Transcript Document

A Method for Runtime Service Selection
Hong Qing Yu
Internal seminar (18/10/2007)
Department of Computer Science
Outline
 Web service selection problems
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Competable definition
Runtime service selection
Multicriteria aggregation methods
LSP method
OWA operators
A modified LSP method for service selection
Future work plan
Service selection problems based on mulitcriteria
 When are the services competable?
 What is the most significant difference between
design time service selection and run time
selection?
 How can we measure the individual criterion of
each service?
 How can we aggregate the muilticriteria to get
final evaluation result for each comparable
service?
Run time competable definition
Definition 1:
Services are comparable in run time, iff their
input, output, precondition, effect (IOPE) are
comparable.
Input comparable: Iservice

Output comparable: Oservice
Irequirement

Orequirement
Precondition comparable: Pservice = Prequirement
Effect comparable: Eservice = Erequirement
Run time competable definition
 Example

Input = {departure time, return time, name}
Output = {Reference number}
Precondition = {Registered, login}
Postcondition = {Ticket is blocked}
Input = {departure time, return time}
Output = {Reference number, price}
Precondition = {Registered, login}
Postcondition = {Ticket is blocked}
Input = {departure time, return time}
Output = {Reference number}
Precondition = {Registered, login}
Postcondition = {Ticket is blocked}
Runtime service selection
 Runtime service selection:
Automatic selecting a best suitable service based
on desired Non-functional criteria for
dynamically service composition. (If there are
comparable services)
 For example
Flight
Booking
Register
Payment
Hotel
Booking
Runtime service selection
 Non-functional properties/QoS includes:
[IBM]
[More]
Time consuming
Availability
Location
Accessibility
Language
Integrity
Devices supporting
Performance
…
Reliability
Regulatory
Security
Cost
MultiCriteria aggregation methods
 Arithmetric aggregation
 Geometric aggregation
MultiCriteria aggregation methods
 LSP aggregation
LSP method - orness
 Orness degree (d) depends on what kind of
aggregation function M(x)
 0.5<d<1 : replaceability
 0<d<0.5 : simultaneity
 0<d<1/3 : mandatory
LSP method - example
Integrity
0.5
Cost
0.2
Reputation
0.6
0.45
0.36
Without r
0.9
0
0.3
0.66
0.7
0.2
0.1
2
0.4
0.5
0.1
0.39
0.2
0.47
0.33
With r
0.76
0.03
OWA: a fuzzy set operator
 Definition: An OWA operator of dimension n is a
mapping F : Rn -> R, that has an associated n
vector W = (w1, w2, …wn) T such as wi
[0, 1];
1  i  n, and W = (w1+w2+…+wn = 1).
 F(a1, a2, … an) = w1b1+w2b2+…+wnbn
 bj is the j-th largest element of the bag
<a1, a2, … an >.
OWA - example
 For example, assume W = [0.4, 0.3, 0.2, 0.1] ,
F(0.7,1, 0.3, 0.6) = (0.4)(1)+(0.3)(0.7)+(0.2)(0.6)+(0.1)(0.3)=0.76.
 A fundamental aspect of this operator is the reordering step, an aggregate ai is not associated
with a particular weight wi but rather a weight is
associated with a particular ordered position of
aggregate
OWA - orness
orness
1 n 1

( n  i ) i

n  1 i 1
 This orness measurement function can be proved equal
to Fodor’s orness measurement function, when OWA
operator is applied.
Combining OWA operator with LSP
Integrity
Cost
Reputation
0.5
0.2
0.6
0.9
0
0.3
0.7
0.2
0.1
0.4
0.5
0.1
(0.6, 0.5, 0.2)
(0.9, 03, 0)
(0.1, 0.7, 0.2)w
(0.7, 0.1, 0.2)w
Orness (d)=(0.45+0.75)/2 = 0.600
(0.6, 0.5, 0.2)
(0.9, 03, 0)
(0.1, 0.4, 0.5)w
(0.4, 0.1, 0.5)w
Orness (d)=(0.30+0.45)/2 = 0.375
(0.1*2+0.7)/2=0.45
(0.7*2+0.1)/2=0.75
r ≈ 2.0
(0.1*2+0.4)/2=0.3
(0.4*2+0.1)/2=0.45
r ≈ 0.2
Dujmovic’s LSP method
 Dujmovic’s LSP method includes five major steps:
1. Specifying evaluation criteria (manually)
2. Defining evaluation methods for each criterion
(manually)
3. Orness degree analysis (manually)
4. Local aggregation and global aggregation (manually)
5. Cost/benefit analysis (manually)
A modified LSP method for service selection

Our proposed the modified LSP method for service
selection has four major steps:
1. Specifying evaluation criteria for a group services which
are in the same services category (manually)
2. A unified type-based evaluation methods are defined for
all kinds of criteria (automatically)
3. OWA combining degree analysis/decision (automatically)
4. Aggregating soft criteria and hard criteria to get final
result (automatically/statically)
A modified LSP method for service selection
1. Service selection concept model
Criterion
Category
-criteria
-name
-description
1
*
-name
-type
-weight
-value
*
1
-criterion
1
-data
*
MetaData
-data
Operation
-name
1
-operations
*
-service
1
Service
-name
-endpoint
-description
*
-url
-attribute
-value
0<W<1, bigger evaluation value is desired (soft)
-1<W<0, smaller evaluation value is desired (soft)
W=1, the criterion is hard requirement
Relevance engine
2. Type-based evaluation methods
  vmax  v
1  
  vmax  vmin
E
  vmax  v 
  vmax  vmin 
1
E
0

 iff W  0

(1) Value metric
otherwise
if criteria is met
(2) Boolean metric
otherwise
E  e1  e2  ...  en  / n
with ei being a score for each
element of the set
(3) Set metric
A modified LSP method for service selection
 Automatic orness analysis and calculation:
W = (w1, w2, … wn)
F1 = (a11, a21, … a1n)->W1’->d1
F2 = (a21, a22, … a2n)->W2’->d2
…
Fn = (an1, an2, … ann)->Wn’->dn
d1  d 2...  ...dn
Orness(d)=
n
A modified LSP method for service selection
 Aggregation
n
L = ( ω1 E1r + ω2 E2r +  + ωn Enr )1/ r with 0  E  1,  ωi  = 1
i=1
0
0
>0
0
>0
Conclusion
 Web service selection problems






Competable definition
Runtime service selection
Multicriteria aggregation methods
LSP method
OWA operators
A modified LSP method for service selection
Future work plan
 Complexity analysis
 Implementation and evaluation
Questions