
d
y
n
am
ic
Qo
S
v
ar
ia
b
les
in
d
icate
d
ea
r
lier
,
s
ep
ar
ate
p
r
o
ce
s
s
es
m
u
s
t
b
e
ca
r
r
ie
d
o
u
t in
an
ex
ec
u
tio
n
en
v
ir
o
n
m
e
n
t in
th
is
u
n
s
tab
le
[
2
1
]
.
Fo
g
co
m
p
u
t
in
g
o
r
clo
u
d
co
m
p
u
t
in
g
ex
t
en
d
e
d
in
t
o
ed
g
e
d
ev
ic
es
,
i
s
th
o
u
g
h
t
to
b
e
cr
u
c
ia
l
f
o
r
ef
f
e
ct
iv
e
ly
h
an
d
l
in
g
in
te
ll
ig
e
n
t
p
r
o
d
u
ct
io
n
l
in
e
ac
tiv
i
ti
es
.
I
t
s
h
ig
h
r
el
iab
il
i
ty
,
lo
w
l
at
en
cy
,
a
n
d
d
is
tr
ib
u
t
ed
ar
ch
it
ec
tu
r
e
a
llo
w
i
t
to
r
ea
ct
q
u
ick
ly
to
ta
s
k
r
eq
u
e
s
t
s
f
r
o
m
e
n
d
d
ev
ic
es
.
Ho
w
ev
e
r
,
ta
s
k
r
eq
u
es
t
s
in
in
te
ll
ig
en
t
p
r
o
d
u
ct
io
n
l
in
e
s
ty
p
ic
al
ly
n
ee
d
to
b
e
r
e
s
p
o
n
d
ed
to
q
u
ick
ly
,
th
e
r
ef
o
r
e
f
o
g
co
m
p
u
t
in
g
r
e
s
ea
r
ch
m
u
s
t
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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t J E
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&
C
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m
p
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I
SS
N:
2088
-
8
7
0
8
A
s
ystema
tic
r
ev
iew
o
f h
eu
r
is
tic
a
n
d
meta
-
h
eu
r
is
tic
meth
o
d
s
fo
r
d
yn
a
mic
ta
s
k
…
(
Ha
med
Ta
lh
o
u
ni
)
5989
co
n
ce
n
tr
at
e
o
n
d
y
n
am
ic
al
ly
s
ch
ed
u
l
in
g
a
ct
iv
i
t
ie
s
to
m
ax
im
iz
e
f
o
g
n
o
d
e
s
w
i
th
l
i
m
it
ed
r
e
s
o
u
r
ce
s
.
I
n
in
t
el
lig
en
t
m
an
u
f
ac
tu
r
in
g
l
in
e
s
,
t
h
i
s
i
s
c
r
u
c
ia
l
to
lo
w
er
in
g
ta
s
k
r
e
ac
tio
n
ti
m
e
s
an
d
r
ai
s
in
g
ta
s
k
co
m
p
le
t
io
n
r
at
es
[
2
2
]
.
A
n
ew
s
tr
ateg
y
n
am
ed
Hu
n
ter
Plu
s
in
v
esti
g
ates
th
e
im
p
ac
t
o
f
ex
p
a
n
d
in
g
th
e
g
ated
g
r
a
p
h
c
o
n
v
o
lu
ti
o
n
n
etwo
r
k
s
(
GGCN
s)
g
ated
r
ec
u
r
r
en
t
u
n
it
to
a
b
id
ir
ec
tio
n
al
g
ated
r
ec
u
r
r
en
t
u
n
it
.
T
h
e
ar
ticle
also
ex
am
in
es
th
e
u
s
e
o
f
co
n
v
o
lu
tio
n
al
n
e
u
r
al
n
etwo
r
k
s
(
C
NNs)
in
clo
u
d
-
f
o
g
o
p
tim
izatio
n
task
s
ch
ed
u
lin
g
[
2
3
]
.
A
p
r
io
r
ity
-
b
ased
p
r
ee
m
p
tiv
e
task
s
ch
ed
u
lin
g
with
a
m
u
lti
-
q
u
eu
e
s
ch
em
e
th
at
ac
h
iev
es
an
o
p
tim
al
task
ass
ig
n
m
en
t
f
o
r
th
e
d
elay
-
to
ler
a
n
t
ap
p
licatio
n
s
with
a
d
eg
r
ee
o
f
p
r
o
ce
s
s
in
g
d
elay
an
d
th
e
laten
c
y
-
s
en
s
itiv
e
f
o
g
ap
p
licatio
n
s
.
At
r
u
n
-
tim
e,
th
e
m
u
lti
-
d
im
en
s
io
n
al
q
u
an
tized
p
o
ly
g
o
n
(
M
QP
)
alg
o
r
ith
m
d
iv
id
es
task
s
in
to
s
h
o
r
t
an
d
lo
n
g
ac
co
r
d
in
g
to
t
h
eir
b
u
r
s
t
tim
e.
T
h
e
MQ
P
alg
o
r
ith
m
k
ee
p
s
d
if
f
er
en
t
task
q
u
eu
es
f
o
r
d
if
f
er
e
n
t
ca
teg
o
r
ies
o
f
task
s
an
d
ad
ju
s
ts
th
e
v
alu
e
o
f
th
e
tim
e
s
lo
t d
y
n
am
ically
f
o
r
p
r
ee
m
p
tio
n
[
2
4
]
.
A
p
ar
allel
m
u
lti
-
th
r
ea
d
in
g
p
la
tf
o
r
m
is
s
u
g
g
ested
to
f
i
n
d
th
e
o
p
tim
al
o
f
f
lo
a
d
in
g
s
o
l
u
tio
n
an
d
th
e
b
est
s
u
b
-
ca
r
r
ier
f
o
r
e
v
er
y
o
f
f
l
o
ad
e
d
task
.
Mo
s
t
im
p
o
r
tan
tly
,
o
u
r
co
n
tr
ib
u
tio
n
b
i
n
d
s
a
th
r
ea
d
t
o
ev
er
y
I
o
T
d
ev
ice
an
d
cr
ea
tes
a
p
o
p
u
latio
n
o
f
r
a
n
d
o
m
s
o
lu
tio
n
s
.
T
h
en
,
ev
er
y
p
o
p
u
latio
n
is
u
p
d
ated
an
d
ass
ess
ed
b
ased
o
n
th
e
s
u
g
g
ested
f
itn
ess
f
u
n
ctio
n
th
at
tak
es
in
to
ac
co
u
n
t
a
tr
ad
eo
f
f
b
etwe
en
d
elay
an
d
en
er
g
y
co
n
s
u
m
p
tio
n
.
W
h
en
n
ew
task
s
ar
e
r
ec
eiv
ed
in
ea
c
h
tim
e
s
lo
t,
an
e
v
alu
atio
n
is
co
n
d
u
cte
d
to
p
r
eser
v
e
s
o
m
e
m
em
b
er
s
o
f
th
e
o
l
d
p
o
p
u
latio
n
an
d
to
c
r
ea
te
n
e
w
in
d
iv
id
u
als
b
ased
o
n
s
o
m
e
cr
iter
ia
[
2
5
]
.
A
f
o
g
-
clo
u
d
f
it
alg
o
r
ith
m
th
at
d
is
tr
ib
u
tes
task
s
b
etwe
en
t
h
e
f
o
g
a
n
d
clo
u
d
in
an
ev
e
n
m
a
n
n
er
,
d
ep
en
d
in
g
o
n
p
r
io
r
ity
lev
els.
Ad
d
itio
n
ally
,
a
m
o
d
if
ied
Har
r
is
-
h
awk
s
o
p
tim
izatio
n
(
MH
HO)
in
s
p
ir
ed
m
eta
-
h
eu
r
is
tic
m
eth
o
d
is
s
u
g
g
ested
to
allo
ca
te
th
e
o
p
tim
al
av
ailab
le
r
eso
u
r
ce
to
a
task
in
a
lay
er
.
T
h
e
p
r
im
ar
y
aim
o
f
th
is
p
ap
er
is
to
m
in
im
ize
th
e
m
ak
esp
an
tim
e,
task
ex
ec
u
tio
n
co
s
t,
an
d
p
o
wer
co
n
s
u
m
p
tio
n
a
n
d
m
ax
im
ize
r
eso
u
r
ce
u
tili
za
tio
n
i
n
b
o
th
th
e
f
o
g
an
d
clo
u
d
lay
er
s
[
2
6
]
.
