TELKOM
NIKA Indonesia
n
Journal of
Electrical En
gineering
Vol. 12, No. 8, August 201
4, pp. 6369 ~ 6379
DOI: 10.115
9
1
/telkomni
ka.
v
12i8.591
2
6369
Re
cei
v
ed
No
vem
ber 2
1
, 2013; Re
vi
sed
Febr
uary 18,
2014; Accept
ed March 6, 2
014
Dynamic Virtual Programming Optimizing the Risk on
Operating System
Prashan
t Ku
mar Patra*
1
, Padma Loch
a
n Pradhan
2
1
Dept. of CSE,
Coll
eg
e of Engi
neer
ing & T
e
chnol
og
y, BPUT
,
Bhub
an
es
w
a
r-
751
00
3, Orissa, India
2
Dept. of CSE,
Centra
l Institute
of
T
e
chnol
og
y, Rai
pur, CG, India
*Corres
p
o
ndi
n
g
author, e-ma
i
l
: citrprcs@redi
ffmail.com.
A
b
st
r
a
ct
T
h
is virtual pr
ogra
m
mi
ng l
a
ngu
ag
e is a pri
m
ar
y
metho
d
of impr
ovi
n
g the perfor
m
ance a
n
d
protectin
g
system res
ourc
e
s i
n
arou
nd the c
l
ock on a
n
yw
he
re and
anyti
me
platfor
m
. This prop
osed
articl
e
is contrib
u
tes to the dev
elo
p
m
e
n
t of an opti
m
a
l
metho
d
that aims to d
e
termine th
e min
i
mal cost an
d ti
me
to be i
n
veste
d
into sec
u
re
an
d rob
u
st co
mp
uting
in
ri
ght w
a
y, right ti
me.
ever
yw
here
an
d everyti
m
e of
th
e
glo
be. F
u
rth
e
r
m
or
e, the
virtu
a
l
progr
a
m
min
g
meth
od
o
p
t
im
i
z
in
g th
e (C
PU
, MEMOR
Y
)
sp
a
c
e & tim
e
of th
e
oper
ating
system. Th
is dy
na
mic
cod
e
ful
l
y
supp
ort to th
e
current virtu
a
li
z
a
ti
on t
o
co-
o
p
w
i
th larg
e sca
l
e
compl
e
x infras
tructure and a
pplic
atio
ns. W
e
have to
defi
ne, desi
gn, de
velo
p
& depl
o
y
me
nt the virtual
progr
a
m
min
g
(C++, JAVA
&
RAIN) on real t
i
me oper
atin
g system to parti
cipates t
he
mil
l
i
ons of users a
b
le
to access o
n
l
i
ne w
eb
and
mobil
e
a
p
p
licati
o
n si
mu
ltane
ou
sly. W
e
have t
o
inte
grate th
is
VPL w
i
th rea
l
time
hardw
are, softw
are and mid
d
lew
a
re to fa
cilitate to
cus
t
omers re
qu
irement. This dyna
mic code
is
communic
a
tin
g
,
interfacing,
messag
e
passi
n
g
, replic
at
ing a
m
o
ng the sev
e
ral subj
ects an
d obj
ects over a
distributed s
u
per scalar
env
ironm
e
nt (MIMD). This pr
opos
ed
method
opt
i
mi
z
ed the sys
tem
attacks
and
dow
n time by i
m
p
l
e
m
e
n
tin
g
VPL on co
mp
lex
heterog
on
ous
infrastructure.
Ke
y
w
ords
:
vir
t
ual
pr
ogra
mming lan
g
u
age, java enter
prise
edi
ti
on, multi
p
le
i
n
struction o
n
multi
p
le dat
a,
risk m
i
tigation, ROI: return on invest
m
e
nt
Copy
right
©
2014 In
stitu
t
e o
f
Ad
van
ced
En
g
i
n
eerin
g and
Scien
ce. All
rig
h
t
s reser
ve
d
.
1. Introduc
tion
The hig
h
level pro
g
ram
m
i
ng lang
uag
e is a set of instru
ction
s
, co
mmand
s, scri
pts an
d
other
sym
bol
s &
syntaxes use to
write
the ap
plicatio
n software p
a
ck
ages for
specific scientifi
c
and co
mme
rcial pu
rpo
s
e.
The prog
ra
mming lang
u
age
s that the develope
r us
e to write source
code to
solve for
specific
problem is call
ed
high-l
evel languages (Ada,
BASIC, COBOL, REXX
,
C, C++, JAV
A
). The
s
e
code
s c
an
be
tran
slated, i
n
terp
reted
a
nd compil
ed
into a lo
w-l
e
vel
langu
age,
wh
ich i
s
recogni
zed
dire
ctly b
y
the com
put
er h
a
rd
wa
re
as
ze
ro a
nd o
ne (i.e; lo
w le
vel
langu
age
s).
The
high
-leve
l
lang
uag
es a
r
e
de
signe
d
t
o
be
e
a
sy to
writea
ble,
rea
dable,
relia
bl
e
(WRR), avail
able, ro
bu
st, sc
alabl
e and
unde
rsta
nda
b
l
e by huma
n
being. Th
e progra
mme
rs t
o
write pe
su
dcode, so
urce
code u
s
ing
syntax, logical symb
ol and just like
English wo
rds,
gramm
a
r
as
per flo
w
cha
r
t and alg
o
rith
m (spe
cifi
cati
on). Fo
r exa
m
ple, the
co
ntrol
stateme
n
ts
and
re
se
rved
English
words li
ke
go to,
for lo
op,
d
o
while,
if
then else, co
ntinu
e
an
d
b
r
ea
k
are
use
d
in mo
st
major
pro
g
ra
mming lan
g
u
age
s to const
r
uct the
programmed to
sol
v
e our p
u
rp
o
s
e.
