TELKOM
NIKA
, Vol.14, No
.1, March 2
0
1
6
, pp. 273~2
7
9
ISSN: 1693-6
930,
accredited
A
by DIKTI, De
cree No: 58/DIK
T
I/Kep/2013
DOI
:
10.12928/TELKOMNIKA.v14i1.2256
273
Re
cei
v
ed
Jul
y
1, 2015; Re
vised Novem
ber 23, 20
15;
Accept
ed De
cem
ber 1
0
, 2015
Optimization of Wireless Internet Pricing Scheme in
Serving Multi QoS Network Using Various Attributes
Irmeil
y
a
na*,
Fitri Ma
y
a
P
u
spita, Iffah
Husniah
Jurusan Matematika, Faku
lta
s
Matematika d
an Ilmu Pen
get
ahu
an Al
am, Universit
a
s Sri
w
i
j
a
y
a,
Jln. Ra
ya Pra
b
u
muli
h KM 32 Inder
ala
y
a O
g
a
n
Ilir Sumatera
Selata
n Indo
ne
sia
*Corres
p
o
ndi
n
g
author, em
ail
:
imel_u
nsri@
y
aho
o.co.id
A
b
st
r
a
ct
Pricing scheme in wireless networks were d
e
velope
d to provid
e max
i
mu
m be
nefit to the intern
e
t
service
provi
d
er (ISP), w
here the
giv
en
sche
m
e
c
a
n
guar
ante
e
cus
t
omer
satisfac
tion
and
servi
c
e
provi
ders w
ho
use suc
h
serv
i
c
es. So that th
e pro
pos
ed
mo
del s
h
o
u
ld
be
abl
e to attract
consu
m
er int
e
r
e
st
in ap
plyi
ng su
ch services. In this researc
h
w
e
establis
hed w
i
rel
e
ss pricin
g mod
e
l
that involv
e QoS
attributes th
en
the
mo
de
l w
i
l
l
be
transfor
m
ed i
n
to
a mo
d
e
l
of opti
m
i
z
a
t
i
on.
Prici
n
g
mo
dels in
w
i
r
e
les
s
netw
o
rks w
ill be studie
d
by lo
okin
g
at existin
g
mo
de
ls as a nonli
n
e
a
r pro
g
ra
mmin
g pro
b
l
e
m
that can b
e
solve
d
opti
m
all
y
usin
g LINGO 11.0. T
he so
lu
tion is to
maxi
mi
z
e
the tota
l p
r
ice for the c
o
n
nectio
n
b
a
sed
o
n
the QoS p
a
ra
meters. Opti
mal res
u
lts i
n
th
e
maxi
mi
z
i
n
g
o
f
pricin
g sch
e
m
e is
achi
eve
d
w
hen pr
ovid
er
s set
the incre
a
se of
price cha
n
g
e
s due to QoS ch
ang
es an
d nu
mb
er of QoS valu
e.
Ke
y
w
ords
: w
i
reless i
n
ternet p
r
icing,
multi Qo
S netw
o
rk, Qo
S attribute, opti
m
i
z
at
io
n
Copy
right
©
2016 Un
ive
r
sita
s Ah
mad
Dah
l
an
. All rig
h
t
s r
ese
rved
.
1. Introduc
tion
In runnin
g
the busin
ess n
e
twork of the Inter
net we
cannot se
pa
ra
te
from the discussio
n
on pri
c
ing n
e
twork sch
e
me
s wh
ere the I
n
ternet is
sup
posed to prov
ide the best
QoS that means
providin
g diff
erent
networks fo
r
certai
n
service
s
[1,
2]. Di
scussio
n
o
n
the
wi
red
pricing
Intern
et
of
multi services [3-5] and
mu
lti-QoS net
wo
rk [6] h
a
s
bee
n discu
s
sed i
n
previous st
udie
s
. Fro
m
the
discu
ssi
on
we can
sho
w
t
hat the
optim
al solution i
n
ord
e
r to p
r
o
v
ide ben
efits to the int
e
rn
et
servi
c
e
provider (ISP) i
s
d
e
termin
ed via
the
deter
min
a
tion of
the
cost of
ba
si
c,
premi
u
m
qual
ity
and QoS leve
ls.
The devel
op
ment of the wirel
e
ss n
e
twork i
s
very importa
nt in busi
n
e
ss life
[7-9] an
d
techn
o
logy [1
0, 11]. Huan
g
and Ga
o ap
proa
ch th
at
it was
refe
rre
d
to as optimi
z
ation problem
s.
