Inter
national
J
our
nal
of
Electrical
and
Computer
Engineering
(IJECE)
V
ol.
8,
No.
1,
February
2018,
pp.
304
–
325
ISSN:
2088-8708
304
I
ns
t
it
u
t
e
o
f
A
d
v
a
nce
d
Eng
ine
e
r
i
ng
a
nd
S
cie
nce
w
w
w
.
i
a
e
s
j
o
u
r
n
a
l
.
c
o
m
Pr
efer
ences
Based
Customized
T
rust
Model
f
or
Assessment
of
Cloud
Ser
vices
Shilpa
Deshpande
and
Rajesh
Ingle
Department
of
Computer
Engineering,
Colle
ge
of
Engineering
Pune,
Sa
vitribai
Phule
Pune
Uni
v
ersity
,
India
Article
Inf
o
Article
history:
Recei
v
ed
May
13,
2017
Re
vised
No
v
23,
2017
Accepted
Dec
7,
2017
K
eyw
ord:
Cloud
computing
Customized
trust
assessment
Dynamic
trust
Elastic
trust
computation
Quality
of
Service
(QoS)
ABSTRA
CT
In
cloud
en
vironment,
man
y
functionally
similar
cloud
services
are
a
v
ailable.
But,
the
ser
-
vices
dif
fer
in
Quality
of
Service
(QoS)
le
v
els,
of
f
ered
by
them.
There
is
a
di
v
ersity
in
user
requirements
about
the
e
xpected
qualities
of
cloud
services.
T
rust
is
a
measure
to
under
-
stand
whether
a
cloud
service
can
adequately
meet
the
user
requirements.
Consequently
,
trust
assessment
plays
a
significant
role
in
selecting
the
suitable
cloud
service.
This
pa-
per
proposes
preferences
based
customized
trust
model
(PBCTM)
for
trust
assessment
of
cloud
services.
PBCTM
tak
es
into
account
user
requirements
about
the
e
xpected
quality
of
services
in
the
form
of
preferences.
Accordingly
,
it
performs
customized
trust
assessment
based
on
the
e
vidences
of
v
arious
attrib
utes
of
cloud
service.
PBCTM
enables
elastic
trust
computation,
which
is
responsi
v
e
to
dynamically
changing
us
er
preferences
with
time.
The
model
f
acilitates
dynamic
trust
based
periodic
selection
of
cloud
services
according
to
v
ary-
ing
user
preferences.
Experimental
results
demonstrate
tha
t
the
proposed
preferences
based
customized
trust
model
outperforms
the
other
model
in
respect
of
accurac
y
and
de
gree
of
satisf
action.
Copyright
c
2018
Institute
of
Advanced
Engineering
and
Science
.
All
rights
r
eserved.
Corresponding
A
uthor:
Shilpa
Deshpande,
Department
of
Computer
Engineering,
Colle
ge
of
Engineering
Pune,
Sa
vitribai
Phule
Pune
Uni
v
ersity
,
Pune,
Maharashtra,
India
Email:
shilpshree@yahoo.com
1.
INTR
ODUCTION
Cloud
computing
has
entered
mainstream
and
recei
v
ed
wider
acceptance.
It
is
increasingly
adopted
by
indi-
viduals,
small
and
medium
scale
enterprises
(SMEs)
and
go
v
ernment
or
g
anizations
to
run
their
critical
applications.
The
reason
for
this
acceptance
is
the
characteristics
of
cloud
lik
e
scalability
,
on
demand
service,
an
ytime-an
ywhere
access,
economic
benefits
of
pay-per
-use,
dele
g
ation
of
maintenance
and
administration,
performance
and
disaster
re-
co
v
ery
.
Cloud
services
ha
v
e
proliferated
to
include
softw
are
as
a
service,
database
as
a
service,
platform
as
a
service,
infrastructure
as
a
service,
security
as
a
service
and
storage
as
a
service
[1].
Cloud
en
vironment
still
remains
chal-
lenging
to
rely
on
because
of
f
actors
lik
e
loss
of
control
o
v
er
applications
and
data,
increased
threats
of
security
[2],
performance
is
sues
related
to
virtualization
[3],
enterprise
grade
a
v
ailability
requirements
[4,
5,
6]
and
adequately
meeting
Quality
of
Service
(QoS)
e
xpectations
of
users
[7].
Cloud
computing
has
compelling
adv
antages
yet
challenges
too.
F
or
an
enterprise
to
adopt
cloud,
it
is
impor
-
tant
that
enterprise
has
a
certain
belief
that
adv
antages
of
cloud
can
be
realized.
T
rust
is
a
measure
of
this
belief
[8].
Con
v
entionally
,
people
rely
on
reputation
[4,
8],
service
le
v
el
agreement
(SLA)
[6,
9],
self-assessment
[8,
9]
and
cloud
auditing
[8,
9]
for
trust
assessment
in
cloud
en
vironment.
Ho
we
v
er
,
trust
assessment
in
cloud
en
vironment
poses
further
important
issues,
which
are
re
v
ealed
as
part
of
the
follo
wing
discussion.
Reputation
based
traditional
trust
assessment
technique
relies
on
the
opinions
of
cloud
users.
The
opinions
tak
en
in
the
form
of
ratings
or
feedbacks
may
be
subjecti
v
e
in
nature
[8].
Therefore,
reputation
cannot
be
an
e
xact
reflection
of
realistic
capabilities
of
the
cloud
service.
A
service
le
v
el
agreement
(SLA)
established
between
a
cloud
service
consumer
and
a
pro
vider
consists
of
functional
and
QoS
f
acets
of
the
of
fered
service
[10].
Le
v
els
of
SLA
are
not
consistent
among
the
cloud
service
pro
viders
of
fering
analogous
services.
Moreo
v
er
,
for
a
service
pro
vider
,
J
ournal
Homepage:
http://iaescor
e
.com/journals/inde
x.php/IJECE
I
ns
t
it
u
t
e
o
f
A
d
v
a
nce
d
Eng
ine
e
r
i
ng
a
nd
S
cie
nce
w
w
w
.
i
a
e
s
j
o
u
r
n
a
l
.
c
o
m
,
DOI:
10.11591/ijece.v8i1.pp304-325
Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE
ISSN:
2088-8708
305
promises
made
in
SLA
and
actual
QoS
deli
v
ered
are
not
consistent.
Consequently
,
it
mak
es
hard
for
consumers
,
to
e
v
aluate
the
trust
of
a
cloud
service,
solely
based
on
the
SLA
[6,
9].
Cloud
service
pro
vider
may
announce
the
self-
assessment
of
the
of
fered
cloud
services,
based
on
cloud
t
ransparenc
y
mechanisms
[8].
Ho
we
v
er
,
such
e
v
aluat
ion
of
cloud
services
reflects
merely
a
generalized
trust
assessment
of
cloud
services
from
the
vie
wpoint
of
pro
vider
.
The
mechanism
of
self-assessment
does
not
tak
e
into
consideration
cloud
user’
s
perspecti
v
e.
A
formal
cloud
audit
based
trust
e
v
aluation
is
an
anot
h
e
r
method
which
pro
viders
may
use
to
ensure
the
quality
of
of
fered
services.
Ho
we
v
er
,
audit
report
typically
represents
only
a
static
trust
assessment
of
the
service
at
the
time
when
auditing
is
done
[9].
Cloud
QoS
attrib
utes
such
as
performance,
a
v
ailability
,
reliability
and
security
are
significant
for
user
and
hence
for
trust
assessment
of
a
cloud
service
[9].
P
ast
recorded
e
vidences
of
QoS
attrib
utes
signify
the
actual
v
alues
and
the
y
represent
the
realistic
capabilities
of
a
cloud
service
[8].
