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Reputation-based Semantic Service Discovery – Cardiff UniversitySlide No. 1
Reputation-based Semantic Service Discovery
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ETNGRID 2004Presented on 14th June 2004
Slide No. 2
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Motivating ExampleThe Research ProblemThe Traditional ApproachesA better Approach
FrameworkFramework OverviewMatchmaker and Service ComposerReputation Management
ConclusionsFuture Studies
Reputation-based Semantic Service Discovery – Cardiff UniversitySlide No. 3
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� Mr Screen Bean is looking for a reliable Toyota Saloon Car selling
Service.
Reputation-based Semantic Service Discovery – Cardiff UniversitySlide No. 4
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S1 S2 S3
Sells Toyota Cars
Sells Toyota Cars
Sells Saloons Cars
Reputation-based Semantic Service Discovery – Cardiff UniversitySlide No. 5
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� Use of UDDI
UDDI
FindService
ServiceNameCategory
services
<find_service generic="2.0" xmlns="urn:uddi-org:api_v2"><name>ToyotaCarSellingService</name><categoryBag>
<keyedReference tModelKey=“21525-25365-2589-2“keyName=“automobile" keyValue=“car" />
</categoryBag></find_service>
Limitations
S1(ToytaCarService)+
S2S1(ToytaCarService)
?
? UDDI cannot help automatically locate services based on service capabilities and behaviours (i.e. Trust).
Reputation-based Semantic Service Discovery – Cardiff UniversitySlide No. 6
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� Use of Semantic Matchmaking
Matchmaker
FindService
Ontologyservices
Limitations
<profile:Profile rdf:ID="RequestToyotaSellService"><input><profile:ParameterDescription rdf:ID="Price_Input"><profile:parameterName>Price</profile:parameterName"><profile:restrictedTo rdf:resource="Concepts.daml#Price"\></profile:ParameterDescription></input><output><profile:ParameterDescriotion rdf:ID="Car_Output"><profile:parameterName>ToyotaSaloon</profile:parameterName"><profile:restrictedTo rdf:resource="Vehicle.daml#ToyotaSaloon"\></profile:ParameterDescription></output></profile:Profile>
S1(ToytaCarService)+
S2 (ToytaCarService)+
S3(SaloonCarService)
Matchmakers cannot help automatically locate services based on service behaviours (i.e. Trust).
?
Reputation-based Semantic Service Discovery – Cardiff UniversitySlide No. 7
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� A better approach would enable users to:
� Easily and efficiently discovered a reputable service that is more suitable to user’s needs.
� Focus on the conceptual basis of their experiments rather than understanding the low level details of locating services.
� Easy create and share high quality complex workflows.
Reputation-based Semantic Service Discovery – Cardiff UniversitySlide No. 8
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Grid computing efforts adopt Web services technologies, i.e. Web Services Resource Framework. Our approach is relevant for deployment with WSRF.
Slide No. 9
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Motivating Example� The Research Problem� The Traditional Approaches� A better Approach
FrameworkFramework OverviewMatchmaker and Service ComposerReputation Management
ConclusionsFuture Studies
Reputation-based Semantic Service Discovery – Cardiff UniversitySlide No. 10
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Discovery MangerService
MatchmakerService
ComposerService
Reputation MangerService
ServiceRepository Rulebase
Reputation-based Semantic Service Discovery – Cardiff UniversitySlide No. 11
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� Compares service request with service advertisements.
� Ensures the reputation metrics of the advertised service meet the
requirements of the request.
� Implementation is based on the Paolucci’s algorithm.
M. Paolucci, T.Kawamura, T.Payne, and K.Sycare. “Semantic matching of Web services capabilities”, Processding of the 1st
International Conference on Semantic Web
Reference
Reputation-based Semantic Service Discovery – Cardiff UniversitySlide No. 12
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Plug-in MatchI/O of Advert
and Request “similar”
Request
AdvertS = (0,1)
class subsumption
Exact MatchI/O of Advert
and Request match
Advert
Request
S=(0,1)Data TypeMatching
Reputation MetricsMatching
+
Paolucci’s algorithm overview
Reputation-based Semantic Service Discovery – Cardiff UniversitySlide No. 13
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� Discovery Manager Service (DMS) requests the Service Composer
(SC) if the Matchmaker is unable to retrieve a service.
� SC puts together combination of services that can provide the
required functionality and match the requested reputation metric.
� CS uses a dynamic adaptive algorithm using two different sources
of information:
�Rule base: CS queries a rule base to retrieve a rule which can
provide a composition template.
�Chaining Services: CS attempts to create a chain of services
that when put together can fulfil the user objective.
Reputation-based Semantic Service Discovery – Cardiff UniversitySlide No. 14
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� Rule base: CS queries a rule base to retrieve a rule which can
provide a composition template.
� CS attempts to semantically match the inputs and outputs of each
element in the template with services in the repository.
� If matching does not succeed, CS attempts to find another rule that can
decompose the template further (recursively).
� CS will then query the service repository to ascertain if any service
match the rule.
� Services are connected together into a workflow graph based on the
control constructs specified in the rule.
Reputation-based Semantic Service Discovery – Cardiff UniversitySlide No. 15
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� What constitute “good” reputation is a subjective criterion.
� Users may want services that have good reputation rating in
multiple contexts
� Contexts: accessibility, or reliability (or both)
� Three phases are involved in computing the reputation of a service:
1. Reputation Interrogation Phase (RIP).
2. Reputation Rating Phase (RRP).
3. Reputation Computation Phase (RCP).
Reputation-based Semantic Service Discovery – Cardiff UniversitySlide No. 16
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� Chaining Services: CS attempts to create a chain of services that when put
together can fulfill the user objective.
