Digital Catapult Centre Brighton - Dr Nour Ali
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1 Mobile and Self-Adaptive Ambients in Service Oriented Architecture Dr Nour Ali School of Computing, Engineering and Mathematics 2nd of March, 2016 [email protected]
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Transcript of Digital Catapult Centre Brighton - Dr Nour Ali
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Mobile and Self-Adaptive Ambients in Service Oriented Architecture
Dr Nour AliSchool of Computing, Engineering and Mathematics
2nd of March, 2016
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COLLABORATORS
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INTERNET OF THINGS ARCHITECTURE
Smart Objects (Things)
End UserNetwork
WirelessInternet Cloud
Services
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CONTENTS
Modelling Internet of Things Applications Automatic Code Generation and Deployment Self-Adaptation to Services and Resources Conclusions and Further Work
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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
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An ambient is a place, delimited by a boundary, where computation happens.
Examples of ambients are: Devices CarData packetsFirewallsNetworksA Building or an airplane
AMBIENT CALCULUSCardelli and Gordon, 1998
m
n
in m PQ
R
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AMBIENT-SERVICE ORIENTED RUNTIME META-MODEL
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AMBIENT RUNTIME MODEL
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SMART HOME
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Device Ambients: Smart TV Sensors Alarm
Mobile ambients: Car Mobile Device that represents the human.
Location Ambients House Rooms
AMBIENTS
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MODELLING MOBILE AMBIENTS IN SMART HOMES
House
GarageReception Room
Living RoomMobile Device
enter
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MODELLING MOBILE AMBIENTS IN SMART HOMES
House
GarageReception Room
Living RoomMobile Device
enter
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MODELLING MOBILE AMBIENTS IN SMART HOMES
House
GarageReception Room
Living RoomMobile Device
enter
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AMBIENTS IN SERVICE ORIENTED ARCHITECTURE
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ENTERING MOBILITY SERVICE CONTRACT
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CONTENTS
Modelling Internet of Things Applications Automatic Code Generation and Deployment Self-Adaptation to Services and Resources Conclusions and Further Work
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To model service oriented architecture of distributed and mobile systems.
automatically generate and execute them at runtime.
AUTOMATIC CODE GENERATION AND DEPLOYMENT
TransformationsATL
Declarative Languages for OSGIModelling Tool
EMF/GMF
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CONTENTS
Modelling Internet of Things Applications Automatic Code Generation and Deployment Self-Adaptation to Services and Resources Conclusions and Further Work
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MOTIVATION: ADAPTATION TO RESOURCES
How can we self-adapt at runtime to resources?
InternetOr Cloud services
Home Cinema
CPU, battery,
etc
InternetOr Cloud services
CPU, battery,
etc
NOT ALL APPLICATIONS AND SERVICES HAVE THE SAME PRIORITY
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OUR APPROACH
InternetOr Cloud
ARCHITECTURAL MODEL @ RUNTIME
SYSTEM @ RUNTIME
Planning Mobile Adaptation +
Architectural Metamodel
Discrete Swarm Optimization Algorithm
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AUTONOMIC AMBIENTS
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AMBIENT-SERVICE ORIENTED RUNTIME META-MODEL
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Mobile DEVICE BEFORE ENTERING CINEMA
SCENARIO
enter
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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 50Friends Service 70 50 10
Restaurant Service 50 30 10
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MOBILE USER INTERFACE FOR ALGORITHM
Total No of Resources, Services and Apps
No of IterationsName of Service and the Utility of Service
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MOBILE USER INTERFACE FOR ALGORITHM
Services and their Dependencies
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OUTPUT
4G[0]WLAN[1]Health App [1]VideoService [1]Friends Service[1]
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MOBILE DEVICE IN CINEMA
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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.
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25% of the battery
EVALUATION ON MOBILE DEVICE
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CONTENTS
Modelling Internet of Things Applications Automatic Code Generation and Deployment Self-Adaptation to Services and Resources Conclusions and Further Work
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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
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QUESTIONS?
Dr. Nour Ali
Principal Lecturer in Software EngineeringUniversity of Brighton
Home page: www.cem.brighton.ac.uk/staff/na179/ Email: [email protected]
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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, Soli s, 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 Soli s, Carlos (2010) Ambient-PRISMA: ambients in mobile aspect-oriented software architecture Journal of Systems and Software, 83 (6). ISSN 0164-1212
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