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.

I. Introduction

Nowadays, many industrial control systems are real-time programs mainly implemented on Programmable Logic Controllers (PLCs). In real-time control systems, performance-related requirements such as timing requirements for response time is a highly important aspect required to be analyzed, tested and guaranteed more rigorously than other types of software systems. One of the basic language elements of the PLC-based industrial control programs are function blocks. According to the programming languages standards of programmable controllers, a control program may consist of zero or more function blocks. Many of the main types of function blocks mostly work based on timers, therefore, timing requirements in terms of response time or time-out are essential parts of the performance-related requirements in real-time control programs.