1. Community Manager A Dynamic Collaboration Solution on Heterogeneous Environment 2006.06.26 Hyeonsook Kim virtus78@ajou.ac.kr  2006 CUS. All rights reserved. 21C R&D Project ICPS ‘06

2. 2 Agenda  Introduction  Our Approach  Experiments  Conclusion & Future Work

3. 3 Agenda  Introduction - Motivation - Community Computing - Related Works  Our Approach  Experiments  Conclusion & Future Work  References

4. 4 Introduction/ Motivation * Problems 1. Heterogeneity 2. Mobility 3. Adaptation  How can we reduce the complexity of collaboration service development in the pervasive computing environment? Pervasive Computing Environment In distributed environment, services and computing devices have to collaborate autonomously and continually to achieve a goal  High level heterogeneity of device, service, communication protocol, network and etc.  High level dynamism and unpredictability  Intelligent environment, Computationally enhanced environment  Services adapting to user intention and Environment change

5. 5 Introduction/ Community Computing Community Computing Abstract Layer Implemental Layer Runtime Adaptation * New Computing Paradigm 1.Abstracted Collaboration Model 2.Model Based Service Description 3.Automatic transformation into real world Image Display ScannerAuthentication  For the consistent collaboration service among the diverse middlewares, services and devices, the service model need to be separated and abstracted from the runtime environment.

6. 6 Introduction/ Community Computing Community Computing An abstract and autonomic collaboration model for pervasive fusion service in pervasive computing environment. 3. Create community 2. Set up goal 5. Learn the results 1. Sense situation 4. Collaborate inter/intra community Community Metaphor

7. 7 Introduction/ Related Works MDA (Model Driven Architecture)  supports a methodology for the automatic transformation of platform independent service model into platform dependent model  has limits for collaboration modeling  cannot support dynamic collaboration service which has to adapt itself to the runtime environment dynamically SWS (Semantic Web Services)  add semantic knowledge to existing web services  discover and composite services dynamically  support just one type service description language  cannot support integration of various domains and environments

8. 8 Agenda  Introduction  Our Approach - Community Model & Description - Community Life Cycle & Member Binding - Community Manager  Experiments  Conclusion & Future Work  References

9. 9 Our Approach/ Community Model & Description Community Model

10. 10 Our Approach/ Community Model & Description CDL (Community Description Language)

11. 11 Our Approach/ Community Instantiation Community Instantiation

12. 12 Our Approach/ Community Life Cycle & Member Binding Community Member Binding on Community Life Cycle  Creation Phase When describing the community, the members are handled with service types which imply meta-services  Organization Phase The members are handled with service names of the services which satisfy service types and constraints in the execution environment.  Execution Phase After a community is activated, the members are handled with service ids of the service instances which run on the real device

13. 13 Our Approach/ Community Manager Community Manager  Situation Generator generates situation message  Community Organizer interpret community template and manage community life cycle  Community Executor discovery and select member instances  Situation Rule Repository  Community Repository  Meta Service Repository

14. 14 Our Approach/ Community Management System Community Interpretation

15. 15 Our Approach/ Community Management System Dynamic Plug-in Middleware Adaptor  MOM (Message Oriented Middleware) Based  Runtime adaptor injection  Spring Framework  JAXB Framework  Dynamic reconfiguration of communication channels (Source, Pipes, Sink)

16. 16 Agenda  Introduction  Our Approach  Experiment - Demo Scenario - A Sample Community Template - Community Viewing  Conclusion & Future Work  References

17. 17 Experiment/ Demo Scenario Scenario Flow

18. 18 Experiment/ A Sample Community Template Notify L2 Community

19. 19 Experiment/ Community Viewing Notify L2 Community  Community Organization  Member Discovery: - Member Type: TEXT_VIEW

20. 20 Agenda  Introduction  Our Approach  Experiment  Conclusion & Future Work - Major Contributions - Future Works  References

21. 21 Conclusion/ Major Contributions Advantages  Ability of collaboration in heterogeneous dimension Adaptive Reconfigurable  Encapsulation (Abstraction) Balancing of transparency and awareness  Reuse Reusing the unit of a community with a goal, members and policies  Dynamic Decision Making Externalized logic  Sharing Context in Community

22. 22 Conclusion/ Future Works Future works  Community Management Policy Based Member Discovery Community Action Evolution by Learning  Member Monitoring Member Defect Detection Automatic Healing  Meta Service Ontology Standardization of Specific Domain Service Interface Service Mapping on Syntax and Semantic Understanding  Stronger Security/Privacy methodology  Inter Community Management

23. 23 Agenda  Introduction  Our Approach  Experiment  Conclusion & Future Work  References - Lists

24. 24 References/ Lists  Puneet Gupta, "Evolving a pervasive IT infrastructure: a technology intefration approach”, Pers Ubiquit Comput 2004, 8: 31  Object Management Group, Model Driven Architecture Guide, 2003  M. Paolucci, K. P. Sycara. Autonomous Semantic Web Services. IEEE Intemet Computing. 7(5): 3441, 2003.  http://www.w3.org/submission/OWL-S  R Sivashanmugam, Framework for Semantic Web Process Composition. Inlemational Journal of Electronic Commerce, 2004  B. Benatallah, Environment for Web Services Composition. IEEE Internet Computing. 17(1):40-48, 2003  J. Michael Yohe, “Community Comuputing and the Computing Community", Proc. of ACM SIGUCCS Conference on User Services, October 1994

25. 25 Thank you!