Con
s
um
ers can make a p
r
ofit by using
the disc
ount
fee that is consi
dered a
model no
nlin
ear
[12]. Previou
s
re
sea
r
ch
on the
mo
d
e
ling
of
no
n
linear Wi
rel
e
ss finan
cin
g
sche
me
ever
unde
rtaken
b
y
[13]. Wirele
ss networks
are
develo
p
e
d
to take adv
antage
of the
use
r
. G
r
ub
b
[12]
and Wu [14] stated that the financi
ng o
f
two par
t tari
ff schem
e ca
n improve u
s
er sati
sfactio
n
.
The sim
u
latio
n
results
sug
gest a lin
k be
tween the
co
st of elasticity
factor u
s
er a
c
ceptan
ce.
In fact, rece
nt numerou
s re
sea
r
ch focused on
the wireless
p
r
icin
g are
availa
ble [15-19].
Only a few rese
arch focu
sed o
n
the mathemati
c
al
modeling of broa
dba
nd pricing [20] or
with
compl
e
te information on
users
and utility function
[21]. Scarce research
exami
ne the wi
reless
prici
ng throug
h mathemati
c
al prog
ram
m
i
ng and
com
e
up with the o
p
timization p
r
oblem. Mainl
y
,
the re
sea
r
ch
on wi
rele
ss p
r
icin
g de
scri
b
e
the surveys of method
s t
o
ch
arg
e
the
3G/4G p
r
i
c
in
g ,
then proceed
to simulation
method to find the re
sults and l
a
stly a
nalyze the
re
sults.
Ho
wever,
we
attempt t
o
intro
d
u
c
e th
e mathe
m
atical mod
e
ling
o
f
the
wirel
e
ss p
r
ici
ng
mo
del of
with
Q
o
S
attributes
su
ch as b
and
wi
dth, end-to
-e
nd delay,
an
d BER (Bit Erro
r Rate) by
con
s
ide
r
in
g the
model of the
wirel
e
ss n
e
twork
as n
onlin
ear p
r
og
ram
m
ing problem
s that are
sol
v
ed optimally
by
usin
g LING
O
11.0. Obtained solution
is expe
cted
to provide inf
o
rmatio
n on
the relation
ship
betwe
en the factors of a
c
cept
an
ce an
d co
st factors that
explaine
d
mathematica
lly.
Thus, the m
a
in co
ntributi
on of this p
aper
i
s
to provide a mat
hematical progra
mming
involving Qo
S attribute in wirel
e
ss net
work o
p
ti
mizati
on that involves thre
e QoS
attributes. The
new
app
roa
c
h may p
r
ovid
e additio
nal i
n
formatio
n
to
se
rvice
providers in
ado
p
t
ing a
wirel
e
ss
scheme
with certai
n QoS a
ttributes.
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 16
93-6
930
TELKOM
NIKA
Vol. 14, No. 1, March 2
016 : 273 – 2
7
9
274
2. Rese
arch
Metho
d
In this
study
, the p
r
icin
g
schem
es p
r
opo
sed
by [
13] with
Qo
S attribute
s
su
ch
as
band
width, e
nd-to
-en
d
del
ay, and BER will be impr
oved by adding
the origin
al model of [22-2
4
]
that con
s
ist
s
of the price
sensitiv
ity of th
e users, price sensitivit
y of
the cla
ss a
n
d
also the ba
se
price of th
e
cla
s
s into t
he o
b
jective
functio
n
an
d con
s
traint
functio
n
s.
This
study
u
s
ed
se
con
dary d
a
t
a obtained from one of th
e local
se
rver in Palemba
n
g
. The data u
s
ed
con
s
i
s
t o
f
the data traffi
c of mail an
d
traffic of digil
i
b.
The mod
e
l
will then be
solved u
s
in
g
LINGO
11.0 to
obtain the opt
imal solutio
n
.
3. Model
The main o
b
j
ective of this re
se
arch i
s
to obtain t
he maximum
benefit for servi
c
e
providers. The approach used i
s
by utilizing the m
a
thematical m
o
deling approach. The model is
formed by gat
herin
g for info
rmation o
n
the para
m
eters and variabl
e
s
.
