Ranking services for composition

Hong Qing Yu (Harry)

Service composition “Composition of Web services has

received much interest to support business-to-business or enterprise application integration.” [1]

Static

Dynamic

[2]

Issues for composition Global services registration Service search/discovery Understanding composition

requirements Service selection Workflow generation Service invoking

Ranking problem for selection If there are more than two services

satisfying functional requirements, Which one is best to use? Cheapest one Fastest one Best performance Other non-functional properties. Logic Scoring preference is a technique

can help us.

Logic scoring preference Traditional Scoring Techniques are simpleE=W1E1+W2E2+…+WnEn, 0 ≤ E ≤ 1. There is a problem [4]It is regardless of the level of importance, thecontribution of component Ei to the global

scoreis limited to Wi

LSP (Logic Scoring preference)

Logic scoring preference Differences are r & W

E=(W1Er1+W2Er

2+…+WnErn)1/r, 0 ≤ E ≤ 1,

W1+W2+…+Wn=1, Wi>0, i=1,2,…,n.

r is a real number selected to achieve the desired logical properties of the aggregation function

Logic scoring preference

[4] [5]

Ranking by composition context

: is an European project

The meaning of context in the project

Context affects service selection

We need a simpler way to define r

Designing evaluation rules

E=(W1Er1+W2Er

2+…+WnErn)1/r, 0 ≤ E ≤ 1,

W1+W2+…+Wn=1, Wi>0, i=1,2,…,n.

1. Filtering rules2. Evaluation function 3. r selection

Filtering rules

Cost<$35 Speed>30/s Quality>85

Irreplaceable preference criteria

Replaceable preference criteria

If the service’s properties do not achieve the irreplaceable preference, then it will be filtered out.

Evaluation function

Exact match Es=1 (if the criteria is matched) or 0 (if is not matched)

Set overlap Es=(e1+e2+…+ei) /i (with Ei being a score for each criteria)

Level match if i is the number of levels and ic is current service level value, then we define: Es=ic/i

Evaluation function

Specific value if vx is the maximum value of all relevant services in one criteria, vn is the minimum value and vi is the current service value, then we calculate:

r selection

E=W1E1+W2E2+...WnEn

Can we compute the weight for choosing the r instead of using the way introduced in [5].

On the one hand, Filter makes all aspects criteria is replaceable, which means that we need conjunction.

On the other hand, if the weight of each criterion are so difference, we also need disjunction.

r selection rules We are in a very balanced position, and

we can narrow our r selection tables

To simplify defining the r value, we just select 1.5, 1, 0.5.

If (highest weight – lowest weight)>average weight, then r=1.5 If (highest weight – lowest weight)<average weight, then r=0.5 If (highest weight – lowest weight)=average weight, then r=1

Example

Worked Example Criterion requirement:1. More people’s weight=0.62. Quality’s weight=0.33. Cost’s weight=-0.1 The resultEskype=(2/3)1.5·0.6+(2/3)1.5·0.3+11.5·0.1=0.590

Etalkfly=11.5·0.6 +(1/3)1.5·0.3+0=0.658

Ehotmail =11.5·0.6+11.5·0.3+(0.6)1.5·0.1=0.946

References

1. http://www.zurich.ibm.com/pdf/ebizz/icaps-ws.pdf

2. http://www.active-endpoints.com/open-source-tutorial.htm

3. http://www.isi.edu/~thakkar/icaps2003-p4ws.pdf

4. http://citeseer.ist.psu.edu/cache/papers/cs/2874/http:zSzzSzcs.sfsu.eduzSzpeoplezSzjozozSzlsp.pdf/a-method-for-evaluation.pdf

5. “Continuous Preference Logic for System Evaluation”, Jozo J. Dujmovic, USA

Thanks

Questions