The l
ogi
cal
operators
an
d symb
ols (&
&, | |, ++,
<,
>, ==
and !=)
a
r
e
comm
o
n
syntaxe
s
a
r
e
available in
al
l most all hi
g
h
level progra
mming
lan
g
u
age
s a
s
avail
able on to
day
on concurre
nt,
parall
e
l and
distrib
u
ted en
vironme
n
t. There a
r
e
ma
ny high-level
langu
age
s are similar e
n
o
ugh
that pro
g
ram
m
ers
can
ea
sily un
dersta
nd
sou
r
ce
code
written
i
n
multiple
la
ngua
ge
s li
ke
C,
REXX, PASCAL, Ada, COBOL, C++
,
J
a
va
& C# for multiple purpose [1-2].
1.1. Existing High Lev
e
l L
a
nguag
es [1
]
No
w a
day, there
are ma
n
y
prog
rammi
n
g
lang
uag
es are available
and
m
a
ssivel
y
used
in
both
comm
e
r
cial
& scie
n
t
ific appli
c
ati
ons are
u
s
in
g in p
a
rallel,
distri
buted
and
co
ncu
r
rent
operating
sy
stem. Th
e traditional
pro
g
rammi
ng la
ngua
ge
s is
a
n
ea
rly stag
e of conventi
onal
program
m
ing languages are (C, PASCAL, COBOL,
F
O
RT
RAN, Ada & REXX) commonly known
as procedu
re
oriented p
r
o
g
rammi
ng (P
OP). In t
he proce
dure orie
nted app
roa
c
h, the proble
m
is
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 23
02-4
046
TELKOM
NI
KA
Vol. 12, No. 8, August 2014: 636
9 –
6379
6370
viewed
as
a
seq
uen
ce
of thing
s
to be
d
one
su
ch a
s
readin
g
, cal
c
u
l
ating an
d pri
n
ting. The P
O
P
is
p
r
ima
r
y focus on a functi
on. A typical
prog
ram
stru
cture of proce
dural p
r
og
ra
mming is sho
w
in
Figure 1.
Figure 1. Architecture of POP
The te
chni
qu
e of hi
era
r
chi
c
al
de
comp
o
s
ition
ha
s be
en u
s
e
to
sp
ecific the ta
sk to th
e
compl
e
ted for solving p
r
og
ram. The procedure ori
ente
d
pro
g
ra
mmi
ng ba
sically consi
s
t of writi
ng
a list of in
stru
ction
(or a
c
tio
n
s) for th
e
co
mputer
to
foll
ow a
nd
org
a
n
i
zing th
ese i
n
stru
ction
s
i
n
to
grou
ps
kn
ow
has fu
nctio
n
. We n
o
rm
ally use
a fl
ow
ch
art to organi
ze thus
actio
n
and rep
r
e
s
en
t
the follow of control from
one a
c
tion to
another
. Wh
ile we con
c
e
n
trate on the
developme
n
t of
function ve
ry little attention is given to t
he data
th
at are b
e
ing
used by vario
u
s function
s. Wha
t
happ
en
s to t
he d
a
ta?
Ho
w a
r
e
they a
ffected by
th
e fun
c
tion th
at wo
rks on
them? In
a
multi
function
p
r
og
ram, many
im
portant
data
i
t
ems
pla
c
ed
as glob
al so
that
they
m
a
y be
a
c
ce
ssed by
all the fun
c
tio
n
s. Ea
ch fu
nction may hav
e it’s o
w
n l
o
cal data. Fi
gure 2
s
h
ow
th
e
r
e
la
tio
n
s
h
ip
s
o
f
data and fun
c
tions in a pro
c
ed
ure o
r
ie
nted pro
g
ram.
Figure 2. Rel
a
tion betwee
n
Data & Fun
c
tion
The gl
obal
d
a
ta are m
o
re
vulnerable
to a i
nadve
r
te
nt ch
ang
e by
a fun
c
tion
in
a la
rge
prog
ram
it is
very difficult t
o
ide
n
tify wh
at data
i
s
use
d
by
whi
c
h fu
nction. In
this ca
se,
we
ne
ed
to revise an
external
data
stru
cture
we
also
nee
d to
revise all fu
nction
acce
ss the
data. T
h
is
provide
s
a
n
oppo
rtunity for bu
gs to creep in to
th
e
cod
e
s. An
other
se
riou
s d
r
awba
ck
with
the
pro
c
ed
ural a
ppro
a
ch is th
at it does not
model
re
al worl
d proble
m
s very well.
This is b
e
ca
use
function i
s
act
i
on-o
r
ie
nted d
o
not really corres
pon
ding
to the elemen
ts of the probl
em [1- 2].
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
2302-4
046
Dynam
ic Virtual Prog
ram
m
i
ng Optim
i
zing the Ri
sk o
n
Operating
…
(Pra
sha
n
t Kum
a
r Patra)
6371
1.2.
Char
acteristi
cs of POP [1
]
a)
Empha
sis i
s
on functio
n
a
nd algo
rithm
s
, but neither data nor o
b
je
ct.
b)
Larg
e
progra
m
s are divide
d in two
small
e
r program
s kno
w
n a
s
fun
c
tion
s.
c)
Most of the function
sha
r
e
global d
a
ta.
d)
Data move o
n
ly aroun
d the system fro
m
function to
function.
e)
Functio
n
tran
sform
s
data from one from
to another.
f)
Employs top-down app
ro
a
c
h in prog
ram
desig
n.
1.3.
