A Framework for Learning Based DVFS Technique Selection and Frequency Scaling for Multi-core Real-Time Systems

Multi-core processors have become very popular in recent years due to the higher throughput and lower energy consumption compared with unicore processors. They are widely used in portable devices and real-time systems. Despite of enormous prospective, limited battery capacity restricts their potential and hence, improving the system level energy management is still a major research area. In order to reduce the energy consumption, dynamic voltage and frequency scaling (DVFS) has been commonly used in modern processors. Previously, we have used reinforcement learning to scale voltage and frequency based on the task execution characteristics. We have also designed learning based method to choose a suitable DVFS technique to execute at different states. In this paper, we propose a generalized framework which integrates these two approaches for real-time systems on multi-core processors. The framework is generalized in a sense that it can work with different scheduling policies and existing DVFS techniques.