Transcript Ranking method for services composition

Ranking services for composition
Hong Qing Yu (Harry)
Service composition
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“Composition of Web services has received
much interest to support business-to-business
or enterprise application integration.” [1]
Static
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Dynamic
[2]
Issues for composition
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Global services registration
Service search/discovery
Understanding composition requirements
Service selection
Workflow generation
Service invoking
Ranking problem for selection
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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 simple
E=W1E1+W2E2+…+WnEn, 0 ≤ E ≤ 1.
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There is a problem [4]
It is regardless of the level of importance, the
contribution of component Ei to the global score
is limited to Wi
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LSP (Logic Scoring preference)
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Logic scoring preference
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Differences are r & W
E=(W1Er1+W2Er2+…+WnErn)1/r, 0 ≤ E ≤ 1,
W1+W2+…+Wn=1, Wi>0, i=1,2,…,n.
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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
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The meaning of context in the project
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Context affects service selection
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We need a simpler way to define r
Designing evaluation rules
E=(W1Er1+W2Er2+…+WnErn)1/r, 0 ≤ E ≤ 1,
W1+W2+…+Wn=1, Wi>0, i=1,2,…,n.
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3.
Filtering rules
Evaluation function
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
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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
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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
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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
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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=0.6
2.
Quality’s weight=0.3
3.
Cost’s weight=-0.1
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The result
Eskype=(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
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References
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http://www.zurich.ibm.com/pdf/ebizz/icaps-ws.pdf
http://www.active-endpoints.com/open-sourcetutorial.htm
http://www.isi.edu/~thakkar/icaps2003-p4ws.pdf
http://citeseer.ist.psu.edu/cache/papers/cs/2874/http:zSzzS
zcs.sfsu.eduzSzpeoplezSzjozozSzlsp.pdf/a-method-forevaluation.pdf
“Continuous Preference Logic for System Evaluation”,
Jozo J. Dujmovic, USA
Thanks
Questions