Authors:
Bakhta Haouari
1
;
2
;
3
;
Rania Mzid
1
;
4
and
Olfa Mosbahi
2
Affiliations:
1
ISI, University Tunis-El Manar, 2 Rue Abourraihan Al Bayrouni, Ariana, Tunisia
;
2
LISI Lab INSAT, University of Carthage, Centre Urbain Nord B.P. 676, Tunis, Tunisia
;
3
Tunisia Polytechnic School, University of Carthage, B.P. 743, La Marsa, Tunisia
;
4
CES Lab ENIS, University of Sfax, B.P:w.3, Sfax, Tunisia
Keyword(s):
Real-Time, Task Placement, Multi-Objective, Refactoring, Pareto Q-Learning.
Abstract:
This paper introduces a novel approach for multi-objective task placement on heterogeneous architectures in real-time embedded systems. The primary objective of task placement is to identify optimal deployment models that assign each task to a processor while considering multiple optimization criteria. Given the NP-hard nature of the task placement problem, various techniques, including Mixed Integer Linear Programming and genetic algorithms, have been traditionally employed for efficient resolution. In this paper, we explore the use of reinforcement learning to solve the task placement problem. We initially modeled this problem as a Markov Decision Process. Then, we leverage the Pareto Q-learning algorithm to approximate Pareto front solutions, balancing system extensibility and energy efficiency. The application of the proposed method to real-world case studies showcases its effectiveness in task placement problem resolution, enabling rapid adaptation to designer adjustments compar
ed to related works.
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