Adv
a
tntage
of Func
tion in OOP Lang
uages
(Mes
s
a
ge orien
t
ed
Languag
e
)
[8]
a)
Functio
n
m
a
ke
s the
len
g
t
hy and
co
m
p
lex
prog
ram
ea
sy an
d i
n
short
form
s. It
mean
s
large prog
ram can be
su
b-divid
e
d
into
self-co
n
tained
a
nd conve
n
ient
small
module
s
havi
ng uniq
ue na
me.
b)
The length o
f
source p
r
o
g
ram
can be
r
edu
ced by
using fun
c
tion by using
it at
different pla
c
e in the prog
ram acco
rdin
g
to the user’
s
requi
rem
ent.
c)
By using function m
e
mory
space can be
properly
utiliz
ed. Also less memory is
requi
re
d to ru
n prog
ram if functio
n
is u
s
e
d
.
d)
A function ca
n be used by many pro
g
ra
ms.
e)
Functio
n
in
crea
se
s the
executio
n
speed
of th
e p
r
og
ram
and m
a
kes the
prog
ram
m
ing
simple.
f)
By using the function portabilit
y of the program is very
easy.
g)
It removes th
e red
unda
ncy (occurre
nce of
duplication of pro
g
ra
ms
) i.e. avoi
ds th
e
repetition a
n
d
saves the ti
me and spa
c
e.
h)
Deb
uggin
g
(removing e
rro
r) be
co
mes v
e
ry
ea
sier a
n
d
fast usin
g the functio
n
sub
prog
ram
m
ing
.
i)
Functio
n
s a
r
e
more flexible
then libra
ry functio
n
.
j)
Testing
(verifi
c
ation a
nd va
lidati
on) i
s
very easy by usi
ng functio
n
.
k)
User can
buil
d
custo
m
ized
library
of de
ferent fu
nctio
n
u
s
e
in
dail
y
routin
e h
a
ving
spe
c
ific g
oal
and lin
k with the many prog
ram
simil
a
r to
the library fu
nction.[1], [8].
Figure 3. Co
mmuni
cation
among
Data
& Function in
OOP
2. Existing Opera
t
ing Sy
stem
As per Flynn’
s cla
s
sificatio
n
of compute
r
archite
c
tu
re in 1972 (l
ayeri
ng tech
nolog
y), The
layering
ap
proach of h
a
rd
ware &
software i
s
defined
as foll
ows: T
h
is i
s
the
trad
itional ha
rd
ware
,
softwa
r
e
an
d
layeri
ng architecture avai
lable as
on
t
oday, but
we
are m
o
ving
to virtuali
z
ati
o
n
worl
d to more
optimize ou
r
hard
w
a
r
e, so
ftware, resourc
e
cos
t
& time [5].
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 23
02-4
046
TELKOM
NI
KA
Vol. 12, No. 8, August 2014: 636
9 –
6379
6372
Figure 4. Architecture of O
S
The
con
c
u
r
re
nt pro
g
ra
mmi
ng involve
s
the notat
io
ns f
o
r exp
r
e
ssi
ng
potential p
a
rallelism,
so th
at o
perations may
b
e
exe
c
uted
i
n
pa
rall
el
a
n
d
the techniq
ues for solving the resulting
synchro
n
ization and com
m
unication p
r
oble
m
s (p
ro
ducers & co
nsum
ers). T
he con
c
u
r
re
nt,
distrib
u
ted an
d parall
e
l op
erati
ng
syste
m
have been
used at vari
ous time
s to descri
be vari
ous
types
of
conc
urrent programming (C++
,J
AVA &
RAIN). There are many
more multiple
pro
c
e
s
sors, sha
r
ed m
e
m
o
ry, sha
r
e
cach
es
st
ore to implement
ation co
ncep
ts and a
r
e
not
importa
nt from the prog
ra
mming lan
g
u
age poi
nts of view at all the time [5, 7].
2.1. Existing Programming Langua
ge
s & Da
ta Collection
The
high leve
l programmin
g
langua
ge
is a compute
r
langu
age, the
prog
ramme
rs use to
develop a
ppli
c
ation
s
,
com
m
and
s, script
s, and othe
r se
t of instru
ctions fo
r a com
puter to exe
c
ute
for
spe
c
ific
purp
o
se. We
have to
fin
d
out th
e
se
veral diffe
ren
t
prog
ram
m
i
ng a
nd
scrip
t
ing
langu
age
s
cu
rre
ntly listed
i
n
ou
r
databa
se
coll
ecti
o
n
and su
rvey
fo
r
the
spe
c
ific requi
rem
ents as
per custom
er’s
requi
rem
e
nt in m
u
lti-co
mputer
environment
for
multiple-
purp
o
se
is defin
e
d
on
Table 2. The
r
e are several prog
ram
m
ing
langua
ge
s list out in categ
o
ry wise as o
n
today.
Table 1. Data Collec
t
ion [3-5], [7],
[10]
SN
Heteroge
neous
Oper
ating
S
y
stem (MIMD
)
DESRIPTI
O
N
S
CATEGOR
Y
TY
PES O
F
LANG
UA
G
ES
(POP, FP
L, PPL,
CCP, OOP)
PURPOSE
01
UNIX, Win, NT,
Linux
APPLICATIONS AND
PROG
RAMS
DEVELO
P
MENT
C, C++, JAVA, C#, PERL
Commercial & Scientific
02
UNIX, Win, NT,
Linux
ARTIFIC
I
AL
INTELLI
GENCE
DEVELO
P
MENT
LOGIC ( P
r
olog )
,
LISP,
Haskell, ML
Mathematical & Scientific
03
UNIX, Win, NT,
Linux
DATABASE
DEVELO
P
MENT
Dbase, Fo
x pro
,
SQL, M
y
SQL, S
y
base, Or
acle,
Ingress.
Business & Commercial, ERP
04
UNIX, Win, NT,
Linux
G
A
ME DEVEL
OPMENT
C, C++, JAVA, RUB
Y
,
Ant
Entertainment
05
UNIX, Win, NT,
Linux
DEVICE DRIVE
R
( HARDWARE
)
C, C++, JAVA, C#, PERL
Mathematical, Scientific &
Benchmarking,
06
UNIX, Win, NT,
Linux
INTERFA
C
E
DEVELO
P
MENT
C, C++, JAVA, C#, PERL
Mathematical, Scientific &
Benchmarking,
07
UNIX, Win, NT,
Linux
INTENET, I
N
TR
ANET &
WEB DEVELO
P
MENT
DHTML, HTML, JAVA
SCRIPT, PERL,
AWK, SED,
Ant, PHP, VBScript. Python.