Therefore,
the
e
vidences
of
cloud
QoS
attrib
utes
are
needed
to
be
tak
en
into
account
by
t
he
trust
e
v
aluation
mechanism.
There
is
a
di
v
ersity
in
requirements
of
cloud
users
about
the
e
xpected
qualities
of
cloud
services.
Hence,
customized
trust
assessment
[9]
of
a
cloud
service
which
tak
es
into
consideration
the
user
preferences
for
the
cloud
QoS
attrib
utes,
is
needed
to
enable
the
personalized
selection
of
suitable
cloud
service
[11].
Cloud
service
pro
vider’
s
capacity
to
pro
vide
services
v
aries
with
time.
As
a
result,
the
QoS
le
v
els
of
of
fered
services
also
change
with
time.
Therefore,
trust
e
v
aluation
based
on
one-time
e
vidences
of
QoS
attrib
utes
is
not
enough
and
it
has
to
be
a
cons
tant
dynamic
process
[8].
Requirements
of
the
user
about
the
e
xpected
QoS
may
change
dynamically
with
time
[12].
Cloud
service
pro
vider’
s
ability
to
meet
user
requirements
is
not
al
w
ays
constant.
Therefore,
trust
assessment
which
includes
one-time
checking
of
requirements
of
the
user
is
not
adequate.
Hence,
trust
assessment
needs
to
be
respons
i
v
e
to
the
changes
in
requirements
of
the
user
.
This
implies
the
need
for
elastic
trust
assessment
as
per
the
changes
in
requirements
of
the
user
.
In
this
paper
,
we
present
preferences
based
customized
trust
model
(PBCTM),
addressing
the
abo
v
e
men-
tioned
issues.
More
specifically
,
the
contrib
utions
are:
1.
A
no
v
el
method
for
trust
comput
ation
of
a
cloud
service
based
on
the
distances
of
v
arious
service
e
vidences
from
the
user
preferences.
2.
Customized
trust
assessment
mechanism
containing
mathematical
formulation
of
weights
which
are
computed
based
on
the
relati
v
e
importance
of
cloud
service
attrib
utes
with
respect
to
QoS
e
xpectations
of
user
.
3.
Introduction
of
the
concept
of
elastic
trust
computation
of
a
cloud
service
and
an
algorithm
for
it.
4.
Mechanism
for
ranking
of
cloud
services
based
on
trust
computation
which
is
dynamic,
elas
tic
and
considers
preferences
of
users.
5.
Comparison
of
the
proposed
trust
model
with
other
model
with
re
g
ard
to
accurac
y
and
a
ne
w
measure
of
de
gree
of
satisf
action
of
trust
assessment.
The
paper
is
or
g
anized
as
follo
ws.
Section
2
presents
a
re
vie
w
of
related
w
ork.
In
Section
3,
the
architecture
of
the
system
meant
for
the
proposed
trust
model
and
the
functional
o
v
ervie
w
of
trust
assessment
are
described.
Section
4
defines
the
preferences
based
customized
trust
model
(PBCTM)
and
presents
the
details
of
customized
and
dynamic
trust
assessment.
Section
5
presents
the
algorithm
for
elastic
trust
computation
of
a
cloud
service.
Section
6
depicts
the
method
for
ranking
of
cloud
services
based
on
the
proposed
trust
model.
Section
7
presents
the
qualitati
v
e
comparison
of
PBCTM
with
other
models.
Section
8
co
v
ers
the
performance
e
v
aluation
of
the
proposed
trust
model
including
the
results
and
analysis.
Section
9
concludes
the
paper
.
2.
RELA
TED
W
ORK
Reputation
based
approaches
mak
e
use
of
feedbacks
from
man
y
cloud
users
to
e
v
aluate
trust
of
a
cloud
service.
T
rust
assessment
approaches
proposed
by
[13,
14,
15]
are
based
on
reputation.
These
approaches
do
not
tak
e
into
account
requirements
of
user
for
t
rust
e
v
aluation.
Moreo
v
er
,
these
re
p
ut
ation
based
approaches
f
all
short
in
performing
dynamic
assessment
of
trust
[8].
Besides
user
feedbacks,
fe
w
of
the
approaches
in
literature,
tak
e
into
consideration
additional
f
actors
such
as
pro
vider’
s
self-declarations
and
e
xpert’
s
ratings,
for
trust
assessment
[16].
Ho
we
v
er
,
credibility
[4]
of
the
f
actors
included
in
trust
e
v
aluation
is
a
main
concern
in
these
approaches.
Habib
et
al.
[10]
proposed
an
architecture
to
enable
trust
assessment
of
cloud
service
pro
viders
using
v
arious
f
actors
such
as
pro
vider
statements,
user
feedbacks
and
certificates.
A
trust
model
based
on
service
le
v
el
agreement
(SLA)
parameters
is
proposed
by
P
a
w
ar
et
al.
[17].
Ghosh
et
al.
[18]
proposed
a
frame
w
ork
for
assessment
of
risk
of
interaction
with
cloud
service
pro
vider
.
The
approach
in
turn
includes
e
v
aluating
t
rust
of
the
service
pro
vider
.
The
trust
is
estimated
on
the
basis
of
direct
and
indirect
interactions
Pr
efer
ences
Based
Customized
T
rust
Model
for
Assessment
...
(Shilpa
Deshpande)
Evaluation Warning : The document was created with Spire.PDF for Python.
306
ISSN:
2088-8708
between
customer
and
cloud
pro
vider
.
The
approaches
[10,
17,
18]
do
not
of
fer
dynamic
trust
update
along
a
period
of
time.
Also,
these
approaches
do
not
consider
QoS
requirements
of
us
er
for
trust
assessment.
A
model
is
recommended
by
Mo
yano
et
al.
[19]
to
e
v
aluate
trust
of
cloud
pro
viders
.
Although,
the
approach
is
simple,
trust
assessment
mainly
depends
on
the
accessibility
to
the
information
released
by
the
cloud
pro
viders.
Fe
w
of
the
approaches
do
tak
e
into
account
QoS
attrib
utes
for
trust
e
v
aluation.
The
approach
proposed
by
Manuel
et
al.
[20]
e
v
aluates
the
trust
of
a
cloud
resource
in
terms
of
summation
of
v
alues
ass
igned
to
user
feedbacks,
security
le
v
el
and
reputation.
A
model
is
suggested
by
Manuel
et
al.
[21]
to
compute
the
reputation
based
trust
of
a
resource.
The
model
mak
es
use
of
identity
,
capability
and
beha
vior
v
alues
of
a
resource
to
obtai
n
its
trust
v
alue.
The
approaches
[20,
21]
do
not
consider
requirements
of
users
in
trust
estimation
of
resources.
Also,
these
approaches
do
not
reflect
dynamic
trust
assessment
of
resources
along
a
period
of
time.
A
fuzzy
trust
e
v
aluation
approach
for
cloud
services
is
suggested
by
Huo
et
al.
[22].
The
approach
tak
es
into
consideration
a
set
of
cloud
s
ervice
attrib
utes
to
assess
the
reputation
based
trust
v
alue.
F
an
et
al.
[
23
]
suggested
a
mechanism
for
e
v
aluating
dynamic
trust
of
a
cloud
service
using
multiple
attrib
utes.
The
mechanism
of
trust
computation
relies
on
the
feedbacks
gi
v
en
by
the
users.
Ho
we
v
er
,
authenticity
of
feedbacks
is
not
addressed
by
the
authors.
The
approach
f
acilitates
selection
of
a
service
according
to
the
user
requirements
for
v
arious
attrib
utes.
Ho
we
v
er
,
the
approaches
[21,
22,
23]
are
dependent
on
subjecti
v
ely
allocated
weights
to
the
v
arious
f
actors.
