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Digital Catapult Centre Brighton - Dr Nour Ali

At The Digital Catapult Centre Brighton event, Tech Beyond The Screen: Connectivity & Infrastructure on Wednesday 2nd March, Dr Nour Ali from The University of Brighton spoke about mobile and self adaptive ambients in service oriented architecture.

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Digital Catapult Centre Brighton - Dr Nour Ali

  1. 1. 1 Mobile and Self-Adaptive Ambients in Service Oriented Architecture Dr Nour Ali School of Computing, Engineering and Mathematics 2nd of March, 2016 N.ALI2@BRIGHTON.AC.UK
  2. 2. 2 COLLABORATORS
  3. 3. 3 INTERNET OF THINGS ARCHITECTURE Smart Objects (Things) End UserNetwork Wireless Internet Cloud Services
  4. 4. 4 CONTENTS  Modelling Internet of Things Applications  Automatic Code Generation and Deployment  Self-Adaptation to Services and Resources  Conclusions and Further Work
  5. 5. 5  Solves Interoperability problem.  What are the generic building blocks for IoT devices and services? Use models and then generate/configure devices and code to specific platforms. Model independently of the kind of device. MODEL DRIVEN DEVELOPMENT
  6. 6. 6  An ambient is a place, delimited by a boundary, where computation happens.  Examples of ambients are: Devices Car Data packets Firewalls Networks A Building or an airplane AMBIENT CALCULUS Cardelli and Gordon, 1998 m n in m P Q R
  7. 7. 7 AMBIENT-SERVICE ORIENTED RUNTIME META- MODEL
  8. 8. 8 AMBIENT RUNTIME MODEL
  9. 9. 9 SMART HOME
  10. 10. 10  Device Ambients:  Smart TV  Sensors  Alarm  Mobile ambients:  Car  Mobile Device that represents the human.  Location Ambients  House  Rooms AMBIENTS
  11. 11. 11 MODELLING MOBILE AMBIENTS IN SMART HOMES House Garage Reception Room Living Room Mobile Device enter
  12. 12. 12 MODELLING MOBILE AMBIENTS IN SMART HOMES House Garage Reception Room Living Room Mobile Device enter
  13. 13. 13 MODELLING MOBILE AMBIENTS IN SMART HOMES House Garage Reception Room Living Room Mobile Device enter
  14. 14. 14 AMBIENTS IN SERVICE ORIENTED ARCHITECTURE
  15. 15. 15 ENTERING MOBILITY SERVICE CONTRACT 15
  16. 16. 16 CONTENTS  Modelling Internet of Things Applications  Automatic Code Generation and Deployment  Self-Adaptation to Services and Resources  Conclusions and Further Work
  17. 17. 17  To model service oriented architecture of distributed and mobile systems.  automatically generate and execute them at runtime. AUTOMATIC CODE GENERATION AND DEPLOYMENT Transformations ATL Declarative Languages for OSGI Modelling Tool EMF/GMF
  18. 18. 18 CONTENTS  Modelling Internet of Things Applications  Automatic Code Generation and Deployment  Self-Adaptation to Services and Resources  Conclusions and Further Work
  19. 19. 19 MOTIVATION: ADAPTATION TO RESOURCES How can we self-adapt at runtime to resources? Internet Or Cloud services Home Cinema CPU, battery, etc Internet Or Cloud services CPU, battery, etc NOT ALL APPLICATIONS AND SERVICES HAVE THE SAME PRIORITY
  20. 20. 20 OUR APPROACH Internet Or Cloud ARCHITECTURAL MODEL @ RUNTIME SYSTEM @ RUNTIME Planning Mobile Adaptation Architectural Metamodel Discrete Swarm Optimization Algorithm
  21. 21. 21 AUTONOMIC AMBIENTS
  22. 22. 22 AMBIENT-SERVICE ORIENTED RUNTIME META- MODEL
  23. 23. 23  Mobile DEVICE BEFORE ENTERING CINEMA SCENARIO enter
  24. 24. 24  Create Possible Solutions  Calculate Utility Functions resource costs, utility and current value of resource POSSIBLE CANDIDATE SOLUTIONS AND UTILITY FUNCTION } Uf()=0 Battery COST with DATA (mA) BatterY COST WITH WLAN (mA) Utility Health App 70 50 100 VideoStreaming Service 60 60 50 Friends Service 70 50 10 Restaurant Service 50 30 10
  25. 25. 25 MOBILE USER INTERFACE FOR ALGORITHM Total No of Resources, Services and Apps No of Iterations Name of Service and the Utility of Service
  26. 26. 26 MOBILE USER INTERFACE FOR ALGORITHM Services and their Dependencies
  27. 27. 27 OUTPUT 4G[0]WLAN[1]Health App [1]VideoService [1]Friends Service[1]
  28. 28. 28 MOBILE DEVICE IN CINEMA
  29. 29. 29 IMPLEMENTATION AND EVALUATION -The maximum number of iterations to perform is 1000. - We executed the algorithm 10000 When the number of particles increases, the percentage of success increases. The best execution time was 0.99 ms when 25 particles were used, with an average of 46.4 iterations and 96.4% success.
  30. 30. 30  25% of the battery EVALUATION ON MOBILE DEVICE
  31. 31. 31 CONTENTS  Modelling Internet of Things Applications  Automatic Code Generation and Deployment  Self-Adaptation to Services and Resources  Conclusions and Further Work
  32. 32. 32  We use Model Driven Engineering to develop and manage applications in a technology independent way.  We use autonomic computing to allow applications to self-manage.  Further Work:  Developing a tool that includes: architectural modelling visualizations, monitoring, etc  Allow users to change the utility of the resources provided at runtime.  New case studies to apply our work. CONCLUSION AND FURTHER WORK
  33. 33. 33 QUESTIONS? Dr. Nour Ali Principal Lecturer in Software Engineering University of Brighton Home page: www.cem.brighton.ac.uk/staff/na179/ Email: n.ali2@brighton.ac.uk
  34. 34. 34  Ali, Nour and Solis, Carlos (2015) Self-Adaptation to Mobile Resources in Service Oriented Architecture In: 2015 IEEE International Conference on Mobile Services (MS), New York City, NY, USA, 27 June - 2 July 2015.  Ali, Nour and Solis, Carlos (2014) Mobile architectures at runtime: research challenges In: 1st ACM international conference on mobile software and engineering systems (Mobilesoft), Hyderabad, India, 2-3 June 2014.  Ali, Nour, Solis, Carlos and Chen, Fei (2012) Modeling support for Mobile Ambients in Service Oriented Architecture In: 1st international conference on Mobile Services (MS), Honolulu, Hawaii, 24-29 June, 2012.  Ali, Nour, Ramos, I. and Solis, Carlos (2010) Ambient-PRISMA: ambients in mobile aspect-oriented software architecture Journal of Systems and Software, 83 (6). ISSN 0164-1212 SOME PAPERS

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