Authors:
Amine Chohra
1
;
Felicita Di Giandomenico
2
;
Stefano Porcarelli
3
and
Andrea Bondavalli
4
Affiliations:
1
Images Signals and Intelligent Systems Laboratory and Paris-East University, France
;
2
ISTI and National Research Center, Italy
;
3
University of Pisa, Italy
;
4
University of Florence, Italy
Keyword(s):
Complex and Uncertain Systems, Wireless and Mobile Communication Systems, Analysis and Decision-Making, Deterministic and Stochastic Petri Nets, Database Audit Behaviors, Intelligent Software Agent, Reinforcement Q-Learning and Supervised Gradient Back-Propagation Learning Paradigms, Artificial Neural Networks, Optimal Maintenance Policies.
Related
Ontology
Subjects/Areas/Topics:
Fault-Tolerance and Traffic Reliability Issues
;
Information Ubiquity
;
Network Measurement, Validation and Verification Schemes
;
Performance Analysis of Wireless Networks
;
Telecommunications
;
Wireless and Mobile Technologies
;
Wireless Information Networks and Systems
Abstract:
To enhance wireless and mobile system dependability, audit operations are necessary, to periodically check the database consistency and recover in case of data corruption. Consequently, how to tune the database audit parameters and which operation order and frequency to apply becomes important aspects, to optimize performance and satisfy a certain degree of Quality of Service, over system life-cycle. The aim of this work is then to suggest an intelligent maintenance system based on reinforcement Q-Learning approach, built of a given audit operation set and an audit manager, in order to maximize the performance (performability and unreliability). For this purpose, a methodology, based on deterministic and stochastic Petri nets, to model and analyze the dependability attributes of different scheduled audit strategies is first developed. Afterwards, an intelligent (reinforcement Q-Learning) software agent approach is developed for planning and learning to derive optimal maintenance poli
cies adaptively dealing with the highly dynamic evolution of the environmental conditions. This intelligent approach, is then implemented with feedforward artificial neural networks under the supervised gradient back-propagation learning to guarantee the success even with large state spaces, exploits intelligent behaviors traits (learning, adaptation, generalization, and robustness) to derive optimal actions in different system states in order to achieve an intelligent maintenance system.
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