The
QoS
based
mechanisms
in
literature,
mak
e
us
e
of
a
v
ailability
,
performance,
security
and
reliability
as
the
general
attrib
utes
of
cloud
service
for
trust
assessment.
Throughput,
response
time,
netw
ork
bandwidth
and
ca-
pability
are
the
usually
considered
performa
nce
related
f
actors
in
trust
estimation.
Li
et
al.
[24]
proposed
a
method
for
dynamic
trust
e
v
aluation
of
cloud
resources.
It
mak
es
use
of
recorded
v
alues
of
v
arious
attrib
utes
for
computation
of
trust.
The
authors
do
not
focus
on
consideration
of
user
requirements
for
attrib
utes,
in
e
v
aluating
trust
v
alue
of
a
resource.
Frame
w
orks
are
proposed
by
[25,
26]
for
trust
e
v
aluation
of
cloud
service
pro
viders
based
on
QoS
attrib
utes.
The
approaches
are
based
on
monitoring
QoS
attrib
utes
and
e
v
aluating
the
compliance
with
re
g
ard
to
the
SLA.
System
suggested
by
[26]
i
n
c
orporates
perspecti
v
es
of
dif
ferent
entities
such
as
cloud
users,
auditor
and
peers
in
the
process
of
trust
e
v
aluation.
Supriya
et
al.
[27]
proposed
to
emplo
y
multi-criteria
based
d
e
cision
making
methods
for
e
v
aluating
trust
of
cloud
service
pro
viders.
The
w
ork
f
acilitates
ranking
of
pro
viders
based
on
their
trust
v
alues.
It
of
fers
per
-
sonalized
computation
of
trust
by
considering
priorities
for
the
v
arious
attrib
utes
of
cloud
pro
vider
.
Ho
we
v
er
,
priority
based
weights
assigned
to
the
dif
ferent
attrib
utes
are
static
and
subjecti
v
e.
System
is
proposed
by
Qu
and
Buyya
[11]
for
trust
estimation
of
a
cloud
service
based
on
its
performance
in
terms
of
v
arious
QoS
attrib
utes.
The
approach
tak
es
into
account
QoS
requirements
of
user
and
computes
the
trust
of
a
service
based
on
fulfillment
of
the
requirements.
Ho
we
v
er
,
the
approaches
[11,
25,
26,
27]
do
not
of
fer
dynamic
trust
update
in
cloud
en
vironment.
A
model
is
proposed
by
Manuel
[28]
to
e
v
aluate
trust
of
a
resource
based
on
its
capabilities
and
measured
QoS
attrib
utes.
T
rust
update
is
indicated
only
by
algorithmic
steps.
The
model
enables
matching
the
QoS
requirements
of
users
to
the
resources
according
to
their
com
p
ut
ed
trust
v
alues.
Ho
we
v
er
,
static
weights
based
on
pre-decided
priorities
are
assigned
to
the
v
arious
attrib
utes.
In
summary
,
consideration
of
user
requirements
in
trust
assessment
is
essential
to
enable
the
personalized
selection
of
appropriate
cloud
services.
Ho
we
v
er
,
the
abo
v
e
re
vie
w
of
the
related
w
ork
signifies
that
only
fe
w
of
the
approaches
[11,
23,
27,
28]
consider
requi
rements
of
cloud
users
for
trust
assessment.
Cloud
QoS
attrib
utes
are
sig-
nificant
for
trust
e
v
aluation
of
a
cloud
service.
Evidences
of
QoS
attrib
utes
obtained
through
monitoring
are
unbiased
in
nature
and
are
more
dependable
f
actors
for
trust
estimation.
Ho
we
v
er
,
the
approach
[23]
does
not
tak
e
into
account
e
vidences
of
QoS
attrib
utes
and
trust
assessment
solely
relies
on
the
feedbacks
of
users.
Dynamic
cloud
en
vironment
implies
the
need
for
trust
to
be
assessed
continuously
with
ti
me.
Ho
we
v
er
,
the
approaches
[11,
27]
do
not
of
fer
dynamic
trust
e
v
aluation
of
cl
oud
services.
Although,
the
approach
[28]
tak
es
into
consideration
requirements
of
users,
weights
calculation
for
v
arious
attrib
utes
in
trust
assess
ment
does
not
reflect
preferences
of
users.
Moreo
v
er
,
assessment
of
trust
according
to
the
dynamical
ly
changing
requirements
of
the
user
,
is
not
addressed
by
an
y
of
the
abo
v
e
approaches.
Our
trust
model
PBCTM,
aims
to
address
these
limitations
in
the
earlier
w
ork.
PBCTM
performs
customized
trust
assessment
of
a
cloud
service
by
taking
into
account
e
vidences
of
service
attrib
utes
and
preferences
of
user
for
at-
trib
utes.
Our
model
f
acilitates
elastic
trust
computation
of
a
cloud
service
according
to
the
dynamically
changing
user
preferences
of
attrib
utes
with
time.
PBCTM
enables
computation
of
weights
for
the
multiple
attrib
utes
of
a
service
by
considering
the
relati
v
e
utility
of
attrib
utes
with
respect
to
the
user
preferences.
Dynamic
trust
prediction
used
in
our
model,
allo
ws
ranking
of
cloud
services
to
assist
the
user
in
periodic
selection
of
suitable
service.
3.
ARCHITECTURE
OF
TR
UST
ASSESSMENT
SYSTEM
Figure
1a
sho
ws
the
o
v
erall
layout
of
the
system
meant
for
the
proposed
trust
model.
It
depicts
the
main
trust
assessment
and
ranking
module
which
is
connected
with
the
other
supplementary
modules.
The
functional
specification
collector
compiles
the
functional
requirements
of
the
cloud
service,
submitted
by
cloud
us
er
.
Multiple
IJECE
V
ol.
8,
No.
1,
February
2018:
304
–
325
Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE
ISSN:
2088-8708
307
service
pro
viders
re
gister
their
services
i
n
t
o
the
service
repository
.
Services
Extraction
module
finds
the
services
from
service
repository
whose
functional
specifications
match
with
the
required
one.
The
user
preferences
collector
compiles
the
preference
v
alues
for
cloud
service
attrib
utes
such
as
a
v
ailability
,
throughput
and
response
time,
which
are
submitted
by
the
cloud
user
.
(a)
Architecture
(b)
Functional
o
v
ervie
w
Figure
1.
T
rust
assessment
system
for
preferences
based
customized
trust
model
(PBCTM)
T
rust
assessment
and
ranking
module
is
the
core
component
performing
dynamic
and
elastic
trust
computa-
tion
of
the
cloud
services.
F
or
each
of
the
matching
cloud
services,
the
trust
assessment
is
carried
out
by
taking
into
account
the
user
preferences
and
the
e
vidences
of
service
attrib
utes.
The
results
of
trust
assessment
and
ranking
are
recorded
in
the
customized
trust
archi
v
es.
The
cloud
user
can
select
the
appropriate
cloud
s
ervice
based
on
the
ranking
of
cloud
services.
The
process
of
monitoring,
continuously
observ
es
and
records
the
v
alues
of
attrib
utes
such
as
response
time,
throughput,
a
v
ailability
and
security
,
for
each
of
the
cloud
services.
The
e
vidence
collector
collects
the
e
vidence
f
actors,
recorded
as
part
of
continuous
monitoring
process.
These
e
vidence
f
actors
are
then
used
for
trust
assessment
of
cloud
services.
T
rust
assessment
and
ranking
is
the
main
focus
of
this
paper
.
Hence,
the
details
of
other
modules
which
include
services
e
xtraction,
monitoring
and
related
functionalities,
are
not
discussed
further
,
in
this
paper
.