Business & Commercial, ERP
&Web Engineering
08
UNIX, Win, NT,
Linux
SCRIPTIN
G
DEVELO
P
MENT
HTML, XML, VB
Script, Perl,
PHP, P
y
thon,
Ru
b
y
, Ant, Pe
rl
Business, T
e
leco
m.
Commercial, ERP, Web &
Mobile Mathemat
ical &
Scientific
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Dynam
ic Virtual Prog
ram
m
i
ng Optim
i
zing the Ri
sk o
n
Operating
…
(Pra
sha
n
t Kum
a
r Patra)
6373
3. Problem Statements
a)
Programmin
g
cost is m
o
re.
b)
Secu
rity, Inte
grity, Scalabil
i
ty problem.
c)
Upd
a
tion & Chang
e mana
g
e
ment proble
m
(Ch
ang
e Manag
ement
).
d)
The co
dificati
on, updatio
n, modificatio
n
chang
e wh
en requireme
nt chang
es a
s
pe
r
algorith
m
ch
a
nge
s times to
time, the cod
i
fication is full
y dependi
ng
on algo
rithm.
e)
Hun
geri
ng of CPU & Memo
ry on POP &
FPL.
f)
Optimize the
cha
nge & inci
dent mana
ge
ment. ( Fault Toleran
c
e )
3.1. Reseacrch Q
u
es
tions:
Wh
y
w
e
need VPL?
a)
To optimize Space & Time
b)
To desi
gn an
d develop int
e
rnet, intra
n
e
t
& extranet prog
rammi
ng (GUI) for
com
p
lex
N-tie
r
archite
c
ture o
n
hete
r
oge
neo
us pl
atform.
c)
To maximize
the perfo
rma
n
ce of CP
U & Memory ( be
nch ma
rking ).
d)
Automatic me
mory mana
ge
ment ( Automatic ga
rba
g
e
colle
ction:
AGC)
e)
Incre
a
si
ng th
e busi
n
e
ss (l
arge volu
me
of
data, Information, data wareho
usi
ng
&
data mining
).
f)
Increasi
ng the millions of us
ers (Web & Mobile computing).
g)
Incre
a
si
ng th
e hacke
rs a
s
well a
s
experi
ences h
a
cke
r
s.
h)
Increasi
ng the hardware &
software capab
ilities (N-th bits pr
ocessor & number of
CPU, Memo
r
y
).
3.2. Rese
arc
h
Objectiv
e
Our proposed Virtual Programmi
ng
will
be great
helpful for E-commerce, E-
govern
e
ss, scien
c
e to scie
nce, pr
odu
ct to prod
uct, bu
sine
ss to busi
ness & so
cie
t
y to society:
a)
Busine
ss co
n
t
inuity plannin
g
& disaste
r
recove
ry plan
ning ( BCP/DRP)
b)
Suppo
rt to internal & external system a
udit. ( CMDB
& ITIL )
c)
Keep the syst
em balan
ce a
m
ong OS, Ne
tw
ork, Application & Vario
u
s types of
dev
ice
s
,
su
b-
sy
st
em
s,
re
s
our
ce
s &
use
r
s ne
ed. ( Fa
ult toleran
c
e )
d)
Improve the secu
rity.
e)
Improve the i
n
teroperability.
f)
Improve the perform
a
nce, benchmarkin
g, fault tolerance, reli
ability & high
availability.
3.3. Objectiv
e of Virtual Programming
The virtual p
r
ogra
mming i
s
the most recent pro
g
ra
m
m
ing an
d different ta
sk fo
r different
environ
ment.
It is therefore impo
rtant t
o
have m
o
d
u
lari
zing
pro
g
ram
s
by
co
nce
r
n
partitio
ned
memory a
r
ea
for both dat
a and fun
c
tio
n
. The obj
ect
is co
nsi
d
e
r
e
d
to be a p
a
rtitioned area
of
comp
uter me
mory that sto
r
es d
a
ta and
set of
opera
t
ion that can
be acce
ss d
a
ta. The virtual
prog
ram
m
ing
empha
si
s is
on data rathe
r
than fun
c
tio
n
& pro
c
ed
ure. The progra
m
s are divide
d
into what i
s
called
obje
c
ts.
The
fun
c
tion, ope
rate
on
d
a
ta of a
n
o
b
je
ct. The
data
are
hidd
en, t
hat
can
not be acce
ss by external f
unction
s.
The object
s
may commu
n
i
cate with ea
ch other thro
u
gh
function
s. Th
e data & function
can
b
e
adde
d,
when bu
sin
e
ss rul
e
ch
an
ges. Th
e virtual
program
m
ing solves the
following problems lik
e: real time
system, simulati
on mo
del, A
I
,
internet, extranet, multimedia,
gamming
& parallel p
r
o
g
rammi
ng [1-2].
The high lev
e
l prog
rammi
ng langu
age
s like C++, C# an
d Java
has be
com
e
largel
y
popul
ar in th
e we
b ba
se
d
appli
c
ation i
n
around
the
wo
rld. The
Java’s b
e
cam
e
highly p
o
p
u
lar
and wi
dely a
c
ceptan
ce
s p
r
og
rammi
ng l
angu
age fo
r in
tranet, inte
rnet and extra
nets, e
s
pe
cia
lly
that can b
e
write o
n
ce a
nd ru
n it anywhe
r
e
(byte cod
e
) a
n
y time on hete
r
o
gene
ou
s platform.