So the objecti
ve function:
Max
∑∑
log
(1)
Mean
s that
p
r
ovide
r
want
s to maximi
ze
the total
am
ount
comp
ri
ses th
e
co
st to conn
ect to
the
available Qo
S (
), chan
ge
s in the co
st of all the cha
nge
s in QoS (
), the utility function
measuri
ng th
e deg
ree
of sati
sfactio
n
of
the users,
the ba
se p
r
i
c
e f
o
r e
a
ch cl
ass
j
and
de
cisi
o
n
wheth
e
r the
u
s
ers
i
in
admi
tted in cla
s
s
j
or not. We a
l
so h
a
ve the
sets
of co
nstrains
as th
at a
c
t
as a ba
rri
er functio
n
s of th
e obje
c
tive to be sa
tisfie
d in the aim of obtaining o
p
timal re
sults.
The first
con
s
traint
states that a chan
ge f
ee dep
e
nds o
n
co
st
factors involv
ing ea
ch
attribute QoS
band
width, end-to
-en
d
del
ay, and BER,
the base
cost with the use
r
i and j cla
s
s,
as well as linearity factors.
By collecting a
ll informatio
n obtaine
d followin
g
co
nstraints.
1
(2)
With
as a no
minal value o
f
QoS attribute in the operator network.
The maximu
m value for
band
width is
2Mbp
s, for end-to
-en
d
del
ay 350kb
p
s, and for BER
10
dependi
ng o
n
the
type of traffic
[13].
a basic fee for a conne
ction wit
h
the user
i
and cla
s
s
j
, and
is a
linearity facto
r
.
is defined a
s
:
100
⁄
(3)
Whe
r
e
is defined as line
a
r price facto
r
in use
r
i
and class
j
, linear factor
, and
is
traffic load.
is a linearity factor d
epe
ndi
ng on pa
ram
e
ter
and
th
en:
(4)
With the assu
mption
0
1
.
Linea
rity factor
lies betwe
en the pre
s
rib
ed value fixed by the provider, say
and
s
o
:
(5)
Allowabl
e Tra
ffic load ran
g
e
is also d
e
termin
ed by provider, say
and
, then:
(6)
Whe
r
e
is the increment
of decreme
nt of QoS val
ue that is fixed to be 0 and 1 to implici
t
ly
states that 0 i
s
in be
st effort conditi
on a
n
d
1 is a perfe
ct se
rvice
con
d
ition.
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
1693-6
930
Optimiz
a
tion of Wireless
Internet Pric
ing Sc
hem
e in Servi
ng Multi
QoS Net
w
ork… (Irm
eilyan
a)
275
Value
is set
up between
0.
8
and
1.07
, since in
this rang
e, the be
st quality of service
occurs . Valu
e
is a determ
i
ned linea
r pa
ramete
r with f
a
ctor
sho
w
s
the basi
c
leve
l of price.
0x1
(7)
0.8
1
.
07
(8)
1
(9)
Next constrai
nt will be:
∑∑
,
1,
…
,
(10
)
Whe
r
e
is
bandwith
of 10
0
M
Bps.
1
,
1
,…,
;
1
,
2
,
…
(11
)
1
,
1
,…,
;
1
,
2
,
…
(12
)
explains the
use
r
’s
i
sensitivity price in class
j
.
1
,
1
,…,
;
1
,
2
,
…
(13
)
Whe
r
e
is minimum ba
nd
width for e
a
ch use
r
with
6
Kbps for u
s
er
1 and
5
Kbps for
use
r
2.
1
,
1
,…,
;
1
,
2
,
…
(14
)
,
1,
…
,
;
1
,
2
,
…
(15
)
0
,
1,
…
,
;
1
,
2
,
…
(16
)
0
.
0
1
,
1
,2
,…
(17
)
0
,
1,
2,
…
(18
)
,
1
,…,
;
1
,
2
,
…
(19
)
1,ifus
er
isadm
i
tt
ed
t
o
clas
s
0,
ot
herwise
(20
)
4. Results a
nd Discu
ssi
on
For o
b
je
ctive function
(1)
with subje
c
t to co
nstraints
(2)-(20), the
optimal soluti
on for
4
ca
se
s whe
r
e
the QoS
attributes i
n
volvin
g increm
ent
or d
e
crem
ent
of pri
c
e
due
to QoS
cha
n
g
e
and in
cre
m
en
t or decreme
nt of QoS value is solved u
s
ing LI
NGO 1
1
.0.
