Hybrid DVFS Scheduling for Real-Time Systems Based on Reinforcement Learning

Power consumption is one of the most challenging issues in the design of modern computing systems. In any computational device, processor consumes significant amount of power compared with other components. In order to reduce power consumption, dynamic voltage and frequency scaling (DVFS) has been commonly used in modern processors. In recent years, there has been much research on real-time DVFS techniques. These techniques work with different strategies and perform well under different conditions. However, a single algorithm is not always optimal under different workloads, dynamic slacks, and power settings. Furthermore, the variation in device configuration also affects the suitability of a given DVFS algorithm. Aiming for adaptability, in this paper, we propose a novel reinforcement learning-based approach, which takes a set of existing techniques, specialized to handle different conditions, and switches to the most suitable one in various situations. Experimental results show that the proposed hybrid approach saves more energy than any single policy executing individually.