An
alg
o
r
ith
m
th
at
in
teg
r
ates
a
Hy
b
r
id
task
s
ch
ed
u
lin
g
m
eth
o
d
in
f
o
g
co
m
p
u
tin
g
b
ased
o
n
f
u
zz
y
l
o
g
ic
a
n
d
d
ee
p
r
ein
f
o
r
c
em
en
t
lear
n
in
g
(
HT
SF
FDR
L
)
alg
o
r
ith
m
with
a
T
ak
ag
i
-
Su
g
en
o
f
u
zz
y
in
f
er
e
n
ce
s
y
s
tem
.
T
h
r
o
u
g
h
r
ea
l
-
tim
e
in
ter
ac
tio
n
with
th
e
en
v
ir
o
n
m
e
n
t,
th
is
h
y
b
r
id
ap
p
r
o
ac
h
en
a
b
les
d
y
n
am
ic
task
p
r
io
r
itizatio
n
as we
ll a
s
o
n
lin
e
m
o
d
if
icatio
n
o
f
th
e
s
ch
ed
u
lin
g
r
u
les
[
2
7
]
.
An
elep
h
a
n
t
h
e
r
d
in
g
o
p
tim
iz
atio
n
(
E
HO)
alg
o
r
ith
m
b
ased
o
n
Sin
e
C
o
s
in
e
is
in
teg
r
ate
d
with
t
h
e
im
p
r
o
v
e
d
p
ar
ticle
s
war
m
o
p
ti
m
izatio
n
(
I
PS
O)
alg
o
r
ith
m
to
im
p
r
o
v
e
th
e
task
s
ch
ed
u
lin
g
p
er
f
o
r
m
an
ce
u
s
in
g
p
ar
am
eter
s
s
u
ch
as
l
o
ad
b
ala
n
cin
g
a
n
d
r
eso
u
r
ce
u
tili
za
tio
n
.
T
h
e
tr
ad
itio
n
al
E
HO
an
d
PS
O
alg
o
r
ith
m
s
ar
e
en
h
an
ce
d
u
s
in
g
a
s
in
e
co
s
in
e
-
b
ased
clan
-
u
p
d
ate
o
p
er
ato
r
an
d
h
u
m
an
g
r
o
u
p
o
p
tim
izer
th
at
en
h
a
n
ce
th
e
alg
o
r
ith
m
'
s
ex
p
lo
r
atio
n
an
d
e
x
p
lo
itatio
n
ca
p
a
b
ilit
ies
an
d
p
r
e
v
en
t
b
ein
g
ca
u
g
h
t
in
t
h
e
lo
ca
l o
p
tim
a
tr
ap
[
2
8
]
.
A
h
y
b
r
id
e
v
o
lu
tio
n
ar
y
task
s
ch
e
d
u
lin
g
an
d
VM
p
lace
m
en
t
(
H
E
T
SVP)
alg
o
r
ith
m
f
o
r
r
eliab
le
f
o
g
co
m
p
u
tin
g
task
s
ch
ed
u
lin
g
an
d
VM
p
lace
m
e
n
t.
T
h
ey
s
o
lv
e
th
e
task
ex
ec
u
tio
n
tim
e
o
p
tim
izatio
n
an
d
r
eso
u
r
ce
b
alan
ce
s
im
u
ltan
eo
u
s
ly
b
y
co
m
b
i
n
in
g
an
en
h
an
ce
d
p
ar
ticle
s
war
m
o
p
tim
izatio
n
alg
o
r
ith
m
with
a
n
o
v
el
VM
p
lace
m
en
t
s
tr
ateg
y
.
T
h
e
y
ap
p
ly
a
b
in
ar
y
en
c
o
d
in
g
s
tr
ateg
y
,
an
d
th
e
in
f
o
r
m
atio
n
a
b
o
u
t
t
h
e
p
o
s
itio
n
o
f
th
e
p
ar
ticle
is
r
ep
r
esen
ted
u
s
in
g
0
an
d
1
,
wh
ile
th
e
p
ar
ticle
v
elo
city
f
alls
with
in
th
e
in
ter
v
al
[
0
,
1
]
.
W
h
en
th
e
s
ce
n
ar
io
in
v
o
l
v
es
a
d
is
cr
ete
p
ar
ticle
s
war
m
,
ev
er
y
p
ar
ticl
e'
s
p
o
s
it
io
n
will
r
ep
r
esen
t
a
p
o
ten
tial
p
lan
f
o
r
s
ch
ed
u
lin
g
task
s
.
I
n
a
d
d
itio
n
to
th
at,
th
e
y
s
u
p
p
ly
th
e
ad
ap
t
iv
e
co
n
tr
ac
tio
n
f
ac
t
o
r
th
at
en
h
an
ce
s
th
e
p
ar
ticle
s
war
m
o
p
tim
izatio
n
m
eth
o
d
o
l
o
g
y
[
2
9
]
.
A
m
eta
-
h
eu
r
is
tic
alg
o
r
ith
m
h
y
b
r
id
g
r
ey
wo
lf
o
p
tim
izatio
n
(
GW
O)
alg
o
r
ith
m
with
p
ar
ti
cle
s
war
m
o
p
tim
izatio
n
(
PS
O)
is
ca
lled
h
y
b
r
id
p
a
r
ticle
s
war
m
o
p
tim
izatio
n
an
d
g
r
ey
wo
lf
o
p
tim
izatio
n
(
HPSO_
GW
O)
to
allo
ca
te
th
e
task
s
to
th
e
VM
s
to
m
ax
im
ize
th
e
Qo
S
[
3
0
]
.
E
x
am
in
e
s
ec
u
r
ity
-
co
n
s
cio
u
s
r
eso
u
r
ce
allo
ca
tio
n
in
d
ev
ice
-
to
-
d
ev
ice
-
b
ased
f
o
g
co
m
p
u
tin
g
s
y
s
tem
s
.
Fo
r
im
p
r
o
v
i
n
g
task
o
f
f
lo
ad
i
n
g
,
an
in
n
o
v
at
iv
e
m
u
lti
-
o
b
jectiv
e
f
u
n
ctio
n
is
in
tr
o
d
u
ce
d
to
o
p
tim
ize
d
elay
an
d
e
n
er
g
y
s
av
in
g
s
o
v
er
lo
ca
l
co
m
p
u
tin
g
as
well
as
th
e
c
o
s
t
o
f
s
ec
u
r
ity
b
r
ea
ch
es.
Var
io
u
s
m
u
lti
-
o
b
jectiv
e
m
eta
-
h
e
u
r
is
tic
alg
o
r
ith
m
s
,
in
clu
d
i
n
g
n
o
n
-
d
o
m
in
ated
s
o
r
tin
g
g
en
etic
alg
o
r
ith
m
I
I
(
NSGA
-
I
I
)
,
h
a
v
e
b
ee
n
in
tr
o
d
u
ce
d
in
th
e
last
d
ec
ad
e.
Ma
x
im
izin
g
o
b
jectiv
es,
lik
e
en
er
g
y
u
s
ag
e
an
d
d
elay
,
was
th
e
aim
o
f
u
tili
zin
g
th
ese
alg
o
r
ith
m
s
.
T
h
en
th
e
en
h
a
n
ce
d
NSGA
-
I
I
alg
o
r
ith
m
is
u
tili
ze
d
to
f
in
d
th
e
s
o
lu
tio
n
t
o
th
e
p
r
o
b
lem
.
Sig
m
a
Scalin
g
,
a
m
eth
o
d
to
ad
ju
s
t
th
e
f
itn
ess
v
alu
es
s
o
th
at
d
iv
er
s
ity
is
en
s
u
r
ed
in
th
e
p
o
p
u
latio
n
,
is
u
s
ed
to
m
a
n
ag
e
s
elec
tio
n
p
r
ess
u
r
e
in
th
is
alg
o
r
ith
m
.
I
n
co
r
p
o
r
atin
g
Sig
m
a
Scalin
g
,
th
e
alg
o
r
ith
m
'
s
ex
p
lo
r
atio
n
an
d
ex
p
lo
itatio
n
ab
ilit
ies
ar
e
well
m
an
ag
ed
to
im
p
r
o
v
e
its
p
o
ten
tial
to
escap
e
f
r
o
m
lo
ca
l
o
p
tim
a
a
n
d
av
o
id
p
r
em
atu
r
e
c
o
n
v
e
r
g
en
ce
[
3
1
]
.
A
h
y
b
r
id
d
is
cr
ete
o
p
tim
izatio
n
tech
n
i
q
u
e
n
am
ed
HDSOS
-
GOA,
u
tili
zin
g
th
e
d
y
n
am
ic
v
o
ltag
e
a
n
d
f
r
eq
u
e
n
cy
s
ca
lin
g
(
DVFS)
tec
h
n
iq
u
e,
is
s
u
g
g
ested
to
ad
d
r
ess
s
cien
tific
wo
r
k
f
l
o
w
s
ch
ed
u
lin
g
is
s
u
es
in
th
e
f
o
g
co
m
p
u
tin
g
p
ar
a
d
ig
m
.