4.1. QoS
Attribute:
Ba
ndwith
Table 1 an
d Table 2 de
pi
ct the solver
stat
us for e
a
c
h 4 cases a
nd de
cisio
n
variabl
e
value, r
e
spectively.
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 16
93-6
930
TELKOM
NIKA
Vol. 14, No. 1, March 2
016 : 273 – 2
7
9
276
Tabel 1. Solver Status of Mode
l Nonlinie
r Progra
mming M
odel of Wirele
ss Pri
c
in
g Scheme for Q
o
S Attribute: Band
width
Variables
increase
increase
increase
decrease
decrease
increase
decrease
decrease
Model Class
INLP
INLP
INLP
INLP
State
Local Optimal
Local Optima
l
Local Optimal
Local Optimal
Objective 508632
508628
503863
503863
Infeasibility
0
0
0
3.63798
∙
1
0
Iterations 31
32
28
28
GMU
35K
35K
35K
35K
ER 0s
0s
0s
0s
Table 1 di
spl
a
ys the optim
al solution fo
r the bandwi
d
th QoS attribu
t
es of the four ca
se
s.
The valu
e of
the optimal
solutio
n
can
be viewed o
n
obje
c
tive row, which fo
r QoS
ban
d
w
idth
available i
n
four
cases,
the value
will achieve the m
o
st optim
al
results in the fi
rst case which is
5086
32. Th
e
s
e
re
sults wil
l
be o
b
taine
d
by pe
rformi
n
g
iteratio
ns 3
1
times the i
n
feasi
b
ility of 0.
Gene
rated M
e
mory Use
d
(GMU) sh
ows the amount
of used m
e
m
o
ry allo
cation
that is equal
to
35K and Elap
sed
Runtim
e (ER)
sh
ows the total time
use
d
to pro
d
u
ce a
nd finish the model t
hat
is 0 se
co
nd
s.
Furthe
rmo
r
e, Table 2
sho
w
s
the deci
s
io
n
vari
abl
es fo
r 2 u
s
e
r
s an
d
2 cla
s
se
s. Chang
es
in co
sts
due
to cha
nge
s
in QoS for
e
a
ch
ca
se
do
not se
em to app
roa
c
h t
he same val
ue.
Becau
s
e
for
ca
se
s 1
and
2, the chan
g
e
fee
will be
clo
s
e to
1, while in th
e ca
se of
3 an
d
4,
cha
nge
s in the co
st of even app
roa
c
hi
ng 0.1. In
addition, for ca
se
s 1 and 2, the increme
n
t
or
decrem
ent in
the value of QoS is 1 that
sho
w
s t
he
service
s
a
r
e in
perfect
condi
tion, as well
as
for cases 3
and
4, the in
cre
m
ent
or
d
e
creme
n
t in t
he value
of
QoS is 0
whi
c
h i
ndicates
the
servi
c
e is in a
state of best effort.
Tabel 2. De
ci
sion Va
riable
Values of Mo
del No
nlinie
r Programmin
g
Model of Wireless Pri
c
ing
Schem
e for
QoS Attribute
:
Bandwidth
Variables
increase
increase
increase
decrease
decrease
increase
decrease
decrease
PQ
11
1.218333
1.217116
0.07381231
0.07381231
PQ
12
1.137111
1.135975
0.08857477
0.08857477
PQ
21
1.055889
1.054834
0.1033372
0.1033372
PQ
22
0.9746667
0.9736925
0.1180997
0.1180997
x
1 1
0
0
PB
11
0.5126671
0.5126671
0.04295705
0.04295705
PB
12
0.4784893
0.4784893
0.05154845
0.05154845
PB
21
0.4443115
0.4443115
0.06013986
0.06013986
PB
22
0.4101337
0.4101337
0.06873127
0.06873127
a
11
0.15
0.15
0.05
0.05
a
12
0.14
0.14
0.06
0.06
a
21
0.13
0.13
0.07
0.07
a
22
0.12
0.12
0.08
0.08
B
1.07 1.07
0.8
0.8
4.2. Atribu
t
QoS
end-to-end d
e
lay
Table 3 and
Table 4 displ
a
y the solver stat
us and
variable de
ci
sion value
s
for ea
ch
ca
se.
