This was the presentation given at the International Conference on Software Architecture (ICSA 2019), Hamburg, Germany based on our paper on "Machine Learning Driven proactive Decision-making approach for adaptive Architectures". The paper was accepted as a part of the New and Emerging Ideas Track (NEMI). The complete presentation of the paper can be found here : https://www.researchgate.net/publication/331374199_A_Machine_Learningdriven_Approach_for_Proactive_Decision_Making_in_Adaptive_Architectures

1. A Machine Learning-Driven Approach for Proactive
Decision Making in Adaptive Architectures
International Conference on Software Architecture (ICSA), Hamburg,
Germany 2019 (NEMI Track)
Henry Muccini, Karthik Vaidhyanathan
henry.muccini@univaq.it,karthik.vaidhyanathan@gssi.it
28 March 2019

2. Presentation Outline
1 Introduction
2 IoTArchML
3 Preliminary Results
4 Conclusions and Future Works
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3. The World of IoT
The Internet of Things (IoT) is the network of physical objects that
contain embedded technology to communicate and sense or interact
with their internal states or the external environment - Gartner
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4. The World of IoT
The Internet of Things (IoT) is the network of physical objects that
contain embedded technology to communicate and sense or interact
with their internal states or the external environment - Gartner
Figure: Apple Watch Series 4 (Released Today !!!)
1
https://www.repubblica.it/
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5. The World of IoT
The Internet of Things (IoT) is the network of physical objects that
contain embedded technology to communicate and sense or interact
with their internal states or the external environment - Gartner
2
https://www.cpacanada.ca/en/news/world/2019-02-13-internet-of-things-infographic
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6. IoT Architecting : Challenges and Possibilities
Figure: IoT System Architecture
1
A. Taivalsaari and T. Mikkonen, A Roadmap to the Programmable World: Software Challenges in the IoT Era, IEEE
Software, February 2017
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7. IoT Architecting : Challenges and Possibilities
Challenges in Architecting
• Heterogeneity
• QoS Assurances
• Interoperability
• Security
• ...
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8. IoT Architecting : Challenges and Possibilities
Challenges in Architecting
• Heterogeneity
• QoS Assurances
• Interoperability
• Security
• ...
Possible Solution
• Self-adaptive architectures - When and How ?
• Use QoS data and system data to aid adaptation - How ?
• Machine learning can be used to leverage the data - How and What
types ?
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9. IoT Architecting : Challenges and Possibilities
Challenges in Architecting
• Heterogeneity
• QoS Assurances
• Interoperability
• Security
• ...
Possible Solution
• Self-adaptive architectures - When and How ?
• Use QoS data and system data to aid adaptation - How ?
• Machine learning can be used to leverage the data - How and What
types ?
Henry and Karthik A Machine Learning-Driven Approach for Proactive Decision Making in Adaptive Architectures4 / 21

10. IoT Architecting : Challenges and Possibilities
Challenges in Architecting
• Heterogeneity
• QoS Assurances
• Interoperability
• Security
• ...
Possible Solution
• Self-adaptive architectures - When and How ?
• Use QoS data and system data to aid adaptation - How ?
• Machine learning can be used to leverage the data - How and What
types ?
Henry and Karthik A Machine Learning-Driven Approach for Proactive Decision Making in Adaptive Architectures4 / 21

11. IoT Architecting : Challenges and Possibilities
Challenges in Architecting
• Heterogeneity
• QoS Assurances
• Interoperability
• Security
• ...
Possible Solution
• Self-adaptive architectures - When and How ?
• Use QoS data and system data to aid adaptation - How ?
• Machine learning can be used to leverage the data - How and What
types ?
Henry and Karthik A Machine Learning-Driven Approach for Proactive Decision Making in Adaptive Architectures4 / 21

