The document proposes a five-layer semantic grid architecture with a knowledge layer on top of the Gridbus broker. The knowledge layer includes two modules: semantic description and discovery. Semantic description represents grid domain knowledge in an ontology template using Protege-OWL APIs. Semantic discovery uses the Algernon inference engine to retrieve resource information based on user queries. This knowledge layer is implemented in the Gridbus broker and can support popular middleware like Globus and Alchemi.

5. Semantic Grid Architecture Resources Includes Supercomputers, clusters Workstations etc., This layer Manages allocation of computational resources, Job Execution, Secure Access to grid resources This layer deals with the way resources are represented, stores, shared and Maintained This layer act as an infrastructure to support the management and application of scientific knowledge to achieve particular types of goal and objective shared and Maintained Distributed Resources Computation Services Layer Data Services Layer Information Services Layer Knowledge Layer

9. A Five Layered Architecture of Semantic Grid Services

12. Resource Ontology Template

13. Semantic Component Resources Registers Query Generator User Resource Description GridBus Broker Job execution Resource Discovery Resource App. Des File Res. Des File Querying OWL file Algernon Query MDS Request A:B User Submit Job Knowledge Base Results To user … Job Descriptor Resource Information Semantic Description

15. Grid Resource Knowledge base

17. Illustration If the user request Unix OS and if it is not available, the discovery Mechanism obtains machines with Linux OS as the Linux concept is modeled as subconcept of Unix. Corresponding Algernon Axiom ((instance RAM ?inst)(hasFreeMB ?inst ?val) (hasOS ?inst “Linux”)(:TEST(:LISP(=?val"+rightTag+"))) (presentInComputer?inst ?instanceComputer)). The Query format for a resource request whose Requirement criteria is RAM:500 and OS:Linux Is “ freeRAM:>500 hasOS:Linux” Similarly,

18. snapshots

19. Protégé Ontology Editor

26. Extension of the work The semantic component with some extension has been Integrated with Garuda, the National Grid Computing Initiative

30. Garuda Semantic Component RULES Inference Engines Matchmaking Algorithm Ontology Knowledge Base Garuda Resources Resource Information MDS Registers Resource Advertisement User Resource Request Specification

32. RULES Neural Network Algorithm Inference Engines Matchmaking Algorithm Ontology Knowledge Base Resources Resource Information MDS Registers Resource Advertisement User Resource Request Specification Further Work planned in Semantic Component Integration of neural network Technology further improve the performance of Semantic Grid characteristics

33. Semantics everywhere…. Web Services Web Services WSRF Services OWL OWL-S OWL OWL RBAC uses descr descr descr Product/ Process Model Business Process Ontology Organisational Ontology Service Ontology Resource Ontology User/Role Authorisations Services System Resources represents represents represents Auth. Service Ontology-Based Virtual User Desktop Distributed run-time environment ref ref uses uses WSDL grid run on ? Courtesy: Global Grid Forum 16 Athens, Greece, February 13-16, 2006

39. Questions

41. Thank You

42. Backup Slides

43. Ontology Framework Web Services Web Services WSRF Services OWL OWL-S OWL OWL RBAC uses descr descr descr Product/ Process Model Business Process Ontology Organisational Ontology Service Ontology Resource Ontology User/Role Authorisations Services System Resources represents represents represents Auth. Service Ontology-Based Virtual User Desktop Distributed run-time environment ref ref uses uses WSDL grid run on ? Courtesy: Global Grid Forum 16 Athens, Greece, February 13-16, 2006

44. Life without Broker Courtesy: University of Melbourne, Gridbus Broker Presentation

45. Life with Broker Courtesy: University of Melbourne, Gridbus Broker Presentation Scheduling ?