HDSOS
-
GOA
in
teg
r
ates
th
e
s
ea
r
ch
attr
ib
u
tes
o
f
s
y
m
b
io
tic
o
r
g
a
n
is
m
’
s
s
ea
r
ch
(
SOS)
an
d
g
r
ass
h
o
p
p
e
r
o
p
tim
izatio
n
alg
o
r
ith
m
(
GOA)
alg
o
r
ith
m
s
an
d
th
e
ch
o
ice
o
f
t
h
ese
alg
o
r
i
th
m
s
f
o
r
ex
ec
u
tin
g
wo
r
k
f
lo
w
s
ch
ed
u
lin
g
is
d
ete
r
m
in
ed
b
y
th
e
p
r
o
b
a
b
ilit
y
c
o
m
p
u
ted
b
y
th
e
lear
n
in
g
au
t
o
m
ata.
T
h
e
HE
FT
tech
n
iq
u
e
is
em
p
lo
y
e
d
to
ca
lcu
late
th
e
task
o
r
d
er
[
3
2
]
.
A
f
ew
ca
teg
o
r
ies
f
o
r
s
tatic
an
d
d
y
n
am
ic
task
-
s
ch
ed
u
lin
g
tech
n
iq
u
es a
r
e
s
h
o
wn
in
Fig
u
r
e
1
.
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
.
6
,
Decem
b
e
r
20
25
:
5
9
8
6
-
6
0
0
0
5990
T
a
s
k S
c
he
dul
i
ng
S
t
a
t
i
c
D
yna
m
i
c
Heu
r
i
s
t
i
c
M
e
t
a
-
H
e
ur
i
s
t
i
c
R
e
a
l
-
T
i
m
e
H
e
ur
i
s
t
i
c
M
e
t
a
-
H
e
ur
i
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t
i
c
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i
s
t
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c
h
e
d
u
l
i
n
g
C
l
u
s
t
e
r
S
c
h
e
d
u
l
i
n
g
N
S
G
A
-
II
B
e
e
s
L
i
f
e
A
C
O
(
a
n
t
c
o
l
o
n
y
o
p
t
i
m
i
z
a
t
i
o
n
)
S
y
m
b
i
o
t
i
c
O
r
g
a
n
i
s
m
s
S
e
a
r
c
h
S
e
a
r
c
h
A
n
n
e
a
l
i
n
g
G
a
m
e
T
h
e
o
r
y
A
p
p
r
o
x
i
m
a
t
e
A
p
p
r
o
a
c
h
D
y
n
a
m
i
c
P
r
o
g
a
r
m
m
i
n
g
M
a
r
k
o
v
D
e
s
i
c
i
o
n
L
y
a
p
u
n
o
v
O
p
t
i
m
i
z
a
t
i
o
n
P
S
O
(
p
a
r
t
i
c
l
e
s
w
a
r
m
o
p
t
i
m
i
z
a
t
i
o
n
)
C
S
A
(
c
r
o
w
s
e
a
r
c
h
a
l
g
o
r
i
t
h
m
)
Fig
u
r
e
1
.
C
lass
if
icatio
n
o
f
s
ch
ed
u
lin
g
in
f
o
g
co
m
p
u
tin
g
3.
CL
AS
SI
F
I
CAT
I
O
N
O
F
DY
NAMI
C
T
A
SK
SCH
E
D
UL
I
NG
I
N
F
O
G
CO
M
P
UT
I
NG
T
h
e
f
o
llo
win
g
s
ec
tio
n
e
x
p
lai
n
s
a
task
-
s
ch
ed
u
lin
g
ap
p
r
o
ac
h
th
at
o
p
er
ates
with
in
th
e
co
n
tex
t
o
f
f
o
g
co
m
p
u
tin
g
.
I
n
a
d
d
itio
n
,
a
co
m
p
ar
ativ
e
s
tu
d
y
will
b
e
ca
r
r
ied
o
u
t,
wh
ich
will
in
clu
d
e
th
e
ex
am
i
n
atio
n
o
f
d
if
f
er
en
t
tec
h
n
iq
u
es
in
a
r
an
g
e
o
f
d
o
m
ain
s
,
s
u
c
h
as
th
e
f
u
n
d
am
e
n
tal
b
ac
k
d
r
o
p
,
ca
s
e
s
tu
d
ies,
ad
v
an
ta
g
es,
d
is
ad
v
an
tag
es,
an
d
,
f
i
n
ally
,
t
h
e
s
p
ec
if
ic
r
esu
lts
.
A
f
u
ll
r
esear
ch
an
d
r
ig
o
r
o
u
s
an
aly
s
i
s
o
f
h
eu
r
is
tic
an
d
m
etah
eu
r
is
tic
ap
p
r
o
ac
h
es
f
o
r
s
ch
ed
u
lin
g
is
b
ei
n
g
ca
r
r
ied
o
u
t,
an
d
t
h
is
r
ep
r
esen
ts
a
c
o
m
p
o
n
en
t
o
f
th
at
s
tu
d
y
.
Fig
u
r
e
2
r
e
p
r
esen
ts
th
e
f
o
g
co
m
p
u
tin
g
a
r
ch
itectu
r
e.
Fig
u
r
e
2
.
Ar
c
h
itectu
r
e
o
f
f
o
g
c
o
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
A
s
ystema
tic
r
ev
iew
o
f h
eu
r
is
tic
a
n
d
meta
-
h
eu
r
is
tic
meth
o
d
s
fo
r
d
yn
a
mic
ta
s
k
…
(
Ha
med
Ta
lh
o
u
ni
)
5991
3
.
1
.
Dy
na
m
ic
t
a
s
k
s
cheduli
n
g
m
et
ho
ds
Fo
g
co
m
p
u
tin
g
d
y
n
am
ic
task
s
ch
ed
u
lin
g
is
im
p
er
ativ
e
b
ec
a
u
s
e
o
f
th
e
v
ar
y
in
g
wo
r
k
lo
ad
a
n
d
lim
ited
r
eso
u
r
ce
s
.
R
ath
er
t
h
an
is
o
latin
g
r
ea
l
-
tim
e
s
ch
ed
u
lin
g
as
a
n
in
d
e
p
en
d
e
n
t
ca
teg
o
r
y
,
it
ca
n
b
e
co
n
s
id
er
e
d
a
s
ch
ed
u
lin
g
g
o
al
th
at
is
attain
ab
le
u
s
in
g
b
o
th
h
eu
r
is
tic
an
d
m
eta
-
h
eu
r
is
tic
tech
n
iq
u
es.
Fas
ter
d
ec
is
io
n
-
m
ak
in
g
m
ak
es
h
eu
r
is
tic
tech
n
iq
u
es
b
est
s
u
ited
f
o
r
r
ea
l
-
tim
e
s
ch
ed
u
lin
g
with
less
co
m
p
lex
task
s
.
I
n
co
n
tr
ast,
m
eta
-
h
eu
r
is
tic
alg
o
r
ith
m
s
p
r
o
v
id
e
m
o
r
e
o
p
tim
izatio
n
a
n
d
f
lex
ib
ilit
y
to
s
ch
ed
u
le
ef
f
icien
tly
in
r
ea
l
-
tim
e
f
o
r
co
m
p
licated
a
n
d
lar
g
e
-
s
ca
le
s
itu
atio
n
s
.
Hen
ce
,
th
e
n
ex
t
s
ec
tio
n
class
if
ies
s
ch
ed
u
lin
g
tech
n
iq
u
es
i
n
to
h
eu
r
is
tic
-
b
ased
an
d
m
eta
-
h
eu
r
is
tic
-
b
ased
r
ea
l
-
tim
e
s
ch
ed
u
lin
g
tech
n
iq
u
es,
in
s
tead
o
f
r
ea
l
-
ti
m
e
s
ch
ed
u
lin
g
as a
s
ep
ar
ate
ca
teg
o
r
y
.
3
.
1
.
1
.
H
euristic
-
ba
s
ed
re
a
l
-
t
im
e
s
cheduli
ng
Heu
r
is
tic
-
b
ased
s
ch
ed
u
lin
g
al
g
o
r
ith
m
s
ap
p
l
y
r
u
le
-
b
ased
o
r
in
tu
itiv
e
tech
n
iq
u
es
to
ass
ig
n
jo
b
s
in
a
f
ast
m
an
n
er
.
Su
ch
tech
n
iq
u
es
ar
e
tim
e
-
ef
f
icien
t
f
o
r
r
ea
l
-
tim
e
ap
p
licatio
n
s
as
t
h
ey
e
x
ec
u
te
i
n
a
v
er
y
s
h
o
r
t
tim
e
b
u
t
o
f
ten
d
o
n
o
t
d
eliv
er
o
p
t
im
al
s
o
lu
tio
n
s
.