12. Presentation Outline
1 Introduction
2 IoTArchML
3 Preliminary Results
4 Conclusions and Future Works
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13. Machine Learning
Machine Learning Definition
Machine Learning is a process where a program/process is said to learn
from Experience E, with respect to some class of Task T, and
Performance measure P, if its performance at its Task T measured by
performance P, improves with experience E
1
Mitchell, Thomas M, Machine Learning, McGraw-Hill, Inc., New York, NY, USA, 1997
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14. IoTArchML
What we Propose ?
• Proactive identification of the need for adaptation through
time-series forecasts
• Adaptation decision making using machine learning techniques and
strategy verification using a model checker
• Continuous learning to improve decision making through constant
feedbacks
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15. IoTArchML
Learnability of Software Architecture
Given a task T of solving an architectural problem, given the architecture
A, of the IoT system and a set of quality attributes that models the
QoS Q of the system, the architecture A of the system improves with
Experience E, such that QoS Q of the system is not compromised
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16. IoTArchML
Figure: IoTArchML Overview
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17. IoTArchML
Figure: IoTArchML Overview
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18. Machine Learning Engine
Figure: Machine Learning Engine
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19. Machine Learning Engine
Figure: Machine Learning Engine
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20. Machine Learning Engine
LSTM Network
• Belongs to the class of Recurrent Neural Network (Sequence
Prediction Problems)
• Uses the QoS aggregated time series data
• LSTM can handle the context based prediction effectively compared
to RNN’s
• Consists of input layer, hidden layer and output layer
• Consists of memory blocks which consists of gates and cells.
• Cell is regulated using input, output and forget gates
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21. Machine Learning Engine
Figure: RNN Network
1
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
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22. Machine Learning Engine
Figure: Machine Learning Engine
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23. IoTArchML: Machine Learning Engine
1
• Example states, S = {high energy, high data traffic, etc }
actions, T = {reduce frequency, change component, etc}
• QoS increase/decrease predicted at time t + 1 is considered as the
reward for the decision taken at time t
Q(st, at) = (1 − α) ∗ Q(st, at) + α ∗ (rt + γ ∗ max(Q(st + 1, a)))
where, 0 ≤ α ≤ 1 represents the learning rate, 0 ≤ γ ≤ 1 represents
the discount factor, rt represents the reward for the action chosen at
step t
1
https://deepsense.ai/playing-atari-on-ram-with-deep-q-learning/
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24. IoTArchML: Machine Learning Engine
Figure: Architecture in a Grid
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25. IoTArchML: Machine Learning Engine
Figure: Machine Learning EngineHenry and Karthik A Machine Learning-Driven Approach for Proactive Decision Making in Adaptive Architectures10 / 21

26. Presentation Outline
1 Introduction
2 IoTArchML
3 Preliminary Results
4 Conclusions and Future Works
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27. NdR Case Study
• European researcher’s night at L’Aquila
• Researchers present their work to the general public
• Around 20,000 visitors
• Late hours have more crowd than early hours
• Whether influences the visitors preferences
• Visitors are unable to locate the availability of parking spaces
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28. NdR Case Study
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29. NdR Case Study
• Automated management of parking lots and venues through battery
powered sensors
• Goal : Reduce energy consumption
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30. Implementation Overview
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31. Total Energy Forecast
• Multi-variate time series data with features = (# of components *
lag) + # of components
• RMSE value of 0.85 for 10 minute forecast
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32. Total Data Traffic Forecast
• Multi-variate time series data with features = lag + 1
• RMSE value of 88.89 for 10 minute forecast
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33. View of Kibana Dashboard
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34. Presentation Outline
1 Introduction
2 IoTArchML
3 Preliminary Results
4 Conclusions and Future Works
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35. Conclusions and Future Works
Conclusions
• Introduced an approach that monitors the QoS values
• Pro-actively Forecasts the possible QoS deviations
• Considers a list of alternate solutions and selects the best solution
• Verifies the decision using model checker and feedbacks
Future Works
• Concrete implementation of the approach by applying on a case study
• Adding fallback mechanisms and application on sensor failure
scenarios
• Decision making by considering trade-offs between multiple QoS
parameters
• Applications on real run-time architectures
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36. Conclusions and Future Works
Conclusions
• Introduced an approach that monitors the QoS values
• Pro-actively Forecasts the possible QoS deviations
• Considers a list of alternate solutions and selects the best solution
• Verifies the decision using model checker and feedbacks
Future Works
• Concrete implementation of the approach by applying on a case study
• Adding fallback mechanisms and application on sensor failure
scenarios
• Decision making by considering trade-offs between multiple QoS
parameters
• Applications on real run-time architectures
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37. Thank you for your attention !
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38. The CAPS Tool
1
H. Muccini and M. Sharaf, CAPS: A Tool for Architecting Situational-Aware Cyber-Physical Systems, IEEE International
Conference on Software Architecture Workshops (ICSAW), 2017
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39. LSTM Cell
Figure: LSTM Cell
1
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
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