Learning-Based Self-Adaptive Assurance of Timing Properties in a Real-Time Embedded System

Providing an adaptive runtime assurance technique to meet the performance requirements of a real-time system without the need for a precise model could be a challenge. Adaptive performance assurance based on monitoring the status of timing properties can bring more robustness to the underlying platform. At the same time, the results or the achieved policy of this adaptive procedure could be used as feedback to update the initial model, and consequently for producing proper test cases. Reinforcement-learning has been considered as a promising adaptive technique for assuring the satisfaction of the performance properties of software-intensive systems in recent years. In this work-in-progress paper, we propose an adaptive runtime timing assurance procedure based on reinforcement learning to satisfy the performance requirements in terms of response time. The timing control problem is formulated as a Markov Decision Process and the details of applying the proposed learning-based timing assurance technique are described.