Algorithm
�For each service available, find a service that matches the output of
the service requested. Let one such service be Sn.
�Ascertain the input of Sn. Find a service that can generate the input
for Sn. Let this service be S(n -1)
�This process is iterated until the input of the service S(n-x) matches
the input of the service requested.
�Create the workflow which specifies the order of execution of the
components S1 to Sn.
Reputation-based Semantic Service Discovery – Cardiff UniversitySlide No. 17
� A user requests service reputation from a RMS.
� The reputation request can either be
� A request for the overall reputation score of
a service
� The reputation score of a service within a
particular context
� The aggregation of a set of contexts.
� A user rates a service based on his observations about the service capability.
� The rating is then published to the RMS.
� Relying on the service users to provide feedback to themselves – unlink the P2P reputation mechanisms.
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� Three Phases are
involved:
� RIP
� RRP
� RCP
� RMS computes the reputation of a service by evaluating several ratings from other users that interacted with the service in the past.
Reputation-based Semantic Service Discovery – Cardiff UniversitySlide No. 18
Rating the Availability of a service.
� A user sends a service request to invoke a particular service.
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service
�The service is off-line.�The request is rejected because of high system workload or a system fault.RMS
�The user sends feedback to the RMS. �The feedback is either 0 or 1
Reputation-based Semantic Service Discovery – Cardiff UniversitySlide No. 19
Rating the Reliability of a service.
� A user sends a service request to invoke a particular service.
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Service Negotiate SLA
SLA
SLA establishedInvoking the servicebased on SLASLA Violation
�SLA violation implies that the service was not executed successfully.RMS
�The user sends feedback to the RMS. �The feedback is one the following values: { -2, -1, 0, 1, 2}
Reputation-based Semantic Service Discovery – Cardiff UniversitySlide No. 20
Rating the Reliability of a service (cont..).
� A service user rates service behaviour by examining the terms in the SLA
with his observation during service execution.
� As users cannot monitor the service execution directly, users compute the
estimated execution time test.
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�t = tgen - test
Time Difference
Actual
Execution
Time
Estimated Time
Reputation-based Semantic Service Discovery – Cardiff UniversitySlide No. 21
Rating the Reliability of a service (cont..).
� The user evaluates his perception abut the value of t and sends a rating to
RMS.
� Rating must be a natural number between [-2, 2].
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Not very Reliable-1
Unreliable-2
No evaluation0
Reasonable1
Reliable2
MeaningValue
Reputation-based Semantic Service Discovery – Cardiff UniversitySlide No. 22
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� Two types of service are supported:
� Atomic – executed by a single service provider.
� Composite – combined response from multiple providers.
Generating reputation metrics for atomic services
� RMS receives a reputation interrogation about a particular list of services.
� The request message contains the context in which the user is interested.
� The reputation score of a service within a particular context is computed as the
average rating of the ratings:
NcsR N
csrv�=),(
),(
Reputation-based Semantic Service Discovery – Cardiff UniversitySlide No. 23
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� The reputation score of a service within multiple contexts is computed as
the weighted sum of the reputation score of each context:
),(*)(1�
=
=n
iii csRsR αReputation of service
s within all contexts
The weight attachedto a particular context
�The weight of each context reflects its importance to a particular set of users.
�Each time a user interrogate the reputation of a service within a particular
context, the weight of that context is increased.
Reputation-based Semantic Service Discovery – Cardiff UniversitySlide No. 24
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The Decay Function
� The reputation is associated with a service decays with time.
� A damping function is introduced.
� To compute the decay function R(s,c)new , we evaluate how long ago a
particular rating was generated:
dcsRcsR oldnew
1*),(),( =
Interval between present time and t
Reputation-based Semantic Service Discovery – Cardiff UniversitySlide No. 25
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Generating reputation metrics for composite services.
� CS composes services if the MSS is unable to retrieve a matching service.
� The composite service is constructed from several services with different
reputation scores.
� Four different structures to compose services.
Reputation-based Semantic Service Discovery – Cardiff UniversitySlide No. 26
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� Four different structure to compose services:
A B C
(a) Sequence Structure (b) loop Structure
A B C
AB
D
(c) Parallel Structure
CA
BD
(d) Condition Structure
C
+
-
Reputation-based Semantic Service Discovery – Cardiff UniversitySlide No. 27
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� Four different structure to compose services:
A B C
(a) Sequence Structure (b) loop Structure
A B C
AB
D
(c) Parallel Structure
CA
BD
(d) Condition Structure
C
+
-
Lemma: If the reputation of A within context c is rv(a,c) and the reputation of B within context c is rv(b,c), and the reputation of A is independent of the reputation of B, and the composite service C = A + B is composed as a sequence structure, then the reputation for the composite service C is defined by: rv(a,c) * rv(b,c)
),(0
∏=
N
iicsR
Slide No. 28
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Motivating Example� The Research Problem� The Traditional Approaches� A better Approach
Framework� Framework Overview� Matchmaker and Service Composer� Reputation Management
Conclusions� Future Studies
Reputation-based Semantic Service Discovery – Cardiff UniversitySlide No. 29
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� The content of the SLA.
� Trusted Monitoring Service. (Third Party).
� Identify the relationship between the reputation and QoS.
� Implementation of the future approach.