Prio
r
ity
-
b
ased
s
ch
ed
u
lin
g
,
s
h
o
r
test
-
jo
b
-
f
ir
s
t,
an
d
r
o
u
n
d
-
r
o
b
in
s
ch
ed
u
lin
g
ar
e
s
o
m
e
ex
am
p
les.
3
.
1
.
2
.
M
et
a
-
heuris
t
ic
-
ba
s
ed
r
ea
l
-
t
im
e
s
cheduli
ng
Me
ta
-
h
eu
r
is
tic
tech
n
iq
u
es
b
a
s
ed
o
n
m
ath
em
atica
l
o
r
n
at
u
r
al
-
in
s
p
ir
ed
p
r
o
ce
s
s
es
p
r
o
v
i
d
e
wid
er
ex
p
lo
r
atio
n
f
o
r
id
ea
l
s
o
lu
tio
n
s
.
T
h
ese
m
eth
o
d
s
lik
e
GA,
P
SO,
an
d
AC
O
ar
e
im
p
lem
en
t
ed
to
o
p
tim
ize
th
e
s
ch
ed
u
lin
g
o
f
task
s
in
r
ea
l
-
tim
e
b
ased
o
n
r
eq
u
i
r
em
en
ts
s
u
ch
as
task
ex
ec
u
tio
n
tim
e,
av
ailab
ilit
y
o
f
r
eso
u
r
ce
s
,
an
d
p
o
wer
u
s
ag
e.
T
h
is
m
eth
o
d
is
ap
p
lied
m
o
r
e
in
s
itu
atio
n
s
wh
er
e
t
h
er
e
a
r
e
a
la
r
g
e
s
ize
an
d
d
y
n
am
ic
en
v
ir
o
n
m
en
t o
f
f
o
g
co
n
s
id
er
i
n
g
ef
f
ec
tiv
e
b
alan
cin
g
b
etwe
en
r
eso
u
r
ce
lim
itatio
n
s
an
d
laten
c
y
d
em
an
d
s
.
3.
1
.
3
.
H
euristic
t
ec
hn
iqu
e
s
b
a
s
ed
o
n dy
na
m
ic
t
a
s
k
s
cheduli
ng
I
n
a
f
o
g
co
m
p
u
tin
g
en
v
ir
o
n
m
en
t,
h
eu
r
is
tic
-
b
ased
s
ch
ed
u
l
in
g
tech
n
iq
u
es
u
s
e
in
tu
itiv
e
o
r
r
u
le
-
of
-
th
u
m
b
tech
n
i
q
u
es
to
q
u
ick
ly
an
d
ef
f
ec
tiv
ely
d
ec
i
d
e
h
o
w
to
d
iv
id
e
u
p
jo
b
s
am
o
n
g
av
aila
b
le
r
eso
u
r
ce
s
.
T
h
e
h
eu
r
is
tic
s
ch
ed
u
lin
g
s
tr
ateg
y
is
th
e
s
u
b
ject
o
f
two
r
esear
ch
.
Hu
s
s
ain
an
d
B
eg
h
[
3
3
]
p
r
o
p
o
s
ed
a
n
ew
alg
o
r
ith
m
,
h
y
b
r
id
f
lam
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r
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with
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ith
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(
HFSGA)
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ich
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ess
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ch
allen
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es a
s
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o
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atio
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d
th
e
ir
r
eg
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lar
ity
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th
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ad
.
T
h
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h
y
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r
id
h
eu
r
is
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alg
o
r
ith
m
aim
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n
ce
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e
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er
f
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ce
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ts
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y
im
p
r
o
v
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q
u
alit
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f
s
e
r
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(
Qo
S)
.
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o
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th
e
k
ey
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als
is
th
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o
p
tim
izatio
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o
f
th
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cr
ea
te
s
as
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a
s
th
e
p
r
o
ce
s
s
d
elay
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en
s
itiv
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(
PDST)
in
a
f
o
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clo
u
d
s
y
s
tem
.
Usi
n
g
a
n
ew
m
ix
co
n
ce
r
n
in
g
Flo
atin
g
s
ea
r
ch
in
g
an
d
g
en
eti
c
alg
o
r
ith
m
s
,
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wo
r
k
s
t
o
war
d
s
th
e
g
o
al
o
f
p
r
o
v
id
i
n
g
co
s
t
-
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f
ec
tiv
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an
d
q
u
ality
s
er
v
ice
-
awa
r
e
task
s
ch
ed
u
lin
g
,
wh
ich
u
ltima
tely
co
n
tr
ib
u
tes
to
th
e
en
h
an
ce
m
e
n
t
o
f
th
e
r
esil
ien
ce
an
d
s
tab
ilit
y
o
f
t
h
e
lar
g
er
p
r
o
c
ess
.
T
h
e
alg
o
r
ith
m
f
o
cu
s
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m
itig
atin
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laten
cy
is
s
u
es,
m
an
ag
in
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u
r
s
t
tr
af
f
ic
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ec
tiv
ely
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an
d
co
n
tr
ib
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tin
g
to
th
e
ef
f
icien
t
allo
ca
tio
n
o
f
t
ask
s
with
in
th
e
f
o
g
co
m
p
u
tin
g
en
v
ir
o
n
m
e
n
t.
Z
h
o
u
[
3
4
]
s
u
g
g
ested
a
h
eu
r
is
tic
task
s
ch
ed
u
lin
g
ap
p
r
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ig
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ata
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d
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p
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m
en
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th
at
s
u
p
p
o
r
t
m
ac
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lear
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u
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t
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m
o
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p
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a
r
ch
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d
en
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tain
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alu
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atr
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n
s
u
m
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tio
n
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d
laten
c
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to
ac
co
u
n
t.
B
y
im
p
r
o
v
in
g
c
r
o
s
s
o
v
er
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m
u
tatio
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p
er
ato
r
s
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ased
o
n
f
itn
ess
v
alu
es,
th
e
u
p
g
r
ad
e
d
a
d
ap
tiv
e
g
en
etic
alg
o
r
ith
m
im
p
r
o
v
es
o
n
co
n
v
en
tio
n
al
tec
h
n
iq
u
es.
T
h
e
r
esu
ltin
g
f
o
g
r
e
s
o
u
r
ce
s
ch
ed
u
lin
g
in
th
e
d
y
n
am
ic
lan
d
s
ca
p
e
o
f
in
tellig
en
t
m
an
u
f
ac
t
u
r
in
g
.
T
ab
le
1
p
r
esen
ts
th
e
class
if
icatio
n
o
f
th
e
p
r
ec
e
d
in
g
w
o
r
k
s
alo
n
g
with
th
eir
p
r
im
ar
y
b
ac
k
g
r
o
u
n
d
s
,
ad
v
an
c
em
en
ts
,
a
n
d
d
r
aw
b
ac
k
s
.
T
ab
le
2
d
escr
ib
es
h
o
w
t
h
e
ab
o
v
e
-
m
en
tio
n
e
d
p
u
b
licatio
n
s
wer
e
class
if
ied
,
in
clu
d
in
g
th
e
u
s
ed
alg
o
r
ith
m
,
Qo
S
v
ar
iab
les,
a
n
d
an
aly
tic
en
v
ir
o
n
m
en
t.
I
t
o
f
f
er
s
a
co
m
p
r
eh
e
n
s
iv
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v
iew
o
f
th
e
s
p
ec
if
ic
h
e
u
r
is
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alg
o
r
ith
m
s
im
p
lem
e
n
ted
in
d
y
n
am
ic
task
s
ch
ed
u
lin
g
s
ce
n
ar
io
s
.
T
h
e
tab
le
f
u
r
t
h
er
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s
tr
ates
h
o
w
k
ey
Qo
S
in
d
icato
r
s
s
u
ch
as
laten
cy
,
en
er
g
y
co
n
s
u
m
p
tio
n
,
a
n
d
ex
ec
u
tio
n
tim
e
ar
e
ad
d
r
ess
ed
in
v
a
r
io
u
s
ex
p
er
im
e
n
tal
s
etu
p
s
.
B
y
ca
teg
o
r
izin
g
th
ese
asp
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ts
,
it
b
ec
o
m
es
ea
s
ier
to
i
d
en
tify
p
er
f
o
r
m
a
n
ce
p
at
ter
n
s
an
d
p
o
ten
tial
o
p
tim
izatio
n
s
tr
ateg
ies
f
o
r
ea
ch
alg
o
r
ith
m
.
T
h
is
class
if
icati
o
n
en
ab
les
r
esear
ch
er
s
to
s
y
s
tem
atica
lly
ev
alu
ate
th
e
s
u
itab
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o
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d
if
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er
en
t
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eu
r
is
tic
ap
p
r
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es u
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v
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r
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f
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m
p
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co
n
d
itio
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s
.