W
e
assume
these
as
the
already
e
xisting
v
alid
services
and
are
a
v
ailable
in
the
form
of
e
xternal
interf
aces
to
the
trust
assessment
and
ranking
module.
Figure
1b
sho
ws
the
high-le
v
el
functional
o
v
ervie
w
for
trust
assessment
and
ranking
of
cloud
services.
The
user
preferences
for
v
arious
service
attrib
utes,
are
tak
en
as
input
for
the
trust
assessment.
Evidence
f
actors
for
each
of
the
matching
cloud
services,
o
v
er
the
period
of
time,
are
tak
en
as
another
input
by
the
trust
assessment
module.
The
module
calculates
customized
present
trust
of
services
at
an
instant
of
time,
by
considering
the
user
preferences
and
the
corresponding
service
e
vidence
f
actors.
Subsequently
,
the
module
performs
dynamic
prediction
of
trust
v
alues
of
cloud
services
o
v
er
a
period
of
time.
The
customized
present
trust
and
predicted
trust
v
alues
are
returned
to
the
cloud
user
.
The
ranking
of
cloud
services
is
performed
by
the
module,
based
on
the
predicted
trust
v
alues
of
the
services.
The
resultant
ranking
sequence
of
cloud
services,
which
is
then
returned
to
the
cloud
user
,
f
acilitates
the
customized
selection
of
suitable
cloud
service
for
the
user
.
The
preferences
of
a
particular
cloud
user
may
change
dynamically
with
time.
Accordingly
,
the
operations
of
trust
assessment
and
ranking
of
cloud
services
are
performed
repetiti
v
ely
with
changing
preferences
of
the
user
and
the
continuous
e
vidences
of
each
cloud
service.
This
reflects
the
dynamic
and
elastic
trust
computation
of
cloud
services.
The
cloud
user
can
re
vise
the
selection
of
a
suitable
service
based
on
the
updated
ranking
of
cloud
services.
The
details
of
customized
and
dynamic
trust
asses
sments
of
a
cloud
service
are
described
in
Section
4.
The
steps
depicting
the
control
flo
w
for
elastic
trust
computation
are
presented
i
n
Section
5
in
the
form
of
algorithm.
The
trust
based
ranking
of
cloud
services
is
elaborated
in
Section
6.
4.
PREFERENCES
B
ASED
CUST
OMIZED
TR
UST
MODEL
T
rust
assessment
of
a
cloud
service
is
performed
based
on
the
preferences
of
a
cloud
user
for
service
attrib
utes
and
the
e
vidence
f
actors
of
the
service.
Evidence
f
actors
of
a
cloud
service
signify
the
recorded
v
alues
of
service
attrib
utes.
Pr
efer
ences
Based
Customized
T
rust
Model
for
Assessment
...
(Shilpa
Deshpande)
Evaluation Warning : The document was created with Spire.PDF for Python.
308
ISSN:
2088-8708
Definition
1
Pr
efer
ences
Based
Customized
T
rust
Model
(PBCTM)
is
defined
by
a
12-tuple:
(
L;
AC
;
T
I
;
P
R
;
C
;
N
C
;
P
D
P
;
N
D
P
;
C
P
T
;
C
T
;
E
;
D
)
wher
e
L:
Set
of
v
cloud
services:
f
s
1
;
s
2
;
:::;
s
v
g
A
C:
Set
of
m
cloud
service
attrib
utes:
f
R
1
;
R
2
;
:::;
R
m
g
TI:
Or
der
ed
discr
ete
set
of
n
time
instances,
in
a
time
window:
f
1
;
2
;
:::;
n
g
PR:
Set
of
pr
efer
ences
of
a
user
for
the
values
of
cloud
service
attrib
utes:
f
pr
1
;
pr
2
;
:::;
pr
m
g
C:
An
e
vidence
matrix
whic
h
depicts
m
e
vidence
factor
s
at
eac
h
of
the
n
time
instances.
NC:
Normalized
augmented
e
vidence
matrix
with
pr
efer
ences.
PDP:
Normalized
matrix
for
positive
distances
fr
om
pr
efer
ences.
NDP:
Normalized
matrix
for
ne
gative
distances
fr
om
pr
efer
ences.
CPT
:
Customized
Pr
esent
T
rust
of
a
cloud
service
at
a
particular
time
instant.
CT
:
Cumulative
T
rust
of
a
cloud
service
o
ver
a
period
of
time
.
E:
A
set
of
cor
e
trust
assessment
functions:
f
f
C
P
T
;
f
C
T
g
;
wher
e
f
C
P
T
indicates
a
function
to
compute
Customized
Pr
esent
T
rust
(CPT)
and
f
C
T
is
a
function
to
assess
Cumulative
T
rust
(CT).
D:
A
set
of
allied
functions:
f
f
N
E
;
f
P
D
;
f
N
D
;
f
C
W
g
;
wher
e
f
N
E
is
a
function
to
normalize
e
vidence
factor
s
and
pr
efer
ences;
f
P
D
and
f
N
D
ar
e
the
functions
to
compute
summative
positive
and
ne
gative
distances
fr
om
pr
efer
ences;
f
C
W
indicates
a
function
to
compute
weights
of
cloud
service
attrib
utes.
Evidence
f
actors
of
a
cloud
ser
vice
are
retrie
v
ed
after
e
v
ery
fix
ed
time
interv
al.
Representation
of
the
e
vidence
f
actors
is
sho
wn
by
an
e
vidence
matrix
as:
C
=
2
6
6
6
4
c
11
c
12
:
:
:
c
1
m
c
21
c
22
:
:
:
c
2
m
.
.
.
.
.
.
.
.
.
.
.
.
c
n
1
c
n
2
:
:
:
c
nm
3
7
7
7
5
(1)
In
Equation
(1),
at
a
particular
time
instant
i
in
a
time
windo
w
,
such
that
1
i
n
,
a
ro
w
in
the
matrix
indicates
a
sample
of
e
vidence
f
act
ors
as
f
c
i
1
;
c
i
2
;
:::;
c
im
g
and
each
v
alue
c
ij
in
the
sample,
de
n
ot
es
a
v
alue
of
an
attrib
ute
R
j
.
Thus
there
are
n
samples
of
e
vidence
f
actors.
Column
position
in
the
matrix
indicates
a
specific
attrib
ute
within
the
sample.
Preferences
for
the
v
alues
of
cloud
service
attrib
utes,
as
specified
by
the
user
,
are
combined
with
the
original
e
vidence
matrix,
to
obtain
the
augmented
matrix,
as
sho
wn
belo
w
.
C
P
=
2
6
6
6
6
6
4
c
11
c
12
:
:
:
c
1
m
c
21
c
22
:
:
:
c
2
m
.
.
.
.
.
.
.
.
.
.
.
.
c
n
1
c
n
2
:
:
:
c
nm
pr
1
pr
2
:
:
:
pr
m
3
7
7
7
7
7
5
(2)
In
Equation
(2),
the
last
ro
w
in
the
matrix
indicates
a
sample
of
preferences
as
f
pr
1
;
pr
2
;
:::;
pr
m
g
and
each
v
alue
pr
j
in
the
sample
denotes
a
preference
v
alue
of
an
attrib
ute
R
j
.
F
or
a
cloud
service,
higher
v
alues
for
attrib
utes
such
as
a
v
ailability
and
throughput
are
desired.
Whereas,
lo
wer
v
alues
for
attri
b
utes
such
as
response
time
and
security
violation
incidents
are
e
xpected.
If
the
preference
v
alue
for
an
y
of
the
attrib
utes
is
not
specified
by
the
user
,
then
it
reflects
that,
a
minimum
quality
le
v
el
for
that
service
attrib
ute
is
acceptable
to
the
user
.