Tabel 3. Solver Status of Model Nonlini
e
r Prog
ram
m
i
ng Model of
Wirel
e
ss Pri
c
i
ng Sche
me for
QoS Attribute
:
End-to-End
Delay
Variables
increase
increase
increase
decrease
decrease
increase
decrease
decrease
Model Class
INLP
INLP
INLP
INLP
State
Local Optimal
Local Optima
l
Local Optimal
Local Optimal
Objective 508643
508618
503863
503863
Infeasibility
2.22045
∙
1
0
1.09139
∙
1
0
0 0
Iterations 31
32
28
28
GMU
35K
35K
35K
35K
ER 0s
0s
0s
0s
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
1693-6
930
Optimiz
a
tion of Wireless
Internet Pric
ing Sc
hem
e in Servi
ng Multi
QoS Net
w
ork… (Irm
eilyan
a)
277
Based o
n
Ta
ble 3, it can be se
en that
the optim
al solutio
n
occu
rs in the first case of
5086
43
with
numbe
r of ite
r
ation
of 31
iteration
s
. Fo
r ea
ch
ca
se,
the memo
ry
allocation lo
o
k
s
the sa
me val
ue in th
e am
ount of 3
5
K a
nd ER
of 0
s
. From
Table
4
sho
w
s that i
n
ca
se
s
1 an
d 2
the cost
chan
ges will
be
cl
ose
to
1, whil
e for cases 2
and
3
the
co
st
chan
ge
s in
will
be
cl
ose
to
0.1.
Tabel 4. De
ci
sion Va
riable
Values of Mo
del No
nlinie
r Programmin
g
Model of Wireless Pri
c
ing
Schem
e for
QoS Attribute
:
End-to-End
Delay
Variables
increase
increase
increase
decrease
decrease
increase
decrease
decrease
PQ
11
1.221204
1.214245
0.07381231
0.07381231
PQ
12
1.139790
1.133296
0.08857477
0.08857477
PQ
21
1.058377
1.052346
0.1033372
0.1033372
PQ
22
0.9769630
0.9713962
0.1180997
0.1180997
x
1 1
0
0
PB
11
0.5126671
0.5126671
0.04295705
0.04295705
PB
12
0.4784893
0.4784893
0.05154845
0.05154845
PB
21
0.4443115
0.4443115
0.06013986
0.06013986
PB
22
0.4101337
0.4101337
0.06873127
0.06873127
a
11
0.15
0.15
0.05
0.05
a
12
0.14
0.14
0.06
0.06
a
21
0.13
0.13
0.07
0.07
a
22
0.12
0.12
0.08
0.08
B
1.07 1.07
0.8
0.8
4.3. Atribu
t
QoS
BER
The solver st
atus an
d vari
able de
ci
sion
values for e
a
ch
ca
se a
r
e
pre
s
ented in
Table 5
and Tabl
e 6 for Bit Rate Error
(BER) Q
o
S Attribute.
Tabel 5. Solver Status of Model Nonlini
e
r Prog
ram
m
i
ng Model of
Wirel
e
ss Pri
c
i
ng Sche
me for
QoS Attribute: BER
Variables
increase
increase
increase
decrease
decrease
increase
decrease
decrease
Model Class
INLP
INLP
INLP
INLP
State
Local Optimal
Local Optima
l
Local Optimal
Local Optimal
O
b
jective
4.38386
∙
1
0
506541
503863
504246
Infeasibility
7.27596
∙
1
0
0
8∙
1
0
0
Iterations 26
35
40
28
GMU
35K
35K
35K
35K
ER 0s
0s
1s
0s
For BER Q
o
S Attribute, optimal re
sul
t
s lies in
the
first ca
se li
ke state
d
in
Table 5.
Ho
wever, it appea
rs that the re
sults ob
tained to
be much la
rge
r
than the existi
ng three
ca
ses
whi
c
h is e
qua
l to
4
.
38386
∙
1
0
with 26 iteration
s
.
Cha
nge
s in
co
sts du
e to chan
ge
s in QoS for ea
ch ca
se seem
to be appro
a
chi
ng a
different valu
e altogethe
r a
s
Tabl
e 6 exp
l
ained. Fo
r th
e ca
se of 1, the co
st chan
ges
will be
cl
ose
to 1, in the case
of two
ch
ange
s in the
co
st will
be
cl
ose to
0.5, in
the ca
se
of three
ch
ang
es in
the cost will be close to 0:07, while
in case 4 cost changes is at 0.