T
ab
le
1
.
A
co
m
p
r
e
h
en
s
iv
e
an
a
ly
s
is
o
f
th
e
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v
an
ta
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o
f
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R
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A
d
v
a
n
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a
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e
Li
mi
t
a
t
i
o
n
[
3
3
]
F
o
g
-
c
l
o
u
d
e
n
v
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r
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n
me
n
t
−
S
u
p
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l
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−
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f
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m
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mi
z
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t
i
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.
−
Lo
a
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b
a
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n
c
i
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c
o
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s
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d
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t
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La
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.
[
3
5
]
I
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w
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r
k
a
n
d
st
o
r
a
g
e
c
a
p
a
c
i
t
y
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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3
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[
3
5
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3
.
1
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4
.
M
et
a
-
h
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m
et
ho
d
-
ba
s
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t
a
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k
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cheduli
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T
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t
g
u
i
d
e
t
h
e
ex
p
l
o
r
a
t
i
o
n
o
f
s
o
l
u
t
i
o
n
s
p
a
c
es
t
o
f
i
n
d
o
p
t
i
m
a
l
o
r
n
e
a
r
-
o
p
ti
m
a
l
s
o
l
u
t
i
o
n
s
.
T
h
ese
m
eth
o
d
s
ar
e
g
en
er
ic
p
r
o
b
lem
-
s
o
lv
i
n
g
f
r
am
ewo
r
k
s
th
at
m
ig
h
t
b
e
ap
p
lied
to
m
an
y
o
p
tim
izatio
n
is
s
u
es,
o
n
e
o
f
wh
ich
is
th
e
s
ch
ed
u
lin
g
o
f
task
s
in
an
en
v
ir
o
n
m
en
t
u
s
in
g
f
o
g
co
m
p
u
tin
g
.
W
e
will
an
aly
ze
T
en
s
tu
d
ies
in
th
e
m
eta
-
h
e
u
r
is
tic
class
.
Ku
m
ar
an
d
Kar
r
i
[
3
6
]
p
r
o
p
o
s
ed
a
n
atu
r
e
-
i
n
s
p
ir
ed
m
u
lti
-
o
b
jectiv
e
task
s
ch
e
d
u
lin
g
alg
o
r
ith
m
ca
lled
th
e
elec
tr
ic
ea
r
th
wo
r
m
o
p
tim
izatio
n
alg
o
r
ith
m
(
E
E
OA)
f
o
r
I
o
T
r
e
q
u
ests
in
a
clo
u
d
-
f
o
g
f
r
am
ewo
r
k
.
T
h
i
s
m
e
t
a
h
e
u
r
i
s
t
ic
-
b
a
s
e
d
m
et
h
o
d
o
f
f
e
r
s
a
p
o
t
e
n
t
i
a
l
w
a
y
t
o
a
c
h
iev
e
s
e
r
v
i
c
e
l
e
v
e
l
a
g
r
ee
m
e
n
t
(
S
L
A
)
d
e
m
a
n
d
s
w
h
i
l
e
t
a
k
i
n
g
c
l
o
u
d
-
f
o
g
e
n
v
i
r
o
n
m
e
n
t
s
'
c
o
m
p
l
e
x
i
t
y
i
n
t
o
a
c
c
o
u
n
t
,
t
h
e
r
eb
y
p
u
s
h
i
n
g
t
h
e
b
o
u
n
d
a
r
i
e
s
o
f
ta
s
k
s
c
h
e
d
u
l
i
n
g
.
Si
m
u
l
a
ti
o
n
r
e
s
u
l
t
s
s
h
o
w
e
d
t
h
a
t
t
h
e
p
r
o
p
o
s
e
d
m
e
t
h
o
d
e
f
f
e
c
ti
v
e
l
y
b
a
l
a
n
c
e
s
t
h
e
t
r
a
d
e
-
o
f
f
s
b
et
w
ee
n
e
f
f
i
c
i
e
n
c
y
,
c
o
s
t
,
a
n
d
e
n
e
r
g
y
c
o
n
s
u
m
p
t
i
o
n
.
T
o
h
a
n
d
l
e
t
h
e
c
o
m
p
l
e
x
i
t
y
o
f
t
h
e
n
o
n
l
i
n
e
a
r
is
s
u
e
,
N
a
ja
f
i
z
a
d
e
h
et
a
l
.
[
3
7
]
p
r
o
p
o
s
e
d
a
m
u
lt
i
-
o
b
j
ec
t
i
v
e
m
et
a
-
h
e
u
r
i
s
t
i
c
m
e
t
h
o
d
.
T
h
e
g
o
al
p
r
o
g
r
am
m
in
g
ap
p
r
o
ac
h
(
GPA)
em
p
h
asizes
th
e
r
eq
u
ir
e
m
en
t
f
o
r
s
af
e,
ef
f
icien
t,
an
d
ec
o
n
o
m
ical
jo
b
ex
ec
u
tio
n
b
y
em
p
lo
y
in
g
lim
ited
s
o
lu
tio
n
s
to
m
ee
t
m
u
ltip
le
o
b
jecti
v
es.
Mo
r
eo
v
er
,
th
e
alg
o
r
ith
m
attain
s
co
m
p
etitiv
e
o
u
tco
m
es
co
n
ce
r
n
in
g
s
er
v
ic
e
co
s
t,
in
d
icatin
g
its
ef
f
ec
tiv
en
ess
in
attain
in
g
a
b
alan
ce
d
an
d
o
p
tim
ized
r
eso
l
u
tio
n
f
o
r
jo
b
d
is
tr
ib
u
tio
n
i
n
f
o
g
co
m
p
u
tin
g
s
ettin
g
s
in
clu
d
i
n
g
in
ter
n
et
o
f
th
in
g
s
d
ev
ices.
On
th
e
o
th
er
h
a
n
d
,
to
o
p
tim
ize
b
o
th
th
e
co
s
t
a
n
d
th
e
m
ak
esp
an
,
Ap
at
et
a
l.
[
3
8
]
s
u
g
g
ested
a
tr
ad
itio
n
al
weig
h
ted
m
u
lti
-
o
b
j
ec
tiv
e
in
ter
n
et
o
f
th
in
g
s
s
er
v
ic
e
allo
ca
tio
n
.
Kh
an
et
a
l.
[
1
5
]
d
escr
ib
e
d
a
co
o
p
e
r
ativ
e
m
eth
o
d
f
o
r
m
an
ag
in
g
f
o
g
n
o
d
es
th
at
m
ak
es
u
s
e
o
f
th
e
b
lo
ck
ch
ain
H
y
p
er
led
g
er
f
ab
r
i
c
an
d
th
e
B
-
d
r
o
n
e
g
en
etic
a
lg
o
r
ith
m
with
m
etah
eu
r
is
tic
s
u
p
p
o
r
t.
E
n
h
an
ce
d
co
m
p
u
tin
g
an
d
p
r
o
ce
s
s
in
g
ef
f
icien
cy
ar
e
r
ea
ch
e
d
wh
ile
l
o
wer
in
g
laten
c
y
b
y
u
s
in
g
th
e
p
r
o
x
im
ity
o
f
u
n
m
a
n
n
ed
ae
r
ial
v
eh
icles
(
UAVs)
a
n
d
f
o
g
n
o
d
es,
m
ak
in
g
it
f
ea
s
ib
le
th
r
o
u
g
h
a
wir
eless
s
en
s
o
r
n
etwo
r
k
.
C
o
m
p
ar
in
g
th
e
B
-
d
r
o
n
e
ap
p
r
o
ac
h
'
s
s
im
u
latio
n
r
esu
lts
to
o
th
er
cu
ttin
g
-
ed
g
e
tech
n
iq
u
es,
it
is
s
h
o
wn
t
o
en
h
an
ce
n
etwo
r
k
r
o
b
u
s
tn
ess
,
d
ec
r
ea
s
e
th
e
co
s
t
o
f
d
r
o
n
e
-
le
d
g
er
p
r
eser
v
atio
n
,
in
cr
ea
s
e
p
er
f
o
r
m
an
ce
,
an
d
r
e
d
u
ce
co
m
p
u
tatio
n
al
co
s
ts
.
B
as
s
et
et
a
l.
[
3
9
]
a
n
ew
m
o
d
el
k
n
o
wn
as
h
y
b
r
id
f
lam
i
n
g
o
s
ea
r
ch
with
a
g
en
etic
alg
o
r
ith
m
(
HFSGA
)
is
u
s
ed
in
th
e
p
r
o
p
o
s
ed
s
tu
d
y
to
im
p
r
o
v
e
th
e
s
ch
ed
u
lin
g
o
f
task
s
.