Hence,
in
such
case
the
preference
v
alue
for
the
attrib
ute
in
matrix
C
P
is
set
to
a
minimum
or
maximum
v
alue
of
the
service
attrib
ute
in
the
time
windo
w
,
based
on
the
higher
-v
alue
type
of
attrib
ute
(e.
g.
a
v
ailability)
or
the
lo
wer
-v
alue
type
of
the
attrib
ute
(e.
g.
response
time),
respecti
v
ely
.
In
order
to
transform
all
the
v
alues
in
matrix
C
P
to
uniform
range
and
to
mak
e
them
independent
of
units,
v
alues
of
the
matrix
C
P
need
to
be
normali
zed.
Normalization
includes
scaling
of
the
v
alues.
Thus,
for
further
processing
of
distance
computation,
each
v
alue
in
the
matrix
C
P
is
normalized
in
the
range
denoted
by
[
R
new
min
;
R
new
max
]
.
From
the
perspecti
v
e
of
desired
performance
of
a
cloud
service,
attrib
utes
can
be
cate
gorized
in
tw
o
types:
one
where
higher
v
alues
of
an
attrib
ute
R
j
are
desired
and
the
other
where
lo
wer
v
alues
of
R
j
are
desired.
The
cate
gory
where
higher
v
alues
of
R
j
are
desired,
the
corresponding
normalized
v
alues
x
ij
and
y
j
for
c
ij
and
pr
j
respecti
v
ely
,
are
formulated
as:
x
ij
=
(
c
ij
R
min
j
)(
R
new
max
R
new
min
)
(
R
max
j
R
min
j
)
+
R
new
min
(3)
y
j
=
(
pr
j
R
min
j
)(
R
new
max
R
new
min
)
(
R
max
j
R
min
j
)
+
R
new
min
(4)
IJECE
V
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No.
1,
February
2018:
304
–
325
Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE
ISSN:
2088-8708
309
The
other
cate
gory
where
lo
wer
v
alues
of
R
j
are
desired,
the
corresponding
normalized
v
alues
x
ij
and
y
j
for
c
ij
and
pr
j
respecti
v
ely
,
are
de
vised
as:
x
ij
=
(
R
max
j
c
ij
)(
R
new
max
R
new
min
)
(
R
max
j
R
min
j
)
+
R
new
min
(5)
y
j
=
(
R
max
j
pr
j
)(
R
new
max
R
new
min
)
(
R
max
j
R
min
j
)
+
R
new
min
(6)
In
Equations
(3)
to
(6),
R
min
j
is
the
minimum
v
alue
of
the
attrib
ute
R
j
and
R
max
j
is
the
maximum
v
alue
of
R
j
in
matrix
C
P
.
The
normalized
augmented
matrix
is:
N
C
=
2
6
6
6
6
6
4
x
11
x
12
:
:
:
x
1
m
x
21
x
22
:
:
:
x
2
m
.
.
.
.
.
.
.
.
.
.
.
.
x
n
1
x
n
2
:
:
:
x
nm
y
1
y
2
:
:
:
y
m
3
7
7
7
7
7
5
(7)
In
normalized
matrix
N
C
,
greate
r
v
alue
for
an
y
service
attrib
ute
R
j
where
1
j
m
,
indicates
a
higher
quality
of
a
cloud
service
than
the
quality
of
a
cloud
service
corresponding
to
the
lo
wer
v
alue
of
the
attrib
ute.
4.1.
Computation
of
Distances
fr
om
Pr
efer
ences
If
the
v
alues
of
service
attrib
utes
are
higher
as
compared
to
the
corresponding
preference
v
alues,
it
reflects
a
more
trustw
orthiness
of
a
cloud
service.
Here
we
introduce,
the
ne
w
terms,
Positi
v
e
Distance
(
P
D
)
and
Ne
g
ati
v
e
Distance
(
N
D
)
to
define
the
comparison
of
the
service
attrib
ute
v
alues
and
the
associated
preference
v
alues.
P
D
and
N
D
are
the
measures
for
assessment
of
ho
w
closely
a
cloud
service
meets
or
f
ails
to
meet
the
user
e
xpectations.
Definition
2
P
ositive
Distance
(
P
D
)
and
Ne
gative
Distance
(
N
D
)
for
any
value
a
ij
in
matrix
N
C
,
wher
e
1
i
(
n
+
1)
,
of
attrib
ute
R
j
fr
om
its
corr
esponding
pr
efer
ence
value
y
j
,
ar
e
formulated
as
shown
in
T
able
1.
T
able
1.
Distances
from
Preferences
Scenarios
for
a
ij
P
D
N
D
Attrib
ute
v
alue
(
a
ij
)
=
Preference
v
alue
(
y
j
)
y
j
0
Attrib
ute
v
alue
(
a
ij
)
>
Preference
v
alue
(
y
j
)
y
j
+
a
ij
y
j
a
ij
Attrib
ute
v
alue
(
a
ij
)
<
Preference
v
alue
(
y
j
)
a
ij
y
j
y
j
a
ij
As
defined
in
T
able
1,
if
the
attrib
ute
v
alue
is
greater
than
or
equal
to
the
preference
v
alue,
then
its
(
P
D
)
is
higher
than
its
(
N
D
)
.
If
the
attrib
ute
v
alue
is
lesser
than
the
preference
v
alue,
then
its
(
P
D
)
is
lesser
than
its
(
N
D
)
.
Also,
when
the
attrib
ute
v
alue
is
greater
than
the
preference
v
alue,
then:
i)
its
(
P
D
)
is
higher
than
the
(
P
D
)
for
an
attrib
ute
whose
v
alue
equals
the
preference
v
alue.
ii)
its
(
N
D
)
is
lesser
than
the
(
N
D
)
for
an
attrib
ute
whose
v
alue
equals
the
preference
v
alue.
Whereas,
when
the
attrib
ute
v
alue
is
lesser
than
the
preference
v
alue,
then:
i)
its
(
P
D
)
is
lesser
than
the
(
P
D
)
for
an
attrib
ute
whose
v
alue
equals
the
preference
v
alue.
ii)
its
(
N
D
)
is
higher
than
the
(
N
D
)
for
an
attrib
ute
whose
v
alue
equals
the
preference
v
alue.
Thus,
for
the
v
alues
of
all
the
attrib
utes
in
matrix
N
C
,
which
include
both,
normalized
service
e
vidence
f
actors
and
preference
v
alues,
computations
of
P
D
and
N
D
v
alues
are
performed.
The
computed
P
D
and
N
D
v
alues
are
represented
in
the
form
of
Positi
v
e
Distance
and
Ne
g
ati
v
e
Distance
matrices
respecti
v
ely
,
as:
P
S
=
2
6
6
6
6
6
4
ps
11
ps
12
:
:
:
ps
1
m
ps
21
ps
22
:
:
:
ps
2
m
.
.
.
.
.
.
.
.
.
.
.
.
ps
n
1
ps
n
2
:
:
:
ps
nm
ps
(
n
+1)1
ps
(
n
+1)2
:
:
:
ps
(
n
+1)
m
3
7
7
7
7
7
5
(8)
Pr
efer
ences
Based
Customized
T
rust
Model
for
Assessment
...
(Shilpa
Deshpande)
Evaluation Warning : The document was created with Spire.PDF for Python.
310
ISSN:
2088-8708
N
S
=
2
6
6
6
6
6
4
ns
11
ns
12
:
:
:
ns
1
m
ns
21
ns
22
:
:
:
ns
2
m
.
.
.
.
.
.
.
.
.
.
.
