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 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 Irequirement

Output comparable: Oservice 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

Register

Flight Booking

Hotel Booking

Payment

Runtime service selection

Non-functional properties/QoS includes:[IBM]AvailabilityAccessibility IntegrityPerformance Reliability Regulatory SecurityCost

[More]Time consumingLocationLanguageDevices supporting…

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 Cost Reputation

0.5 0.2

0.9 0 0.3

0.7 0.2 0.1

0.4 0.5 0.1

0.45 0.36

0.66 0.39

2

0.2

Without r

With r0.03

0.330.47

0.76

0.6

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 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

This orness measurement function can be proved equal to Fodor’s orness measurement function, when OWA operator is applied.

1

1

)(1

1 n

iiorness in

n

Combining OWA operator with LSP

Integrity Cost Reputation

0.5 0.2

0.9 0 0.3

0.7 0.2 0.1

0.4 0.5 0.1

0.6

(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 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

-name-description

Category -name-type-weight-value

Criterion

-name

Operation

1

-criteria

*

*

*

-name-endpoint-description

Service

-service 1

-operations *

-url-attribute-value

MetaData

1

-data

*

-criterion1

-data1

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

otherwisevv

vv

Wiffvv

vv

E

minmax

max

minmax

max 01

otherwise

metiscriteriaifE

0

1

settheofelement

eachforscoreabeingewithneeeE in /...21

(1) Value metric

(2) Boolean metric

(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

Orness(d)= n

dndd ......21

A modified LSP method for service selection

Aggregation

1 1,0 with 1

/12211 =ωE)Eω++Eω+Eω(=L

n

=ii

rrnn

rr

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