T
h
e
ef
f
ec
tiv
en
ess
o
f
HFSGA is co
m
p
ar
ed
wit
h
th
at
o
f
o
th
e
r
well
-
k
n
o
wn
alg
o
r
ith
m
s
u
s
in
g
s
ev
en
f
u
n
d
am
e
n
tal
b
en
c
h
m
ar
k
o
p
tim
izatio
n
test
f
u
n
ctio
n
s
.
T
o
f
u
r
th
er
illu
s
tr
ate
th
e
im
p
o
r
tan
ce
o
f
th
e
f
in
d
in
g
s
,
th
e
Frie
d
m
an
r
an
k
test
is
u
s
ed
.
B
etter
r
e
s
u
lts
ar
e
s
h
o
wn
b
y
th
e
ad
o
p
ted
m
o
d
el
in
ter
m
s
o
f
m
ak
esp
an
,
co
s
t,
a
n
d
p
er
ce
n
tag
e
o
f
d
ea
d
lin
e
-
s
atis
f
ied
task
s
(
PDST)
.
Sin
g
et
a
l
.
[
4
0
]
s
tu
d
ied
a
w
h
ale
o
p
tim
iza
tio
n
r
eso
u
r
ce
allo
ca
tio
n
(
W
OR
A)
,
f
o
r
a
f
o
g
c
o
m
p
u
tin
g
s
y
s
tem
p
r
io
r
itized
f
o
r
laten
cy
-
s
en
s
itiv
e
I
o
T
s
er
v
ices.
I
n
a
lim
ited
r
eso
u
r
ce
-
b
ased
f
o
g
en
v
ir
o
n
m
e
n
t,
W
OR
A
em
p
l
o
y
s
d
y
n
am
ic
f
u
zz
y
c
-
m
ea
n
clu
s
ter
in
g
in
th
e
task
class
if
icatio
n
an
d
b
u
f
f
e
r
in
g
m
o
d
u
le
t
o
h
a
n
d
le
h
eter
o
g
e
n
e
o
u
s
r
ea
l
-
tim
e
task
s
,
class
if
y
in
g
an
d
b
u
f
f
er
i
n
g
u
s
in
g
in
cr
ea
s
ed
s
lack
co
s
t
to
d
i
v
id
e
co
n
c
u
r
r
e
n
t
co
m
p
u
ter
th
r
ea
d
s
.
Mo
r
eo
v
e
r
,
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
was
e
v
alu
ated
u
s
in
g
ev
alu
atio
n
m
etr
ics.
T
h
e
s
im
u
latio
n
o
u
tco
m
es
d
e
m
o
n
s
tr
ated
th
at
th
is
alg
o
r
ith
m
o
u
tp
er
f
o
r
m
ed
th
e
co
m
p
etitio
n
in
ter
m
s
o
f
av
er
ag
e
co
s
t,
an
d
d
u
r
atio
n
.
C
o
n
v
er
s
ely
,
Sh
u
k
la
an
d
Pan
d
ey
[
4
1
]
in
v
esti
g
ated
th
e
m
u
lti
-
o
b
jectiv
e
ar
tific
ial
alg
a
e
(
MA
A)
alg
o
r
ith
m
,
an
ef
f
e
c
tiv
e
m
eta
-
h
eu
r
is
tic
ap
p
r
o
ac
h
,
to
s
ch
ed
u
le
s
cien
tific
o
p
er
atio
n
s
in
a
h
eter
o
g
en
eo
u
s
f
o
g
co
m
p
u
tin
g
en
v
ir
o
n
m
en
t.
T
o
m
in
im
iz
e
ex
ec
u
tio
n
tim
es,
en
er
g
y
co
n
s
u
m
p
tio
n
,
an
d
c
o
s
ts
,
task
s
in
th
e
s
u
b
s
eq
u
en
t
s
tag
e
ar
e
s
ch
ed
u
led
u
s
in
g
th
e
MA
A
alg
o
r
ith
m
.
Ad
d
itio
n
ally
,
th
e
alg
o
r
ith
m
em
p
l
o
y
s
an
o
b
jectiv
e
f
u
n
ctio
n
b
ased
o
n
weig
h
ted
s
u
m
s
to
o
p
tim
ize
th
e
u
tili
za
tio
n
o
f
f
o
g
r
eso
u
r
ce
s
.
Fiv
e
b
en
ch
m
ar
k
s
cien
tific
p
r
o
ce
d
u
r
es
ar
e
u
s
ed
to
ass
es
s
th
e
p
r
o
p
o
s
ed
m
eth
o
d
o
lo
g
y
.
T
h
e
s
u
g
g
ested
alg
o
r
ith
m
'
s
p
er
f
o
r
m
an
ce
is
v
e
r
if
ied
b
y
c
o
n
tr
asti
n
g
its
r
esu
lts
with
th
o
s
e
o
f
tr
ai
n
ed
an
d
tr
a
d
itio
n
al
s
ch
ed
u
lin
g
alg
o
r
ith
m
s
.
T
h
e
r
esu
lts
s
h
o
w
s
ig
n
if
ican
t,
tr
ad
e
-
o
f
f
-
f
r
ee
im
p
r
o
v
em
en
ts
in
ex
ec
u
tio
n
tim
e,
en
er
g
y
u
s
ag
e,
an
d
o
v
er
all
co
s
t
o
v
e
r
ea
r
lier
ap
p
r
o
ac
h
es.
Kh
alee
l
et
a
l.
[
4
2
]
i
n
tr
o
d
u
ce
d
an
in
n
o
v
ativ
e
alg
o
r
it
h
m
th
at
em
p
h
asizes
m
u
ltip
le
o
b
jectiv
es
i
n
s
ch
ed
u
lin
g
a
n
d
c
o
m
p
u
tes
a
f
itn
es
s
f
u
n
ctio
n
th
r
o
u
g
h
a
n
t
co
lo
n
y
o
p
tim
izatio
n
.
T
o
im
p
r
o
v
e
co
m
p
u
tatio
n
al
e
f
f
icie
n
cy
,
th
e
alg
o
r
ith
m
e
f
f
icien
tly
d
ec
id
es
h
o
w
to
s
p
lit
u
p
a
p
p
lic
atio
n
s
ac
r
o
s
s
ed
g
e
an
d
clo
u
d
s
er
v
er
s
.
T
h
e
r
esu
lts
s
h
o
w
th
at
th
is
s
tr
ateg
y
lo
wer
s
en
er
g
y
u
s
ag
e
an
d
d
elay
s
ex
p
en
d
itu
r
es.
T
ab
le
3
o
u
tlin
es
th
e
m
ain
b
ac
k
g
r
o
u
n
d
,
an
d
a
d
v
an
ta
g
es.
an
d
d
r
awb
a
ck
s
ass
o
ciate
d
with
m
etah
eu
r
i
s
tic
-
b
ased
d
y
n
am
ic
s
ch
ed
u
lin
g
tech
n
iq
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8
b
s
o
f
t
w
a
r
e
)
Ku
m
ar
et
a
l.
[
4
4
]
h
av
e
s
u
g
g
ested
an
ar
tific
ial
in
tellig
en
c
e
(
AI
)
f
r
am
ewo
r
k
th
at
in
teg
r
ates
f
u
zz
y
m
o
d
els
to
m
ak
e
in
tellig
en
t
d
e
cisi
o
n
s
ab
o
u
t
th
e
ch
o
ice
o
f
wo
r
k
in
g
d
is
tan
tly
o
n
d
ata
ce
n
ter
s
in
th
e
clo
u
d
o
r
f
o
g
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2
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8
8
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8
7
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I
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&
C
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p
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g
,
Vo
l.
15
,
No
.
6
,
Decem
b
e
r
20
25
:
5
9
8
6
-
6
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5994
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y
o
n
-
ed
g
e
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v
ices
to
co
m
p
lete
task
s
.
I
n
th
e
c
o
llab
o
r
ativ
e
cl
o
u
d
-
f
o
g
s
ce
n
ar
i
o
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e
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o
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tio
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s
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W
OA,
a
m
etah
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r
is
tic
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e,
to
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iev
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tim
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m
task
-
to
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r
eso
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r
ce
m
a
p
p
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g
.
E
x
t
en
s
iv
e
ex
p
er
im
en
tal
an
aly
s
is
estab
lis
h
es
th
e
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f
ec
tiv
en
ess
ab
o
u
t
th
e
ef
f
ec
ti
v
en
ess
o
f
th
e
s
u
g
g
ested
s
tr
ateg
y
in
en
h
an
cin
g
th
e
g
en
er
al
ef
f
icac
y
o
f
in
d
u
s
tr
y
5
.