.
ns
n
1
ns
n
2
:
:
:
ns
nm
ns
(
n
+1)1
ns
(
n
+1)2
:
:
:
ns
(
n
+1)
m
3
7
7
7
7
7
5
(9)
In
Equation
(8),
at
position
i
such
that
1
i
n
,
a
ro
w
in
the
ma
trix
P
S
indicates
a
sample
of
positi
v
e
distances
as
f
ps
i
1
;
ps
i
2
;
:::;
ps
im
g
corresponding
to
e
vidence
sample
f
x
i
1
;
x
i
2
;
:::;
x
im
g
of
matrix
N
C
.
Here,
ps
ij
denotes
a
P
D
v
alue
for
an
e
vidence
f
actor
x
ij
of
an
attrib
ute
R
j
from
its
preference
v
alue
y
j
.
Similarly
,
in
Equation
(9),
at
position
i
such
that
1
i
n
,
a
ro
w
in
the
matrix
N
S
indicates
a
sample
of
ne
g
ati
v
e
distances
as
f
ns
i
1
;
ns
i
2
;
:::;
ns
im
g
corresponding
to
e
vidence
sample
f
x
i
1
;
x
i
2
;
:::;
x
im
g
of
matrix
N
C
.
Here,
ns
ij
denotes
a
N
D
v
alue
for
an
e
vidence
f
actor
x
ij
of
an
attrib
ute
R
j
from
its
preference
v
alue
y
j
.
The
(
n
+
1)
th
ro
ws
in
matrices
P
S
and
N
S
represent
the
samples
of
positi
v
e
and
ne
g
ati
v
e
distances
respecti
v
ely
,
for
the
sample
f
y
1
;
y
2
;
:::;
y
m
g
of
preferences
in
matrix
N
C
.
F
or
ne
xt
processing
of
customized
trust
computation,
all
the
distance
v
alues
in
the
matrices
P
S
and
N
S
are
normalized
in
the
range
denoted
by
[
D
new
min
;
D
new
max
]
.
This
con
v
ersion
of
all
the
distance
v
alues
to
uniform
range
is
made
by
preserving
the
original
relati
v
e
ordering
among
the
distance
v
alues
for
each
of
the
attrib
utes.
F
or
each
v
alue
ps
ij
in
matrix
P
S
,
where
1
i
(
n
+
1)
,
the
normalized
v
alue
pd
ij
is
formulated
as
sho
wn
belo
w
.
pd
ij
=
(
ps
ij
P
min
j
)(
D
new
max
D
new
min
)
(
P
max
j
P
min
j
)
+
D
new
min
(10)
where
P
min
j
is
the
minimum
v
alue
of
positi
v
e
distance
and
P
max
j
is
the
maximum
v
alue
of
positi
v
e
distance
for
attrib
ute
R
j
in
matrix
P
S
.
The
normalized
positi
v
e
distance
matrix
is:
P
D
P
=
2
6
6
6
6
6
4
pd
11
pd
12
:
:
:
pd
1
m
pd
21
pd
22
:
:
:
pd
2
m
.
.
.
.
.
.
.
.
.
.
.
.
pd
n
1
pd
n
2
:
:
:
pd
nm
pd
(
n
+1)1
pd
(
n
+1)2
:
:
:
pd
(
n
+1)
m
3
7
7
7
7
7
5
(11)
F
or
each
v
alue
ns
ij
in
matrix
N
S
,
where
1
i
(
n
+
1)
,
the
normalized
v
alue
nd
ij
is
formulated
as
sho
wn
belo
w
.
nd
ij
=
(
ns
ij
G
min
j
)(
D
new
max
D
new
min
)
(
G
max
j
G
min
j
)
+
D
new
min
(12)
where
G
min
j
is
the
minimum
v
alue
of
ne
g
ati
v
e
distance
and
G
max
j
is
the
maximum
v
alue
of
ne
g
ati
v
e
distance
for
attrib
ute
R
j
in
matrix
N
S
.
The
normalized
ne
g
ati
v
e
distance
matrix
is:
N
D
P
=
2
6
6
6
6
6
4
nd
11
nd
12
:
:
:
nd
1
m
nd
21
nd
22
:
:
:
nd
2
m
.
.
.
.
.
.
.
.
.
.
.
.
nd
n
1
nd
n
2
:
:
:
nd
nm
nd
(
n
+1)1
nd
(
n
+1)2
:
:
:
nd
(
n
+1)
m
3
7
7
7
7
7
5
(13)
4.2.
Distance
based
Calculation
of
Customized
Pr
esent
T
rust
Customized
present
trust
of
a
cloud
service
is
an
indication
of
relati
v
e
quality
of
the
service
at
an
instant
of
time,
with
re
g
ard
to
the
e
xpectations
of
the
user
.
Hence,
for
ef
fecti
v
e
customized
trust
assessment
of
a
cloud
service,
e
vidence
f
actors
need
to
be
e
v
aluated
on
the
basis
of
their
positi
v
e
and
ne
g
ati
v
e
distances
from
the
preference
v
alues.
Consequently
,
all
the
m
positi
v
e
distance
v
alues
in
sample
i
such
that
1
i
(
n
+
1)
,
of
matrix
P
D
P
,
are
aggre
g
ated
based
on
weights
of
attrib
utes,
to
form
a
summati
v
e
measure
of
positi
v
e
distances,
as
sho
wn
belo
w
.
S
P
i
=
X
m
j
=1
w
j
pd
ij
(14)
where
pd
ij
is
a
normalized
positi
v
e
distance
for
attrib
ute
R
j
in
sample
i
.
Similarly
,
all
the
m
ne
g
ati
v
e
distance
v
alues
in
sample
i
such
that
1
i
(
n
+
1)
,
of
matrix
N
D
P
,
are
aggre
g
ated
based
on
weights
of
attrib
utes,
to
form
a
IJECE
V
ol.
8,
No.
1,
February
2018:
304
–
325
Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE
ISSN:
2088-8708
311
summati
v
e
measure
of
ne
g
ati
v
e
distances,
as
sho
wn
belo
w
.
S
N
i
=
X
m
j
=1
w
j
nd
ij
(15)
where
nd
ij
is
a
normalized
ne
g
ati
v
e
distance
for
attrib
ute
R
j
in
sample
i
.
In
Equations
(14)
and
(15),
w
j
is
a
weight
assigned
to
cloud
service
attrib
ute
R
j
such
that
0
<
w
j
<
1
and
P
m
j
=1
w
j
=
1
.
Static
weights
are
not
suitable
for
ef
fecti
v
e
customized
trust
assessment
of
a
cloud
service.
Hence,
weights
are
needed
to
be
computed
by
taking
into
consideration
the
user
preferences
for
v
arious
attrib
utes
of
a
cloud
service.
The
details
of
computation
of
weights
for
v
arious
cloud
service
attrib
utes,
are
described
in
Section
4.3.
F
or
an
e
vidence
sample
at
time
instant
i
such
that
1
i
n
,
corresponding
S
P
i
from
Equation
(14)
indicates
a
weighte
d
sum
of
positi
v
e
dis
tances
of
a
ll
m
e
vidence
f
actors
and
correspondi
n
g
S
N
i
from
Equat
ion
(15)
indi
cates
a
weighted
sum
of
ne
g
ati
v
e
distances
of
all
m
e
vidence
f
actors
in
the
sample.
When
v
alues
of
e
vidence
f
actors
of
a
cloud
service
match
the
user
preferences,
then
it
indicates
a
good
cloud
service
in
terms
of
mee
ting
the
user
e
xpectations.
If
positi
v
e
distances
of
e
vidence
f
actors
are
higher
than
the
corresponding
ne
g
ati
v
e
distances,
then
the
cloud
service
meets
the
requirements
of
the
user
.
Consequently
,
for
an
e
vidence
sample
at
time
instant
i
such
that
1
i
n
,
higher
v
alue
of
summati
v
e
positi
v
e
distance
(
S
P
i
)
signifies
the
better
trustw
orthiness
of
a
cloud
service.