The obje
c
t o
r
iented
Java
langu
age g
r
e
en pap
er in
iti
a
lly develope
d by Sun Micro
-
System i
n
USA in 199
1
,
The Java i
s
a si
mple,
obje
c
t orie
nted, distri
bute
d
, interp
reted
,
robu
st, se
cure,
architectu
re
-n
eutral, po
rta
b
le, scala
b
l
e
, re
sou
r
ce
depe
nde
ncies, hig
h
a
v
ailable, hig
h
-
perfo
rman
ce
& through
put, multithread
e
d
and dyna
mi
c. The obj
ect
oriente
d
prog
rammin
g
JAV
A
interfaci
ng, in
teractin
g a
n
d
syn
c
hroni
zin
g
, com
m
uni
cating, interpretable,
scal
a
ble
with fro
n
tend
(API- VB, Java, java script), middle
w
are (Apa
ch
e, Tomcat, Iplanet, We
b
logic, Jbo
s
e) &
backe
nd Dat
aba
se (O
ra
cle,
My
SQL, Sybase
)
an
d
tightly coupl
e
d
with
Ope
r
at
ing System. I
t
is
fully suppo
rte
d
to servi
c
e o
r
iented a
r
chit
ecture
& prog
rammin
g
(SO
A
& SOP) [2], [4],
[6], [9].
Evaluation Warning : The document was created with Spire.PDF for Python.
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046
TELKOM
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KA
Vol. 12, No. 8, August 2014: 636
9 –
6379
6374
Figure 5. Architecture of Web Po
rtal
5. Proposed
Method
: D
y
namic Vi
rtua
lization Prog
ram (Poly
m
orphism)
We
have
to
define,
de
sign, devel
op
and
d
eploy
ment (D^4
)
this p
r
o
p
o
s
e
virtual
prog
ram
m
ing
(Polymorp
h
i
s
m) for
web
based serv
ices an
d fix up the majors automated sy
stem
config
uratio
n
to maintain
resid
ual ri
sk. Mean
while
, we
h
a
ve
to maintain
the system cont
rol
by
applying
aut
omated
meth
od, mo
del,
mech
ani
sm
(M^3) & to
ols on
ope
ratin
g
sy
stem l
e
vel to
optimize the
risk and max
i
mize the de
cisi
on ma
n
a
g
e
ment as p
e
r
busi
n
e
ss re
quire
ment an
d
availability of resource & technology.
Our VPL
sh
ould b
e
d
e
sign in
su
ch
way, that t
he file
syste
m
, shell
an
d ke
rn
el
automatically protected, det
ected & corrected in
around the clo
ck for millions of users.
We have to i
m
pleme
n
t dynamic VPL to
optimized th
e system atta
cks an
d do
wn time by
impleme
n
ting
VPL me
ch
anism
& au
tomated m
e
mory mg
mt, mean
whil
e imp
r
oving
the
throug
hput of
the File, Me
mory an
d Proce
s
sor &
Ke
rnel
system.
Finally, we h
a
ve to maximize
the performa
n
ce & minimi
ze the co
st of the operatin
g
system. Our
obje
c
tive is that fix up the risk
at highe
st (H,
M & L) level with minimal
co
st and time
.
6. Resear
ch
Method (Ac
t
ion & Rea
c
ti
on Applied to Ne
w
t
on’s
Third La
w
)
The p
o
lymorphism
can b
e
define
d
a
s
one
i
n
terfa
c
e to control
acce
ss t
o
a
gene
ral
cla
s
ses of a
c
tions. T
here
are t
w
o
types of
polymo
r
phism
s
one
i
s
com
p
ile tim
e
polymo
r
p
h
i
s
m
and the othe
r one is ru
n time polymorp
h
ism. The
co
mpile time po
lymorphi
sm i
s
functio
n
s a
n
d
operators ov
erloa
d
ing. T
h
e ru
ntime ti
me polymo
r
p
h
ism i
s
d
one
usin
g in
herit
ance an
d virt
ual
function
s. Th
e polymorphi
sm (different
behavio
rs) mean
s that function
s a
s
sume diffe
re
nt
sha
p
e
s
&
f
o
r
m
s at
dif
f
e
re
nt
t
i
mes.
I
n
c
a
se
of
co
mpil
e time it i
s
ca
lled fun
c
tion
overloa
d
ing.
Th
e
prog
ram
i
s
di
vided into
two fun
c
tion
s, t
he two o
r
mo
re fu
nctio
n
s can h
a
ve
sam
e
na
me
but t
heir
para
m
eters li
st should
be
different eit
her i
n
te
rm
s of paramete
r
s
or th
eir
d
a
ta types
as pe
r
busi
n
e
ss req
u
irem
ent. The function
s
whi
c
h differ only in th
eir retu
rn types
can
not be
overloa
ded.
The
compil
er will sele
ct the right
fun
c
ti
ons
dep
endi
ng on th
e type of pa
ram
e
ters
passe
d. In thi
s
cases of cl
ass con
s
tru
c
t
o
rs c
ould be overloa
ded a
s
the
r
e can
b
e
both
i
n
itialized
and uninitiali
z
ed of the objects. This follo
wing test
leve
l program focusin
g
the working of co
mpil
e
time function
s overlo
adin
g
and co
nstructor overlo
adin
g
.
Example of polymorp
h
ism
as follo
ws:
cla
ss O
peratingsy
s
tem {
void w
h
ic
h()
{
Sys
t
em.out.println("
HP
C.");
}
}
cla
ss Sola
ri
s extends O
peratingsy
s
tem {
void w
h
ic
h()
{
Sy
stem.out.println("Sol
a
ri
s
"
);
}
}
cla
ss AIX extend
s Ope
r
ati
ngsy
s
tem {
void w
h
ic
h()
{
System.out.println("AIX.");
}
}
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TELKOM
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ISSN:
2302-4
046
Dynam
ic Virtual Prog
ram
m
i
ng Optim
i
zing the Ri
sk o
n
Operating
…
(Pra
sha
n
t Kum
a
r Patra)
6375
cla
ss O
peratingsy
s
tem {
publi
c
static v
o
id main
(Stri
ng[] arg
s
) {
Operating
s
ystem ref1 = ne
w Ope
r
ating
system();
Operating
s
ystem ref2 = ne
w Solari
s();
Operating
s
ystem ref3 = ne
w AIX();
ref1.whi
ch
();
ref2.whi
ch
();
ref3.whi
ch
();
}
One of the powe
r
ful tool for usi
ng poly
m
orp
h
ism b
e
havior is to u
s
e the sam
e
method
name
but in
the sa
me
cla
ss, ove
r
a
n
d
over a
gai
n t
o
get (achiev
e
) the
de
sire
d effect
s a
s
we
need
ed. Ho
w can we u
s
e p
o
lymorp
hism in j
a
va
prog
ram to
solve ou
r p
u
rpo
s
e th
at on
e
mentione
d in ben
ch ma
rki
n
g se
ction
s
.