0
d
ea
d
lin
e
-
awa
r
e
ass
ig
n
m
en
ts
,
with
s
u
b
s
tan
tial
g
ain
s
b
ein
g
n
o
ticed
in
m
ak
esp
an
,
ex
ec
u
tio
n
co
s
t,
r
e
jectio
n
r
atio
,
an
d
en
er
g
y
u
s
a
g
e
wh
en
co
m
p
a
r
ed
with
alter
n
ativ
e
ap
p
r
o
ac
h
es.
L
iu
et
a
l.
[
1
6
]
d
escr
ib
e
AO_
AVOA
,
a
h
y
b
r
id
m
eta
-
h
eu
r
is
t
ic
(
MH
)
m
eth
o
d
t
h
at
u
s
es
Aq
u
ila
o
p
tim
izer
(
AO)
an
d
th
e
Af
r
ican
v
u
ltu
r
es
o
p
tim
izatio
n
m
eth
o
d
(
AVOA
)
to
s
ch
ed
u
le
I
o
T
r
e
q
u
ests
in
I
o
T
f
o
g
-
clo
d
n
etwo
r
k
s
.
B
y
u
s
in
g
AO
o
p
er
ato
r
s
to
lo
ca
te
th
e
o
p
tim
u
m
s
o
lu
tio
n
w
h
ile
attem
p
tin
g
to
id
en
tify
th
e
id
ea
l
s
ch
ed
u
lin
g
s
o
lu
tio
n
,
AO_
AVOA
en
h
an
c
es
th
e
AVOA
ex
p
lo
r
e
p
h
ase
.
B
ased
o
n
p
e
r
f
o
r
m
an
ce
m
et
r
ics
co
n
s
is
tin
g
o
f
m
ak
esp
an
an
d
th
r
o
u
g
h
p
u
t,
A
O_
AVOA
p
r
o
v
ed
to
b
e
h
ig
h
l
y
ef
f
ec
tiv
e
in
s
o
lv
in
g
th
e
s
ch
ed
u
lin
g
p
r
o
b
lem
in
I
o
T
-
f
o
g
-
clo
u
d
n
etwo
r
k
s
wh
en
co
m
p
a
r
ed
to
m
eth
o
d
s
o
f
AV
OA,
AO,
f
ir
ef
l
y
alg
o
r
ith
m
(
F
A)
,
p
ar
ticle
s
war
m
o
p
tim
izatio
n
(
PS
O)
,
an
d
Har
r
i
s
h
awk
s
o
p
tim
izatio
n
(
HHO)
.
3
.
1
.
5
.
Rea
l
-
t
i
m
e
-
ba
s
ed
dy
na
m
ic
t
a
s
k schedu
li
ng
Fo
u
r
d
if
f
er
e
n
t
r
esear
ch
r
elate
d
to
r
ea
l
-
tim
e
s
ch
ed
u
lin
g
we
r
e
lo
o
k
ed
at.
A
f
u
zz
y
lo
g
ic
-
b
ased
task
s
ch
ed
u
lin
g
tec
h
n
iq
u
e
th
at
d
iv
id
es
u
p
th
e
wo
r
k
lo
a
d
in
a
f
o
g
-
clo
u
d
co
m
p
u
tin
g
s
y
s
tem
b
e
twee
n
th
e
f
o
g
an
d
clo
u
d
lay
er
s
was
p
r
esen
ted
b
y
Ali
et
a
l.
[
4
5
]
.
T
h
e
m
eth
o
d
s
elec
ts
th
e
ap
p
r
o
p
r
iate
p
r
o
ce
s
s
to
d
o
th
e
g
iv
en
jo
b
b
y
u
s
in
g
th
e
ts
k
.
T
h
e
s
im
u
lat
io
n
'
s
r
esu
lts
s
h
o
w
th
at
th
e
s
u
g
g
ested
s
tr
ateg
y
lo
wer
s
th
e
a
v
er
ag
e
t
u
r
n
ar
o
u
n
d
tim
e,
m
ak
esp
an
tim
e,
an
d
d
ela
y
r
ate.
R
ea
l
tim
e
h
eter
o
g
en
e
o
u
s
h
ier
ar
ch
ical
s
ch
e
d
u
lin
g
(
R
T
H2
S
)
,
a
s
ch
e
d
u
lin
g
alg
o
r
ith
m
p
r
o
p
o
s
ed
b
y
Heu
v
el
[
4
6
]
,
is
d
esig
n
e
d
to
m
a
n
ag
e
a
co
llectio
n
o
f
r
ea
l
-
ti
m
e
task
s
with
in
a
h
eter
o
g
en
e
o
u
s
in
teg
r
ated
f
o
g
-
clo
u
d
ar
c
h
itectu
r
e.
T
h
e
alg
o
r
ith
m
d
iv
id
es
th
e
task
ac
co
r
d
in
g
to
its
s
ize
an
d
d
ea
d
lin
e
r
eq
u
ir
em
e
n
ts
,
o
r
it
s
elec
ts
a
p
r
ef
er
a
b
le
f
o
g
n
o
d
e
f
o
r
th
e
task
'
s
ex
ec
u
tio
n
.
T
h
e
s
u
g
g
ested
m
et
h
o
d
'
s
s
im
u
latio
n
r
esu
lts
s
h
o
w
lo
we
r
co
s
ts
an
d
h
ig
h
er
s
u
cc
ess
r
ates.
I
n
an
o
t
h
er
wo
r
k
,
Ma
tti
a
an
d
B
er
ald
i
[
4
7
]
p
r
esen
ted
a
d
ec
e
n
tr
alize
d
a
p
p
r
o
ac
h
f
o
r
r
ea
l
-
tim
e
d
y
n
am
ic
s
ch
ed
u
lin
g
in
f
o
g
c
o
m
p
u
tin
g
e
n
v
ir
o
n
m
en
ts
th
at
is
b
ased
o
n
r
ein
f
o
r
ce
m
e
n
t
lear
n
i
n
g
(
R
L
)
.
E
ac
h
f
o
g
n
o
d
e
was
d
esig
n
ed
to
h
av
e
th
is
alg
o
r
ith
m
in
s
talled
to
en
ab
le
au
to
n
o
m
o
u
s
s
ch
ed
u
lin
g
c
h
o
i
ce
s
d
ep
en
d
in
g
o
n
th
e
cir
c
u
m
s
tan
ce
s
at
h
an
d
.
T
h
e
cl
ass
if
icatio
n
o
f
th
e
af
o
r
em
en
tio
n
ed
s
tu
d
ies,
in
clu
d
in
g
th
eir
p
r
im
ar
y
co
n
te
x
t,
ad
v
an
tag
es,
an
d
lim
itatio
n
s
,
is
ex
p
lain
ed
in
T
ab
le
5
.
T
h
e
o
u
tc
o
m
es
o
f
th
e
d
elay
-
b
a
s
ed
s
im
u
latio
n
d
em
o
n
s
tr
ated
t
h
at,
u
n
d
er
b
o
th
f
ix
ed
lo
ad
c
o
n
d
itio
n
s
an
d
r
ea
l
-
wo
r
ld
g
e
o
g
r
a
p
h
ic
s
ce
n
ar
i
o
s
,
th
er
e
is
a
m
in
im
u
m
co
s
t
t
o
co
m
p
lete
with
in
th
e
d
ea
d
lin
e
in
a
wo
r
k
th
at
is
eq
u
al
to
ev
e
r
y
n
o
d
e
.
R
an
jan
a
n
d
Sh
a
r
m
a
[
4
8
]
s
u
g
g
ested
an
ap
p
r
o
ac
h
th
at
p
r
esen
ts
two
s
c
h
ed
u
ler
s
b
ased
o
n
n
o
n
lin
ea
r
m
ath
e
m
atica
l
p
r
o
g
r
am
m
in
g
:
o
n
e
f
o
r
clo
u
d
co
m
p
u
tin
g
an
d
an
o
th
er
f
o
r
f
o
g
co
m
p
u
tin
g
.
T
h
ese
s
ch
ed
u
ler
s
ch
allen
g
e
p
r
e
-
estab
lis
h
ed
co
n
v
e
n
tio
n
s
b
y
a
llo
ca
tin
g
task
s
d
ep
en
d
in
g
o
n
attr
ib
u
tes
lik
e
p
er
f
o
r
m
an
ce
an
d
av
ailab
ilit
y
.
Fu
zz
y
lo
g
ic
is
u
s
ed
in
th
is
s
tr
ateg
y
to
d
iv
i
d
e
wo
r
k
ac
r
o
s
s
th
e
clo
u
d
a
n
d
f
o
g
lay
er
s
b
ased
o
n
task
n
ee
d
s
(
c
o
m
p
u
tin
g
,
s
to
r
ag
e,
b
a
n
d
wid
th
)
,
as
well
as
co
n
s
tr
ain
ts
(
s
ize
o
f
d
ata,
d
ea
d
lin
es).
