Ring-DVFS: Reliability-Aware Reinforcement Learning-Based DVFS for Real-Time Embedded Systems

Abstract

Dynamic voltage and frequency scaling (DVFS) is one of the most popular and exploited techniques to reduce power consumption in multicore embedded systems. However, this technique might lead to a task-reliability degradation because scaling the voltage and frequency increases the fault rate and the worst-case execution time of the tasks. In order to preserve task-reliability at an acceptable level as well as achieving power saving, in this letter, we have proposed an enhanced DVFS method based on reinforcement learning to reduce the power consumption of sporadic tasks at runtime in multicore embedded systems without task-reliability degradation. The reinforcement learner takes decisions based on the power savings and task-reliability variations due to DVFS and considers the suitable voltage-frequency level for all tasks such that the timing constraints are met. Experimental evaluation was done on different configurations and with different numbers of tasks to investigate the efficiency of the proposed method. Our experiments show that our proposed method works efficiently than other existing works for reducing power consumption without reliability degradation and deadline misses.