Therefore,
customized
present
trust
of
a
cloud
service
at
time
instant
i
,
is
formulated
as
a
relati
v
e
share
of
summati
v
e
positi
v
e
distance
(
S
P
i
)
o
v
er
S
P
i
and
S
N
i
.
Definition
3
Customized
T
rust
value
of
a
cloud
service
(
s
l
),
at
a
time
instant
i
,
termed
as
Customized
Pr
esent
T
rust
(CPT)
is
defined
as:
C
P
T
i
(
s
l
)
=
S
P
i
S
P
i
+
S
N
i
(16)
wher
e
S
P
i
is
a
summative
positive
distance
and
S
N
i
is
a
summative
ne
gative
distance
of
e
vidence
factor
s
for
all
the
m
attrib
utes
of
the
service
,
in
sample
i
suc
h
that
1
i
n
and
0
<
C
P
T
i
(
s
l
)
<
1
.
4.3.
Computation
of
W
eights
W
eight
assigned
to
an
attrib
ute
signifies
the
import
ance
of
the
attrib
ute
in
trust
calculation.
W
eight
of
an
attrib
ute
is
computed
based
on
the
relati
v
e
utility
of
the
attrib
ute
with
respect
to
preference
v
alue
of
the
attrib
ute.
Definition
4
Utility
de
gr
ee
of
an
attrib
ute
R
j
,
in
a
time
window
containing
n
e
vidence
samples,
is
formulated
as
given
below
.
U
(
R
j
)
=
(
n
X
i
=1
x
ij
)
=y
j
(17)
wher
e
x
ij
is
a
normalized
e
vidence
factor
of
attrib
ute
R
j
at
time
instant
i
and
y
j
is
a
corr
esponding
normalized
pr
efer
ence
value
for
the
attrib
ute
.
From
Equation
(17),
when
all
the
e
vidence
f
actors
of
an
attrib
ute
in
a
t
ime
windo
w
of
size
n
,
e
xactly
match
with
the
preference
v
alue,
utility
de
gree
of
the
attrib
ute
becomes
equal
to
n
.
When
one
or
more
e
vidence
f
actors
are
less
than
the
speci
fied
preference
v
alue,
utility
de
gree
of
the
attrib
ute
reduces
to
a
v
alue
which
is
less
than
n
.
Whereas,
when
utility
de
gree
of
the
attrib
ute
goes
be
yond
n
,
it
implies
that
one
or
more
e
vidence
f
actors
are
greater
than
the
preference
v
alue.
This
is
the
most
desirable
situation,
where
the
cloud
service
attrib
ute
meets
the
e
xpected
quality
requirements.
Thus,
the
v
alues
of
utility
de
gree
for
v
arious
attrib
utes
within
a
sample,
signify
the
proportionate
ef
fect
on
the
weights
of
cloud
service
attrib
utes.
Accordingly
,
weight
w
j
of
an
attrib
ute
R
j
is
computed
as
sho
wn
belo
w
.
w
j
=
U
(
R
j
)
=
m
X
k
=1
U
(
R
k
)
(18)
where
0
<
w
j
<
1
and
P
m
j
=1
w
j
=
1
.
Higher
is
the
utility
de
gree
U
(
R
j
)
of
the
attrib
ute,
greater
is
its
resultant
weight.
The
weights
computed
using
Equati
on
(18),
are
substituted
in
Equations
(14)
and
(15),
which
subsequently
results
in
customized
trust
estimation
of
a
cloud
service,
from
Equation
(16).
Pr
efer
ences
Based
Customized
T
rust
Model
for
Assessment
...
(Shilpa
Deshpande)
Evaluation Warning : The document was created with Spire.PDF for Python.
312
ISSN:
2088-8708
4.4.
Calculation
of
Thr
eshold
T
rust
The
preferences
for
v
arious
attrib
utes
are
in
turn
used
to
deri
v
e
the
minimum
e
xpected
trust
v
alue
for
a
cloud
service.
This
trust
v
alue
is
termed
as
threshold
trust
v
alue,
which
serv
es
as
a
baseline
with
which
computed
trust
v
alues
can
be
compared.
From
Equation
(14),
S
P
(
n
+1)
indicates
a
weighted
sum
of
positi
v
e
distances
of
preference
v
alues
of
all
m
attrib
utes
and
corresponding
S
N
(
n
+1)
from
Equation
(15),
represents
a
weighted
sum
of
ne
g
ati
v
e
distances
of
preference
v
alues
of
all
m
attrib
utes.
On
the
lines
of
C
P
T
in
Equation
(16),
a
threshold
trust
v
alue
of
a
cloud
service
at
the
specified
preferences,
is
formulated
as
a
relati
v
e
share
of
S
P
(
n
+1)
o
v
er
S
P
(
n
+1)
and
S
N
(
n
+1)
,
as
sho
wn
belo
w
.
T
pr
(
s
l
)
=
S
P
(
n
+1)
S
P
(
n
+1)
+
S
N
(
n
+1)
(19)
4.5.
Pr
ediction
of
Cumulati
v
e
T
rust
fr
om
Customized
Pr
esent
T
rust
A
set
of
customized
present
trust
(
C
P
T
)
v
alues
computed
at
dif
ferent
time
instances
forms
a
time
series.
From
Equation
(16),
at
time
instant
n
,
time
series
(
C
T
S
)
is:
C
T
S
=
f
C
P
T
1
(
s
l
)
;
C
P
T
2
(
s
l
)
;
:::;
C
P
T
n
(
s
l
)
g
(20)
The
time
series
in
Equation
(20)
is
used
to
predict
the
future
v
alue
of
trust,
termed
as
cumulati
v
e
trust.
Definition
5
Cumulative
T
rust
(CT)
of
a
cloud
service
(
s
l
),
pr
edicted
at
a
time
instant
n
is
defined
as:
C
T
n
(
s
l
)
=
X
n
i
=1
w
0
i
C
P
T
i
(
s
l
)
(21)
wher
e
C
P
T
i
(
s
l
)
is
a
customized
pr
esent
trust
of
cloud
service
(
s
l
)
at
time
instant
i
,
w
0
i
is
a
weight
assigned
to
it
suc
h
that
0
<
w
0
i
<
1
and
P
n
i
=1
w
0
i
=
1
.
C
P
T
v
alues
at
latest
time
instances,
which
represent
recent
quality
of
a
cloud
service,
are
more
rele
v
ant
in
prediction
of
C
T
,
than
the
C
P
T
v
alues
at
prior
time
instances,
which
represent
earlier
quality
of
a
cloud
service.
Hence,
e
xponentially
decreasing
weights
are
assigned
to
the
C
P
T
v
alues,
starting
from
the
latest
C
P
T
v
alue
to
the
C
P
T
v
alues
at
prior
time
instances.
This
is
done
using
a
smoothing
f
actor
such
that
0
<
<
1
.
Thus,
the
v
arious
weights
assigned
to
corresponding
C
P
T
v
alues
are:
w
0
n
=
,
w
0
n
1
=
(1
)
,.
.
.
,
w
0
2
=
(1
)
n
2
and
w
0
1
=
(1
)
n
1
.
It
is
recommended
that
the
v
alue
of
should
be
set
in
the
range
from
0.1
to
0.4.
This
allo
ws
the
predicted
cumulati
v
e
trust
to
match
closely
with
the
computed
customized
present
trust
of
the
service.
5.
ALGORITHM
FOR
ELASTIC
TR
UST
COMPUT
A
TION
Algorithm
1
sho
ws
the
steps
for
elastic
trust
computation
of
a
cloud
service
o
v
er
multiple
time
windo
ws.
The
algorithm
tak
es
a
set
of
cloud
service
attrib
utes,
a
number
of
time
instances
and
the
number
of
time
windo
ws
as
input
for
trust
assessment
of
a
cloud
service.