T
h
e
s
tr
ateg
y
o
u
tp
er
f
o
r
m
s
c
u
r
r
en
t
m
eth
o
d
s
in
ter
m
s
o
f
co
m
p
leted
task
s
,
av
er
a
g
e
t
u
r
n
ar
o
u
n
d
tim
e,
ca
lcu
latio
n
tim
e,
an
d
laten
cy
r
ate,
as
s
h
o
wn
b
y
s
im
u
latio
n
test
in
g
.
T
a
b
le
6
d
elin
ea
tes
th
e
cr
iter
ia
u
s
ed
to
class
if
y
th
e
af
o
r
em
en
tio
n
ed
ar
ticles,
in
clu
d
in
g
th
e
a
n
aly
tic
en
v
i
r
o
n
m
e
n
t,
Qo
S e
lem
en
ts
,
an
d
ap
p
lied
m
eth
o
d
.
T
ab
le
5
.
A
co
m
p
r
e
h
en
s
iv
e
co
m
p
ar
is
o
n
o
f
r
ea
l
-
tim
e
task
s
ch
ed
u
lin
g
alg
o
r
ith
m
s
R
e
f
e
r
e
n
c
e
M
a
j
o
r
c
o
n
t
e
x
t
A
d
v
a
n
t
a
g
e
Li
mi
t
a
t
i
o
n
[
4
5
]
F
o
g
-
c
l
o
u
d
c
o
m
p
u
t
i
n
g
−
I
t
r
e
d
u
c
e
s
t
h
e
w
a
i
t
i
n
g
t
i
me
o
f
t
h
e
t
a
s
k
s.
−
Th
e
l
o
a
d
b
a
l
a
n
c
i
n
g
mec
h
a
n
i
sm
e
n
h
a
n
c
e
s
t
h
e
o
v
e
r
a
l
l
e
f
f
i
c
i
e
n
c
y
−
N
e
e
d
f
o
r
f
u
r
t
h
e
r
i
n
v
e
s
t
i
g
a
t
i
o
n
o
n
c
h
a
n
n
e
l
b
a
n
d
w
i
d
t
h
.
−
I
n
c
o
mp
l
e
t
e
c
o
n
s
i
d
e
r
a
t
i
o
n
o
f
p
r
i
v
a
c
y
a
n
d
m
o
b
i
l
i
t
y
[
4
6
]
H
i
e
r
a
r
c
h
i
c
a
l
h
e
t
e
r
o
g
e
n
e
o
u
s
f
o
g
n
e
t
w
o
r
k
s
Ef
f
i
c
i
e
n
t
r
e
s
o
u
r
c
e
u
t
i
l
i
z
a
t
i
o
n
.
M
i
n
i
m
i
z
e
t
h
e
o
v
e
r
a
l
l
c
o
s
t
V
a
r
i
a
t
i
o
n
s
i
n
w
o
r
k
l
o
a
d
p
a
t
t
e
r
n
s c
a
n
b
e
c
h
a
l
l
e
n
g
i
n
g
[
4
7
]
S
mart
c
i
t
i
e
s
M
a
x
i
m
i
z
e
t
h
e
t
a
s
k
e
x
e
c
u
t
i
o
n
t
i
m
e
.
I
n
c
r
e
a
se
d
c
o
m
p
l
e
x
i
t
y
o
f
s
y
st
e
m s
t
a
t
e
[
4
8
]
F
o
g
c
o
mp
u
t
i
n
g
u
s
i
n
g
d
e
e
p
l
e
a
r
n
i
n
g
t
e
c
h
n
i
q
u
e
s
I
n
c
r
e
a
se
t
h
e
e
f
f
i
c
i
e
n
c
y
o
f
e
x
e
c
u
t
i
o
n
o
f
t
a
s
k
s
i
n
I
o
T
sy
s
t
e
ms
Lo
w
s
c
a
l
a
b
i
l
i
t
y
T
ab
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n
d
icato
r
s
a
n
d
h
o
w
th
ese
will
im
p
ac
t
th
e
ef
f
ec
ti
v
en
ess
o
f
task
s
ch
ed
u
lin
g
in
s
itu
atio
n
s
r
eg
ar
d
in
g
f
o
g
co
m
p
u
tin
g
.
Fig
u
r
e
3
d
y
n
a
m
ic
s
ch
ed
u
lin
g
m
eth
o
d
s
ca
teg
o
r
ies
in
f
o
g
e
n
v
ir
o
n
m
en
t
s
h
o
ws
h
o
w
jo
b
s
ch
ed
u
lin
g
s
tr
ateg
ies
ar
e
ca
teg
o
r
ized
in
a
f
o
g
co
m
p
u
tin
g
e
n
v
ir
o
n
m
en
t.
T
h
e
m
o
s
t
p
o
p
u
lar
jo
b
s
ch
ed
u
l
in
g
tech
n
iq
u
es
ar
e
m
etah
eu
r
is
tic
-
b
ased
alg
o
r
ith
m
s
,
wh
ich
h
av
e
a
6
2
.
5
% u
tili
za
tio
n
r
ate.
Mo
r
e
th
a
n
an
y
o
t
h
er
alg
o
r
ith
m
,
h
e
u
r
is
tic
m
eth
o
d
s
h
a
v
e
b
ee
n
im
p
lem
e
n
t
ed
in
f
o
g
c
o
m
p
u
tin
g
t
o
o
b
tain
th
e
m
o
s
t
ef
f
ec
tiv
e
ap
p
r
o
ac
h
f
o
r
p
er
f
o
r
m
in
g
task
s
o
r
jo
b
s
.
I
t
is
im
p
o
r
ta
n
t
to
h
ig
h
lig
h
t
th
at
wh
e
n
it
co
m
es
to
f
o
g
co
m
p
u
tin
g
,
f
o
r
m
al
tech
n
iq
u
es
ca
n
b
e
a
v
alu
ab
le
to
o
l f
o
r
v
alid
atin
g
t
h
e
co
r
r
ec
tn
ess
o
f
d
y
n
am
ic
s
ch
ed
u
lin
g
a
p
p
r
o
ac
h
es a
n
d
ev
alu
atin
g
f
u
n
ctio
n
al
p
r
o
p
er
ties
.
T
h
e
well
-
k
n
o
wn
s
im
u
latio
n
p
r
o
g
r
a
m
s
C
lo
u
d
Sim
an
d
iFo
g
Sim
co
n
tr
o
l
th
e
co
m
p
u
tatio
n
al
en
v
ir
o
n
m
en
t
in
f
o
g
c
o
m
p
u
tin
g
s
tu
d
ies.
T
h
ese
to
o
ls
o
f
f
e
r
a
th
o
r
o
u
g
h
p
latf
o
r
m
f
o
r
r
esear
c
h
er
s
an
d
in
cl
u
d
e
all
ap
p
r
o
ac
h
es
u
s
ed
in
task
s
ch
ed
u
lin
g
in
v
esti
g
atio
n
s
.
T
h
ese
s
im
u
latio
n
s
ettin
g
s
ar
e
th
e
m
ain
p
lace
wh
er
e
s
ch
ed
u
lin
g
al
g
o
r
ith
m
s
a
r
e
tes
ted
an
d
v
er
if
ie
d
b
ec
a
u
s
e
o
f
t
h
e
d
if
f
ic
u
lties
an
d
c
o
m
p
lex
iti
es
ass
o
ciate
d
with
r
ea
l
-
wo
r
ld
im
p
lem
e
n
tatio
n
.
T
h
e
Qo
S
elem
en
ts
in
f
o
g
en
v
i
r
o
n
m
en
t
ex
p
e
r
im
en
ts
ar
e
s
h
o
wn
in
Fig
u
r
e
4
Qo
S
co
n
s
id
er
a
tio
n
s
in
f
o
g
co
m
p
u
tin
g
task
s
ch
ed
u
lin
g
r
e
s
ea
r
ch
.
T
h
e
m
o
s
t
s
ig
n
if
ican
t
co
m
p
o
n
en
t,
with
a
3
6
%
u
tili
za
tio
n
r
ate,
is
tim
e.
C
er
tain
d
if
f
icu
lt
task
s
,
s
u
ch
as
ev
alu
atin
g
p
r
iv
ac
y
is
s
u
es,
s
ca
lab
ilit
y
,
an
d
r
eliab
ilit
y
t
h
at
h
av
e
not
b
ee
n
ass
es
s
ed
in
f
o
g
c
o
m
p
u
tin
g
,
ar
e
in
v
o
lv
e
d
in
ass
ess
in
g
th
e
ef
f
e
ctiv
en
ess
o
f
s
ch
ed
u
lin
g
.
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