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.
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
3. 3
INTERNET OF THINGS ARCHITECTURE
Smart Objects
(Things)
End UserNetwork
Wireless
Internet
Cloud
Services
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
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
Car
Data packets
Firewalls
Networks
A Building or an airplane
AMBIENT CALCULUS
Cardelli and Gordon, 1998
m
n
in m P
Q
R
10. 10
Device Ambients:
Smart TV
Sensors
Alarm
Mobile ambients:
Car
Mobile Device that represents the human.
Location Ambients
House
Rooms
AMBIENTS
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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
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
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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?
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
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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 50
Friends 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 Iterations
Name of Service and the Utility of Service
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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 Engineering
University of Brighton
Home page: www.cem.brighton.ac.uk/staff/na179/
Email: n.ali2@brighton.ac.uk
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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, 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
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