A
set
of
preferences
tak
en
as
another
input
indicates
the
requirements
of
a
particular
user
about
the
v
alues
of
v
arious
attrib
utes
of
a
cloud
service.
The
algorithm
gi
v
es
the
output
as
sets
of
customized
present
trust
and
cumulati
v
e
trust
v
alues
for
service
s
l
o
v
er
the
time
windo
ws.
The
steps
of
Algorithm
1
for
each
time
windo
w
,
are
e
xplained
as
belo
w
.
Step
1.
(line
7)
The
e
vidence
f
actors
for
the
cloud
service
are
acquired
and
the
resultant
e
vidence
matrix
C
is
formed,
as
sho
wn
in
Equation
(1).
Step
2.
(line
8)
The
preferences
for
service
attrib
utes
are
combined
with
the
e
vidence
matrix,
to
obtain
the
augmented
matrix
C
P
,
as
indicated
in
Equation
(2).
Step
3.
(line
9)
Normalization
function
tak
es
the
augmented
matrix
as
input
and
transforms
all
the
v
alues
in
the
matrix
to
uniform
range
as
specified
by
Equations
(3)
to
(6).
It
results
into
the
normalized
augmented
matrix
N
C
as
gi
v
en
by
Equation
(7).
Step
4.
(line
10)
At
this
point,
the
algorithm
in
v
ok
es
a
function
to
compute
weights
for
v
arious
attrib
utes
of
the
cloud
service.
The
details
of
the
function
to
compute
weights
are
specified
by
Algorithm
2
in
Section
5.1.
Step
5.
(line
11)
Here,
a
function
is
in
v
ok
ed
for
computation
of
distances
for
the
v
arious
attrib
utes
of
the
cl
oud
service,
from
the
specified
preferences
of
attrib
utes.
The
details
of
the
function
to
compute
distances
are
gi
v
en
by
Algorithm
3
in
Section
5.2.
Step
6.
(lines
12
-
17)
At
each
instant
of
time,
computation
of
customized
present
trust
is
performed
based
on
the
IJECE
V
ol.
8,
No.
1,
February
2018:
304
–
325
Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE
ISSN:
2088-8708
313
Algorithm
1
Elastic
T
rust
Computation
for
cloud
service
s
l
1:
Input:
a.
Set
of
m
cloud
service
attrib
utes,
(
AC
)
=
f
R
1
;
R
2
;
:::;
R
m
g
b
.
Number
of
time
instances
(
n
)
c.
User
preferences,
(
P
R
)
=
f
pr
1
;
pr
2
;
:::;
pr
m
g
//
Preferences
for
service
attrib
utes
d.
Number
of
time
windo
ws
for
trust
assessment
(
num
time
windows
)
2:
Output:
a.
Set
of
Customized
Present
T
rust
v
alues
for
service
s
l
,
LP
=
f
C
P
T
[1]
;
C
P
T
[2]
;
:::;
C
P
T
[
n
]
g
b
.
Set
of
Cumulati
v
e
T
rust
v
alues
for
service
s
l
,
LC
=
f
C
T
[1]
;
C
T
[2]
;
:::;
C
T
[
num
timew
indow
s
]
g
3:
Begin
4:
step
=
n
;
5:
j
=
1
;
6:
while
j
num
timew
indow
s
do
7:
Matrix
C
=
Get
e
vidences(
s
l
,A
C,n)
;
8:
Matrix
CP
=
Get
augmat(C,A
C,PR)
;
9:
Matrix
NC
=
Normalize
augmat(CP
,A
C)
;
//
Function
f
N
E
in
Definition
1
10:
Set
W
=
Compute
weights(NC,A
C,n)
;
//
From
Algorithm
2,
W
is
a
set
of
weights
of
m
attrib
utes
11:
Matrix
PN
=
Compute
sumdist(NC,A
C,n,W)
;
//
Matrix
PN
of
summati
v
e
distances
computed
by
Algorithm
3
12:
i
=
1
;
13:
while
i
n
do
14:
Compute
Customized
Present
T
rust
of
service
s
l
at
time
instant
i
as:
C
P
T
[
i
]
=
S
P
i
S
P
i
+
S
N
i
;
//
Function
f
C
P
T
in
Definition
1
and
S
P
i
,
S
N
i
are
elements
of
matrix
PN
15:
Add
C
P
T
[
i
]
in
set
LP
;
16:
i
=
i
+
1
;
17:
end
while
18:
Compute
Cumulati
v
e
T
rust
of
service
s
l
as:
C
T
[
j
]
=
P
n
i
=1
w
0
i
C
P
T
[
i
]
;
//
Function
f
C
T
in
Definition
1,
w
0
i
is
a
weight
assigned
to
C
P
T
[
i
]
19:
Add
C
T
[
j
]
in
set
LC
;
20:
PR
=
Get
updatepr
ef(A
C)
;
21:
n
=
n
+
step
;
22:
j
=
j
+
1
;
23:
end
while
24:
End
summati
v
e
positi
v
e
and
ne
g
ati
v
e
distances
of
e
vidence
f
actors.
The
computed
v
alue
is
added
to
the
output
set
of
cus-
tomized
present
trust
v
alues.
The
details
of
computation
of
customized
present
trust
are
presented
in
Section
4.2.
Step
7.
(lines
18
-
19)
Consequently
,
assessment
of
cumulati
v
e
trust
is
performed
for
the
ne
xt
time
instant
by
using
the
customized
present
trust
v
alues
of
dif
ferent
time
instances
within
a
time
windo
w
.
The
computed
v
alue
is
added
to
the
output
set
of
cumulati
v
e
trust
v
alues.
The
details
of
computation
of
cumulati
v
e
trust
are
elaborated
in
Section
4.5.
Step
8.
(lines
20
-
23)
The
algorithm,
in
v
ok
es
a
function
to
get
the
changes
in
preferences
of
the
particular
user
,
for
the
attrib
utes
of
a
service.
The
number
of
time
instances
for
the
trust
assessment
in
ne
xt
time
windo
w
is
updated.
Accord-
ingly
,
the
algorithm
continues
for
the
reassessment
of
the
trust
of
the
cloud
service
o
v
er
subsequent
time
windo
ws.
Thus,
the
algorithm
reflects
elastic
trust
computation
of
a
cloud
service
according
to
the
dynamically
changing
prefer
-
ences
of
the
user
o
v
er
multiple
time
windo
ws.
5.1.
Algorithm
f
or
Computation
of
W
eights
Algorithm
2
tak
es
a
normalized
augm
ented
matrix,
a
set
of
cloud
service
attrib
utes
and
a
number
of
time
instances
as
input.
The
algorithm
returns
the
set
of
weights
for
the
attrib
utes
of
a
cloud
service,
as
the
ou
t
put.
As
sho
wn
in
the
algorithm,
the
utility
de
gree
for
each
attrib
ute,
is
computed.
From
the
v
alues
of
utility
de
gree,
weight
of
each
attrib
ute
is
computed.
The
details
of
computation
of
weights
are
presented
in
Section
4.3.
5.2.
Algorithm
f
or
Computation
of
Distances
fr
om
Pr
efer
ences
Algorithm
3
tak
es
a
normalized
augmented
matrix,
a
set
of
cloud
service
attrib
utes,
a
number
of
time
instances
and
a
set
of
weights
for
the
attrib
utes
as
input.
It
in
turn,
gi
v
es
a
matrix
P
N
of
summati
v
e
positi
v
e
and
ne
g
ati
v
e
Pr
efer
ences
Based
Customized
T
rust
Model
for
Assessment
...
(Shilpa
Deshpande)
Evaluation Warning : The document was created with Spire.PDF for Python.