Reputation-based Semantic Service Discovery

(1)

Reputation-based Semantic Service Discovery – Cardiff University

Slide No. 1

Reputation-based Semantic Service

Discovery

ETNGRID 2004

(2)

Motivating Example

The Research Problem

The Traditional Approaches

A better Approach

Framework

Framework Overview

Matchmaker and Service Composer

Reputation Management

Conclusions

(3)

Slide No. 3

Mr Screen Bean is looking for a reliable Toyota Saloon Car selling Service.

(4)

S1 S2 S3

Sells

(5)

Reputation-based Semantic Service Discovery – Cardiff University Slide No. 5 Use of UDDI UDDI FindService ServiceName Category 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

(6)

Use of Semantic Matchmaking Matchmaker FindService Ontology services 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)

(7)

Slide No. 7

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.

(8)

Grid computing efforts adopt Web services technologies, i.e. Web Services Resource Framework.

(9)

Slide No. 9

Motivating Example

The Research Problem

The Traditional Approaches

A better Approach

Framework

Framework Overview

Matchmaker and Service Composer

Reputation Management

Conclusions

(10)

!

" #

Discovery Manger Service

Matchmaker

Service ComposerService

Reputation Manger Service

Service

(11)

Slide No. 11

"

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

(12)

"

$

%&

Plug-in Match

I/O of Advert and Request “similar”

Request Advert S = (0,1) class subsumption Exact Match I/O of Advert and Request match

Advert Request S=(0,1) Data Type Matching Reputation Metrics Matching

+

(13)

Slide No. 13

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

(14)

$

%&

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.

(15)

Slide No. 15

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).

(16)

$

%&

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 S_n.

Ascertain the input of S_n. Find a service that can generate the input for S_n. 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.

(17)

Slide 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.

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.

(18)

Rating the Availability of a service.

A user sends a service request to invoke a particular service.

'

service

The service is off-line.

The request is rejected because of high system workload or a system fault.

(19)

Slide No. 19

Rating the Reliability of a service.

A user sends a service request to invoke a particular service.

'

Service

Negotiate SLA

SLA

SLA established Invoking the service

based 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}

(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 t_est.

'

$

%%&

t = t

_gen

- t

_est

Time

(21)

Slide 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].

'

$

%%&

Not very Reliable -1 Unreliable -2 No evaluation 0 Reasonable 1 Reliable 2 Meaning Value

(22)

'

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:

c

s

(23)

Slide No. 23

'

$

%%&

The reputation score of a service within multiple contexts is computed as the weighted sum of the reputation score of each context:

)

,

(

*

)

(

1 =

=

n i i i

c

s

R

s

R

α

Reputation of service

s within all contexts

The weight attached to 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

(24)

'

$

%%&

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:

d

c

s

R

c

s

R

(

,

)

new

=

(

,

)

old

*

1

(25)

Slide No. 25

'

$

%%&

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.

(26)

'

$

%%&

Four different structure to compose services:

A B C

(a) Sequence Structure (b) loop Structure

A B C A B D (c) Parallel Structure C A B D (d) Condition Structure C +

(27)

-Reputation-based Semantic Service Discovery – Cardiff University

Slide No. 27

'

$

%%&

Four different structure to compose services:

A B C

(a) Sequence Structure (b) loop Structure

A B C A B D (c) Parallel Structure C A B D (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

i

c

s

R

(28)

Motivating Example

The Research Problem

The Traditional Approaches

A better Approach

Framework

Framework Overview

Matchmaker and Service Composer

Reputation Management

Conclusions

(29)

Slide No. 29

!

"

The content of the SLA.

Trusted Monitoring Service. (Third Party).

Identify the relationship between the reputation and QoS. Implementation of the future approach.

(30)

! "