Overloa
ded
Method
s as f
o
llows(Co
m
pi
le Time):
Public cl
ass
Operating
s
ystem extend Solari
s {
Public
void mak
e
P
e
rform() {
Sys
t
em.out.println(“Good!”);
}
Public void m
a
kePe
rform(
Boolean failo
ured
) {
S
y
s
t
em.out.println(“ failoured”);
}
Public void m
a
kePe
rform(
Boolean failo
ured
) {
If ( failoured ) {
S
y
s
t
em.out.println(“ failoured”);
}
}
Overri
dde
n Method
s (Run
Time):
In polymorp
h
i
s
m, we
can
create a m
e
th
od in a supe
r cla
ss
( pa
rent
class), then in a su
b
cla
ss al
so d
e
fine that method.
publi
c
ab
stra
ct cla
ss O
p
e
r
atingsy
s
tem {
public cl
ass
Solari
s exten
d
s Op
eratin
g
s
ystem {
public
void mak
e
P
e
rformed() {
Sys
t
em.out.println(
“Good”);
}
Public void makeP
e
rfo
r
m
ed(Bo
olean f
a
ilover) {
If
failover)
{
Sys
t
em.out.println( “Failover”);
}
}
}
Public cl
ass AIX extends Op
eratin
gsyste
m. Solaris & AIX are su
bcl
a
sses of
Operating
s
ystem beca
u
se they extend Operating
s
ystem.
Dynami
c
Met
hod Bindin
g
( Late Binding
):
Dynami
c
met
hod bin
d
ing i
s
ho
w Java d
e
cid
e
s
what
method to cal
l
when it ha
s to deci
de
betwe
en the
sup
e
r cl
ass
a
nd the su
b cl
ass.
Public stati
c
void main(Stri
ng{} args ) {
Operatings
ys
tem
s
o
laris
=
New S
o
laris
(
);
We h
a
ve to j
u
st ma
de a
Solari
s but d
e
cla
r
ed it
as
an Op
erating
s
ystem,
No
rmally we
c
an do in this
way:
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 23
02-4
046
TELKOM
NI
KA
Vol. 12, No. 8, August 2014: 636
9 –
6379
6376
Public stati
c
void main(Stri
ng{} args ) {
S
o
laris so
laris
= Ne
w S
o
lari
s( );
Ho
wever, we can also d
e
cla
r
e a vari
able by it
s super type or
abstract type
to make it
more
gene
ric way.
Public stati
c
void main(Stri
ng{} args ) {
Operating
s
y
s
tem ope
ratin
g
syste
m
;
}
Public stati
c
void main(Stri
ng{} args ) {
Operatings
y
s
tem s
o
laris
=
new Solaris( );
Operatingsy
s
tem aix = ne
w AIX ( );
ArrayLis
t<
Operatings
ys
tem>
ope
ratingsy
s
tem = ne
w ArrayL
ist(O
perating
s
ystem
>
(
)
operatingsy
s
tems.a
dd(sola
ri
s);
operatingsy
s
tems.a
dd(aix
)
;
}
# Save the prog
ram ovcm
ethod.java (source code fil
e
)
# java ovcmethod.java (cr
eated .cla
ss file internally
)
# Ru
n time
.class file
(ovcm
e
thod.
cla
s
s) byte
cod
e
s
an
y where &
any time o
n
a
hetero
gen
eo
us ope
rating
system.
We have to t
e
st & experi
m
ent on the
s
e java & class file on hete
r
ogen
eou
s ha
rdware &
softwa
r
e fo
r ben
chma
rkin
g purpo
se to
evaluate t
he operating system
perfo
rma
n
ce
(Pro
ce
ssor,
memory
& ti
me). A
c
cordi
ngly the te
st
re
sult,
we
can a
b
le to
v
a
lidate th
e o
peratin
g
syst
em
comp
one
nts
as de
scrib
ed
in belo
w
(Ta
b
l
e 2).
6.1. Work Fl
o
w
Diagr
a
m of the
Rec
e
n
t
Virtual Con
cept
Figure 6
6.2. Propose
d
Benc
h Mar
k
ing of Ope
r
ating Sy
stem
This
re
sea
r
ch pape
r
cont
ribute
s
to the
developm
ent
of scalable
prog
ram
m
ing
metrics
on op
erating
system th
at aims
and
obj
ective to
d
e
termin
e the p
e
rform
a
n
c
e f
a
ctors
at opti
m
al
co
st & time
to be i
n
vested into o
p
timal mo
del
& mechani
sms d
e
ci
ding
on the
me
asu
r
e
comp
one
nt
of
ope
rating
system re
sou
r
ces (i.e.
Pr
oc
e
s
s
o
r, M
e
mo
ry
, Encr
yption
key & KERN
E
L)
.
That’s why we a
r
e
calli
ng
as it
“dyn
ami
c
p
r
o
g
ra
mmin
g
mo
del” for risk an
alysi
s
.
Furthe
rmo
r
e,
the
model & me
chani
sm optim
ize the
co
st, time & resou
r
ce
s i
s
supp
o
s
ed to
red
u
ce the op
erati
n
g
system
risks. We have
to optimize t
he tec
hnology & resource cost and
maximizes t
h
e
perfo
rman
ce
factor (th
r
o
u
g
hput) of OS a
nd bu
sine
ss.