If we examin
e from
Table
1, Table
3 a
n
d
Tabl
e 5,
we ca
n
comp
a
r
e the
3 Q
o
S attribute
according to
each ca
se. In
the first ca
se
it appear
s th
at the optimal
solution lie
s
in QoS BER is
equal
to 4.3
8
386x10
7
,
with
the fe
we
st it
eration
s
of 2
6
iteratio
ns. In
the second
case, th
e
opti
m
a
l
solutio
n
inste
ad lies in th
e QoS ban
dwi
d
th that is equ
al to 5086
28,
with 32 iterations
whe
r
e th
e
numbe
r of it
eration
s
. In t
he third case
the optimal
solutio
n
from
the same th
ird Q
o
S that
is
5038
63, but the least iterat
ion sho
w
n in
band
width
an
d end-to
-en
d
delay of 28 iteration
s
. For
the
fourth
ca
se t
he optimal
solution i
s
the
for ba
nd
widt
h and
end
-to
-
end
delay in
the amo
unt
of
5038
63, wh
ere the optimal solutio
n
of bo
th Qo
S can b
e
obtaine
d by 28 iteration
s
.4.3838
6x10
7
.
Also, if we a
nalyze from
previou
s
research
d
one
by [13], our results
sho
w
more o
n
variou
s valu
e
s
of
facto
r
of
accepta
n
ce a
nd
co
st
fa
ctors affe
cted
the
mod
e
ls. By i
n
creme
n
ting
or
decrem
enting
x
a
s
the Q
o
S value, we
obtain the
be
st
optimal val
ue of chargin
g
the wi
rele
ss
netwo
rk. Aga
i
n, by examining t
he re
search cond
u
c
ting by [22]
for pricing the services in
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 16
93-6
930
TELKOM
NIKA
Vol. 14, No. 1, March 2
016 : 273 – 2
7
9
278
differentiated
netwo
rk wh
ere th
e au
cti
on meth
od u
s
ed to
solve
the optimi
z
ati
on p
r
obl
em,
the
probl
em a
c
tu
ally only invo
lves ba
nd
wid
t
h as th
e Q
o
S attribute
s
, so the
pe
rformance of oth
e
r
attributes
are
not clea
rly shown. With o
u
r result
s, other Q
o
S attri
butes
are
also sh
own
and
in
fact, BER sho
w
n better
re
sults than othe
r attribute
s
.
Tabel 6. De
ci
sion Va
riable
Values of Mo
del No
nlinie
r Programmin
g
Model of Wireless Pri
c
ing
Schem
e for
QoS Attribute
:
BER
Variables
increase
increase
increase
decrease
decrease
increase
decrease
decrease
PQ
11
1.217725
∙
1
0
0.6372512
0.07381230
0
PQ
12
1.136543
∙
1
0
0.5947678
0.08857477
0
PQ
21
1.055361
∙
1
0
0.5522844
0.1033372
0
PQ
22
0.9741797
∙
1
0
0.5098010
0.1180997
0
x
1 0
0
0.1
∙
10
PB
11
0.5126671
0.3708654
0.04295704
0.04295705
PB
12
0.4784893
0.3461410
0.05154845
0.05154846
PB
21
0.4443115
0.3214166
0.06013986
0.06013987
PB
22
0.4101337
0.2966923
0.06873127
0.06873128
a
11
0.15
0.15
0.05
0.05
a
12
0.14
0.14
0.06
0.06
a
21
0.13
0.13
0.07
0.07
a
22
0.12
0.12
0.08
0.08
B
1.07 0.8
0.8
0.8
5. Conclusio
n
There a
r
e th
ree
attribute
s
of QoS
in t
h
is
discu
s
sio
n
, namely
ba
ndwi
d
th, en
d
-
to-e
nd
delay, and B
E
R wh
ere e
a
ch attri
bute
has 4
ca
se
s. The optim
al solutio
n
of the three Q
o
S
indicates that
the result
s will be optimal if it is
the first case where i
n
case of increment of
and in
cre
m
en
t of
when
we
have Bit Error Rate attrib
ute.
Ackn
o
w
l
e
dg
ements
The re
se
arch
leading to th
is study
wa
s financ
i
a
lly su
pporte
d by Directo
r
ate of
High
er
Educatio
n Ind
one
sia (DIKTI) for su
ppo
rt throu
gh “Hiba
h
BersaingT
a
hun I”, 2015.
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TELKOM
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1693-6
930
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a
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Internet Pric
ing Sc
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QoS Net
w
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eilyan
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