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Activity Update. WP4 Meeting Bologna – 29.07.2003 Simone Ludwig Electronic and Computer Engineering Department Brunel University / PPARC. Outline. Recent Work Basic Service Discovery Prototype Performance Measurements Ontology Design Rule-based Engine Planned/Ongoing Work
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Activity Update WP4 Meeting Bologna – 29.07.2003 Simone Ludwig Electronic and Computer Engineering Department Brunel University / PPARC
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
Architecture of Semantic Service Discovery Prototype Input/Output Process Resources HEP Applic. Onotology Service Request User Inter-face Context Selection DAML+ OIL Parser Grid Service Ontology Semantic Selection Inference Engine (JESS) Set of rules Registry Selection Service Registry (UDDI) Service Response Resource Ontology Matchmaking Engine DataTAG WP 4 Meeting, Bologna
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
Performance Measurement Setup • 3 different approaches • Centralised • Decentralised • Hybrid DataTAG WP 4 Meeting, Bologna
Global Registry VO2 VO1 VO3 Centralised Approach DataTAG WP 4 Meeting, Bologna
Measurements for CSD DataTAG WP 4 Meeting, Bologna
Local Registry Local Registry Local Registry RSDB RSDB RSDB VO1 VO2 VO3 Or chain model Decentralised Approach DataTAG WP 4 Meeting, Bologna
Measurements for DSD DataTAG WP 4 Meeting, Bologna
VO1 VO2 VO3 Global Registry Local Registry Local Registry Local Registry Hybrid Approach DataTAG WP 4 Meeting, Bologna
Measurements for HSD DataTAG WP 4 Meeting, Bologna
Comparison DataTAG WP 4 Meeting, Bologna
CSD DSD HSD Admini- stration Easy Moredifficult More difficult Easy More complex More complex Manage-ment Security Easy More complex More complex Scalability Not good Good Good Perform-ance / SDT Limited Good Good Reliability Lowest Medium Highest Results DataTAG WP 4 Meeting, Bologna
Ontology Design • Ontology Tool: Protégé • Application: HEP application use cases • Extraction of use cases -> ontology • -> HEP application ontology DataTAG WP 4 Meeting, Bologna
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
Semantic Matchmaking Module DataTAG WP 4 Meeting, Bologna
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
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
Time Outline Perfor-mance Measure-ments Inte-gration of semant. Part with basic SDP Resource Ontology (RO) Basic SD Prototype Ontology Design Inte-gration with RO Similarity Matching September October August June July November December May DataTAG WP 4 Meeting, Bologna