We h
a
ve to
impleme
n
t o
u
r ide
a
ba
se
d on the
assumptio
n
dat
a on B
C
P. Ho
w the
operating
system pe
rform
a
nce
maximi
ze a
s
pe
r
our
busi
n
e
s
s re
q
u
irem
ent. Ou
r obj
ective i
s
that
maximize
s ou
r Busin
e
ss (th
r
oug
hput
) & minimizes o
u
r
Technol
ogy & Reso
urce
s.
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
2302-4
046
Dynam
ic Virtual Prog
ram
m
i
ng Optim
i
zing the Ri
sk o
n
Operating
…
(Pra
sha
n
t Kum
a
r Patra)
6377
6.3. Perform
a
nce Analy
s
is
We can tes
t
the various
virtual programmi
ng s
a
mple lik
e (.J
AVA
& .CLASS)
c
o
mpile
time polymorphism & run t
i
me polymorp
h
ism on di
fferent type of o
peratin
g syst
em. We have
to
analysi
s
h
o
w the Pro
c
e
ssor, Mem
o
ry,
Ca
che
and
o
t
her
sub
co
m
pone
nts a
r
e
workin
g o
n
the
hetero
gen
eo
us platfo
rm. Ho
w is the b
ehaving t
he
h
a
rd
wa
re & so
ftware, when
millions
of users
workin
g sa
m
e
time for sa
me data & informatio
n.
The op
eratin
g system vali
dation an
d ve
rifica
tion fo
r the high
perfo
rman
ce
com
p
uting to
manag
e E-Commerce, E-Payment and
produ
ct lik
e
B2B, B2C,
P2P & G2G for su
perscal
a
r
comp
uting. T
hese syste
m
validation, verifica
tion & b
e
n
chm
a
rkin
g can be d
e
fine i
n
the dynami
c
prog
ram
m
ing
.
We have t
o
maintain th
e risk free e
n
vironm
ents
on the h
a
rd
ware,
softwa
r
e
&
appli
c
ation
(a
lgorithm
) on
b
a
si
s of the fol
l
owin
g
data.
The verifi
cati
on an
d valida
t
ion of ha
rd
ware
& softwa
r
e
o
n
supe
r
scala
r
envi
r
onm
en
t as follo
w to
satisfying
to t
he B2B, B2
C, Web
& Mob
ile
c
o
mputing.
We have fix up
our ot
hers
c
o
ntrol tools like AES, MD
5,
SSH & SSL as
per bus
i
ness
requi
rem
ents.
6.3.1. Theore
t
ical Ben
c
h
m
arking
We have to
make n
e
w id
ea to com
p
a
r
e vario
u
s types of UNIX
operatin
g systems
para
m
eters li
ke p
r
o
c
e
s
sor,
memory, file system,
kern
el on ide
n
tical
Virtual programming of
Ja
va
or C++ pe
rforming on
stan
dard
pro
d
u
c
t like B2B,
B2C, M2M, & P2P. The relative performance
of the syste
m
s on ide
n
tical tasks is m
o
re imp
o
rtant
to us than the ab
solute
best pe
rform
ance
that could b
e
achieve
d
for any individual sy
ste
m
through sy
stem spe
c
ific fine tuning a
nd
perfo
rman
ce
analysi
s
. For
comp
ari
s
o
n
p
u
rpo
s
e ,
w
e h
a
ve only sou
r
ce code avail
able for te
stin
g
of Proce
s
sor,
Memory & Kernel p
a
ram
e
ters, ou
r be
nchm
arkin
g
methodol
ogy is the bla
ck
box
approa
ch av
ailable in
se
q
uential & a
s
well a
s
rand
o
m
ize
way to
determi
ne o
u
r obje
c
tive. We
usu
a
lly attempt to explain cu
riou
s
& intere
stin
g
results thro
ugh continu
ous te
sting
and
ben
chma
rkin
g rathe
r
than
investigatio
ns of Memory, CPU & Kern
e
l
code et
c.
Why
we
nee
d Ben
c
hm
arking
? T
o
ma
ke th
e
syste
m
hig
h
fault
toleran
c
e,
whi
c
h
i
s
s
a
tis
f
ying to
BCP & DRP.
6.4. Propose
d
Opera
t
ing
Sy
stem Scalable Metric
s
The followi
ng
dynamic dat
a is helpi
ng to our pu
rp
ose for ben
chm
a
rki
ng.
We have to u
pdate the
s
e d
a
ta dynamica
lly as per impl
ementation of
J2EE as follo
ws:
Table 2
B
X
X
X
X
X
X
X
Business
J2EE
HA
VLIW
MIMD
K
M M M M M M
M
Kernel
M
HA
VLIW
MIMD
V
Java Java Java Java
Java
A=2
^
n
VPL
J2EE
HA
VLIW
MIMD
E
16 32 64 128
256
512
E=2
^
n
Encr
y
p
tion
J2EE
HA
VLIW
MIMD
P
32
64
128 256 512 1024
P=2
^
n
Processor
J2EE
HA
VLIW
MIMD
M
16 32 64 128
256
512
M=2
^
n
Memor
y
J2EE
HA
VLIW
MIMD
C
L L M
H
H
H
C=2
^
n
Control
J2EE
HA
VLIW
MIMD
Whe
r
e X : Volume of the busin
ess (u
nknown), M:
Kernel Valu
e (u
nkn
o
w
n
), VLIW: Very
larg
e
instructions word, MIMD: M
u
ltiple
Instru
ction on Multip
le Data, HA: High Availabil
i
ty, C: Control
,
RM: Risk Miti
gation. Wh
en
control i
s
hig
h
, then
risk is law as p
e
r F
u
zz’s la
w ap
p
lied in to
ben
chma
rkin
g.
