Semantic Service Discovery Prototype DataTAG Activity Update WP4 Meeting Bologna – Simone Ludwig Electronic and Computer Engineering Department Brunel University / PPARC

DataTAG WP 4 Meeting, Bologna2 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, Bologna3 Architecture of Semantic Service Discovery Prototype Matchmaking Engine Service Request Input/Output Process Resources User Inter- face Service Registry (UDDI) Grid Service Ontology Service Response DAML+ OIL Parser DAML+ OIL Parser Inference Engine (JESS) Semantic Selection Set of rules Set of rules Resource Ontology Registry Selection Context Selection HEP Applic. Onotolog y

DataTAG WP 4 Meeting, Bologna4 Basic Service Discovery Prototype  Implementation of the basic service discovery prototype –OGSA-based XML SOAP WSDL UDDI  GUI:

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9 Performance Measurement Setup  3 different approaches –Centralised –Decentralised –Hybrid

DataTAG WP 4 Meeting, Bologna10 Centralised Approach

DataTAG WP 4 Meeting, Bologna11 Measurements for CSD

DataTAG WP 4 Meeting, Bologna12 Decentralised Approach Local Registry RSDB Or chain model VO1 VO2VO3

DataTAG WP 4 Meeting, Bologna13 Measurements for DSD

DataTAG WP 4 Meeting, Bologna14 Hybrid Approach Global Registry Local Registry VO2 VO1 VO3

DataTAG WP 4 Meeting, Bologna15 Measurements for HSD

DataTAG WP 4 Meeting, Bologna16 Comparison

DataTAG WP 4 Meeting, Bologna17 Results CSDDSDHSD Admini- stration EasyMore difficult Manage- ment EasyMore complex SecurityEasyMore complex ScalabilityNot goodGood Perform- ance / SDT LimitedGood ReliabilityLowestMediumHighest

DataTAG WP 4 Meeting, Bologna18 Ontology Design  Ontology Tool: Protégé  Application: HEP application use cases  Extraction of use cases -> ontology  -> HEP application ontology

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DataTAG WP 4 Meeting, Bologna20 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, Bologna21 Semantic Matchmaking Module

DataTAG WP 4 Meeting, Bologna22 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, Bologna23 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, Bologna24 Time Outline May JuneJuly August SeptemberOctober 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

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