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A Method for Runtime Service Selection. Hong Qing Yu Internal seminar (18/10/2007) Department of Computer Science. Outline. Web service selection problems Competable definition Runtime service selection Multicriteria aggregation methods LSP method OWA operators

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slide1

A Method for Runtime Service Selection

Hong Qing Yu

Internal seminar (18/10/2007)

Department of Computer Science

outline
Outline
  • 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
service selection problems based on mulitcriteria
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
Run time competable definition

Definition 1:

Services are comparable in run time, iff their input, output, precondition, effect (IOPE) are comparable.

Input comparable: Iservice Irequirement

Output comparable: Oservice Orequirement

Precondition comparable: Pservice = Prequirement

Effect comparable: Eservice = Erequirement

run time competable definition1
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

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 selection1
Runtime service selection

Non-functional properties/QoS includes:

[IBM]

Availability

Accessibility

Integrity

Performance

Reliability

Regulatory

Security

Cost

[More]

Time consuming

Location

Language

Devices supporting

multicriteria aggregation methods
MultiCriteria aggregation methods

Arithmetric aggregation

Geometric aggregation

lsp method orness
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
LSP method - example

Integrity

Reputation

Cost

0.5

0.45

0.2

0.36

0.6

Without r

0.66

0.39

0.9

0

0.3

0.7

2

0.2

0.1

0.1

0.5

0.2

0.4

0.47

0.33

With r

0.03

0.76

owa a fuzzy set operator
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
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 re-ordering 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
OWA - orness
  • This orness measurement function can be proved equal to Fodor’s orness measurement function, when OWA operator is applied.
combining owa operator with lsp
Combining OWA operator with LSP

Integrity

Reputation

Cost

0.5

0.2

0.6

0

0.3

0.9

0.7

0.2

0.1

0.1

0.5

0.4

(0.6, 0.5, 0.2) (0.1, 0.7, 0.2)w (0.1*2+0.7)/2=0.45

(0.9, 03, 0) (0.7, 0.1, 0.2)w (0.7*2+0.1)/2=0.75

Orness (d)=(0.45+0.75)/2 = 0.600 r ≈ 2.0

(0.6, 0.5, 0.2) (0.1, 0.4, 0.5)w(0.1*2+0.4)/2=0.3

(0.9, 03, 0) (0.4, 0.1, 0.5)w (0.4*2+0.1)/2=0.45

Orness (d)=(0.30+0.45)/2 = 0.375 r ≈ 0.2

dujmovic s lsp method
Dujmovic’s LSP method
  • Dujmovic’s LSP method includes five major steps:
  • Specifying evaluation criteria (manually)
  • Defining evaluation methods for each criterion (manually)
  • Orness degree analysis (manually)
  • Local aggregation and global aggregation (manually)
  • Cost/benefit analysis (manually)
a modified lsp method for service selection
A modified LSP method for service selection
  • Our proposed the modified LSP method for service selection has four major steps:
  • Specifying evaluation criteria for a group services which are in the same services category (manually)
  • A unified type-based evaluation methods are defined for all kinds of criteria (automatically)
  • OWA combining degree analysis/decision (automatically)
  • Aggregating soft criteria and hard criteria to get final result (automatically/statically)
a modified lsp method for service selection1
A modified LSP method for service selection
  • Service selection concept model

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
Relevance engine

2. Type-based evaluation methods

(1) Value metric

(2) Boolean metric

(3) Set metric

a modified lsp method for service selection2
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

Orness(d)=

conclusion
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
Future work plan

Complexity analysis

Implementation and evaluation