6.5. Practical
Approa
ch for Risk Asse
ssment
We have to verify & validated the real ti
me ope
rating system integrity, high availability,
reliability, scalability of vi
rtual programming lang
uages (polym
orphi
sm
) on heterogeneous
operating
sy
stem platform
an
d we h
a
ve
to st
udy
the
beh
aviors of the
su
b sy
stem
of the
operating
sy
stem like: Shel
l, File, Kernel
, Proces
so
r, Memory
&
En
cryption
key as pe
r
b
u
si
ne
ss
requi
rem
ent
& availability
of tech
nolo
g
y. We
c
an
ap
ply so
me
revi
ew
method
o
n
inte
rnal
UNIX
operating system
for
our b
enchma
r
ki
ng purp
o
se
to
m
i
tigate the risk facto
r
. This benchma
r
ki
n
g
method
can
be a
pplie
d o
n
tra
d
itional
as
well
a
s
Web b
a
sed
ap
plicatio
n
whi
c
h is goi
ng
to
be
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 23
02-4
046
TELKOM
NI
KA
Vol. 12, No. 8, August 2014: 636
9 –
6379
6378
facilitated to
the pe
rforma
nan
ce
analy
s
is, be
nchm
a
r
king, fa
ult tol
e
ran
c
e
a
nd
ri
sk a
s
sessm
e
n
t
over a co
mpl
e
x real time o
peratin
g syst
em.
6.6. Experimental pra
c
tic
e
on benc
h
m
arking: Un
der
SUN
SO
LARS UNIX
Table 3. Verif
i
c
a
tion & Analys
is
[3], [5], [
7
], [10]
SN ACTION
PLAN
(SUBJECT-I
NPU
T
)
DESCRIPTI
O
NS
RISK
ANAL
Y
S
IS
(O
BJECT-O
U
TP
UT)
01
iostat
Input /output stati
s
tics
CPU & Device Utilizat
ion, HA
availabil
i
ty
, Reliability
& integr
ity
of
Processor.
PRIMARY
RISK
ASSESSMENT
02 pmstat
Processors
statistics
Global Statistics
among all the
processors & users : PRIMAR
Y
RISK
ASSESSMENT
03
vmstat
virtual memor
y
st
atistics
[MEMO
R
Y
ACTIVITIES]
Statistics of all th
e processor runa
ble,
block, sw
ap, free
buffer, input/o
ut
put
block device
s
, CPU detail, sy
ste
m
, user,
idle, w
a
iting stage. HA availability
,
Reliability
& integr
ity
of Memory
.
PRIMARY
RISK
ASSESSMENT
04 sar
sy
stem
activities
Activi
t
i
es report o
n
: paging & s
w
ap
ping
of O
S
detail. PRI
M
ARY
RISK
ASSESSMENT
05
ps –ef | gr
ep
ACTIVITIES
OF
PROCESS
OR
The suspious pro
c
essor or orphan
/dead
one. [space & time complexit
y
issue]
SECO
NDA
RY
RI
SK ASSESSME
N
T
06
lsof l more
FILE S
Y
S
T
EM A
C
TIVITIES
list of open files sy
st
em
w
h
ich is ver
y
high risk. SECO
NDARY
RISK
ASSESSMENT
07 /etc/sy
s
tem
KERNEL S
Y
S
T
E
M
ACTIVITIES
Can be upd
ate th
e kernel
PRIMARY
RISK
ASSESSMENT
08 /etc/ssh/sshd_co
n
fig
CKM file sy
stem
Automated Con
t
rol
Cr
y
p
togra
p
h
y
en
able through ssh
implementation AES: 256 bits chi
pper
ssh-key
gen -b
1
024 -f
/etc/ssh_host_key -n
'' chmod
- -
-
/etc/ssh/ssh_con
fig
Preventative control, n=1024, 204
8,
4096
chimed r
w
x
(i. e. 4 2 1 ) –
blank is
nothing
[ H, M, L ]
9
/var/adm/messag
e
Date & time stamp of events
Hard
w
a
re & Soft
w
a
re p
r
oblem an
aly
s
is
7. Results a
nd Analy
s
is
The
su
bject
a
nd o
b
je
ct can
able
to m
a
p
p
ing, inte
grat
e, co
mmuni
cate, syn
c
h
r
on
ize
and
optimize th
ro
ugh thro
ugh
real time op
erating
sy
ste
m
. This virtu
a
l prog
ram
m
i
ng utilities a
nd
appli
c
ation
will b
e
m
o
re me
asura
b
l
e
an
d a
c
co
untable
for
perfo
rman
ce,
fault tole
ra
nce,
throug
hput,
ben
ch
marki
ng a
nd
ri
sk asse
ssme
nt on
any
ap
plicatio
n ove
r
a
complex
IT
infrast
r
u
c
ture.
How is the system behavi
ng, when
milli
ons of users acce
ssi
ng the
same pie
c
e
of
data & inform
ation in a
r
ou
nd the cl
ock
(high ava
ila
bili
ty, scalability and reliability
)
. We
can
onl
y
review practi
cally ba
se
d
on theo
reti
ca
l idea. Bu
t, we h
a
ve to review a
nd ju
stify the syst
em
behavio
r of space & time complexity based on ma
chin
e size and p
r
oblem si
ze
s [5].
On behalf of t
he /etc/sy
s
tem script,
we
can
update and improve the
kernel capability as
per b
u
sin
e
ss
requi
rem
ent and that ca
n
be help to ou
r machine
size and p
r
obl
e
m
sizes a
naly
s
is
(Tabl
e 1-3
)
.
Ho
w
is beh
aving
the UNIX serve
r
al
ong with
its
sub
compon
ents,
whe
n
we a
r
e
runni
n
g
on the differe
nt processo
r, memory & en
crypti
on
key on the same
prog
ram
m
ing
& application
or
reverse way? The /var/adm/messa
ge script
s will
be give
the out
put statisti
cs of hardware and
softwa
r
e p
r
ob
lem of real ti
me event ma
nagem
ent
sy
stem in
cludi
n
g
date an
d time stam
p on
unix
machi
ne.
In this ways,
we can impro
v
e the perform
ance, ben
chmarkin
g, fault toleran
c
e
and ri
sk
assessm
ent
at a time to utilizing the virtual
programming appl
ication, which is help to
our
busi
n
e
ss, technolo
g
y and
so
ciety in aro
und the glo
b
e
.
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