Q-learning Based Backup for Energy Harvesting Powered Embedded Systems
Wei Fan, Yujie Zhang, Weining Song, Mengying Zhaoa, Zhaoyan Shen and Zhiping Jia
School of Computer Science and Technology, Shandong University, Qingdao, China
azhaomengying@sdu.edu.cn
ABSTRACT
Non-volatile processors (NVPs) are used in energy harvesting powered embedded systems to preserve data across interruptions. In NVP systems, volatile data are backed up to non-volatile memory upon power failures and resumed after power comes back. Traditionally, backup is triggered immediately when energy warning occurs. However, it is also possible to more aggressively utilize the residual energy for program execution to improve forward progress. In this work, we propose a Q-learning based backup strategy to achieve maximal forward progress in energy harvesting powered intermittent embedded systems. The experimental results show an average of 307.4% and 43.4% improved forward progress compared with traditional instant backup and the most related work, respectively.
Keywords: Energy harvesting system, Non-volatile processor, Q-learning, Forward progress.
