Sem

an

tic S

erv

ice D

iscovery

Pro

toty

pe

Sem

an

tic S

erv

ice D

iscovery

Pro

toty

pe

Data

TA

GActivity UpdateActivity Update

WP4 Meeting Bologna – 29.07.2003

Simone LudwigSimone Ludwig

Electronic and Computer Engineering DepartmentElectronic and Computer Engineering Department

Brunel University / PPARCBrunel University / PPARC

DataTAG WP 4 Meeting, Bologna 2

Outline

Recent Work– Basic Service Discovery Prototype

– Performance Measurements

– Ontology Design

– Rule-based Engine

Planned/Ongoing Work– Integration of the semantic part with the basic service discovery prototype

– Resource Ontology

– Investigation of Similarity Matching

Time Outline

DataTAG WP 4 Meeting, Bologna 3

Architecture of Semantic Service Discovery Prototype

Matchmaking Engine

Service Request

Input/Output Process Resources

User Inter-face

User Inter-face

Service Registry (UDDI)

Service Registry (UDDI)

Grid Service

Ontology

Grid Service

Ontology

Service Response

DAML+OIL

Parser

DAML+OIL

Parser

Inference Engine (JESS)

Inference Engine (JESS)

Semantic Selection

Semantic Selection

Set of rules

Set of rules

Resource Ontology

Resource Ontology

Registry Selection

Registry Selection

Context Selection

Context Selection

HEP Applic.

Onotology

HEP Applic.

Onotology

DataTAG WP 4 Meeting, Bologna 4

Basic Service Discovery Prototype

Implementation of the basic service discovery prototype– OGSA-based

• XML• SOAP• WSDL• UDDI

GUI:http://193.62.142.4:31000/webapp/ServiceDiscoveryJSP/ServiceDiscovery.jsp

DataTAG WP 4 Meeting, Bologna 5

DataTAG WP 4 Meeting, Bologna 6

DataTAG WP 4 Meeting, Bologna 7

DataTAG WP 4 Meeting, Bologna 8

DataTAG WP 4 Meeting, Bologna 9

Performance Measurement Setup

3 different approaches– Centralised

– Decentralised

– Hybrid

DataTAG WP 4 Meeting, Bologna 10

Centralised Approach

Global Registry

VO1VO1 VO2VO2 VO3VO3

DataTAG WP 4 Meeting, Bologna 11

Measurements for CSD

136

137

138

139

140

141

142

143

WS1 WS2 WS3 WS4 WS5 WS6 WS7 WS8 WS9

SD

T /

ms

DataTAG WP 4 Meeting, Bologna 12

Decentralised Approach

Local Registry

Local Registry

Local Registry

RSDB RSDB RSDB

Or chain model

VO1VO1 VO2VO2 VO3VO3

DataTAG WP 4 Meeting, Bologna 13

Measurements for DSD

0

100

200

300

400

500

600

700

WS1 WS2 WS3 WS4 WS5 WS6 WS7 WS8 WS9

SD

T /

ms

DataTAG WP 4 Meeting, Bologna 14

Hybrid Approach

Global Registry

Local Registry

Local Registry

Local Registry

VO2VO2VO1VO1 VO3VO3

DataTAG WP 4 Meeting, Bologna 15

Measurements for HSD

0

100

200

300

400

500

600

Registry 1 Registry 2 Registry 3 Registry 4

SD

T /

ms

Service not found Service found

DataTAG WP 4 Meeting, Bologna 16

Comparison

0

100

200

300

400

500

600

700

CSD DSD HSD

SD

T /

ms

min. max.

DataTAG WP 4 Meeting, Bologna 17

Results

CSD DSD HSD

Admini-stration

Easy More difficult More difficult

Manage-ment

Easy More complex More complex

Security Easy More complex More complex

Scalability Not good Good Good

Perform-ance / SDT

Limited Good Good

Reliability Lowest Medium Highest

DataTAG WP 4 Meeting, Bologna 18

Ontology Design

Ontology Tool: Protégé

Application: HEP application use cases

Extraction of use cases -> ontology

-> HEP application ontology

DataTAG WP 4 Meeting, Bologna 19

DataTAG WP 4 Meeting, Bologna 20

Rule-based Engine

Also called Inference Engine Is a generic control mechanism that applies knowledge

present in the knowledge base (ontology) to task-specific data to arrive at some conclusion.

2 different approaches:– Forward chaining (data-directed inference):

• JRules• JESS

– Backward chaining (goal-directed inference):• Mandarax

DataTAG WP 4 Meeting, Bologna 21

Semantic Matchmaking Module

DataTAG WP 4 Meeting, Bologna 22

Integration

Integration of semantic part with basic service discovery prototype

Prototype will consist of:– Basic Part:

• Web/Grid services• SOAP• WSDL• Service Registry (UDDI)

– Semantic Part:• Context ontologies for the 4 HEP applications (CMS, ATLAS, ALICE, LHCb)• Grid Application Ontology• DAML+OIL Parser• Set of rules• Inference Engine

DataTAG WP 4 Meeting, Bologna 23

Resource Ontology

Extract the concept– Basic Structure of Resources

• CE• SE• WN• RB• UI

– Attributes of each resource element

– Relationship between the resources

Define the resource ontology

DataTAG WP 4 Meeting, Bologna 24

Time Outline

May June July August September October December

Basic SD Prototype

Perfor-mance

Measure-ments

Ontology Design

Inte-gration of semant. Part with basic SDP

Resource Ontology

(RO) Similarity Matching

Inte-gration with RO

November

DataTAG WP 4 Meeting, Bologna 25