Adaptive Checkpointing Method for Intermittent Computing Systems

Energy harvesting system is used to power Internet-of-Things (IoT) devices by collecting energy from the environment, allowing these devices to operate without being limited by battery life. Due to weak and unstable energy output, these devices often experience frequent power failures, leading to non-progress issues where running programs lose data and must restart from the beginning. Intermittent computing systems have been proposed to address this by saving system states through checkpoints and resuming from them after a reboot. Currently, two different checkpointing methods are widely adopted: periodic and just-in-time (JIT). The periodic method conducts checkpoints frequently and indiscriminately, without regard to the remaining energy level. This approach often results in a large number of unnecessary backups and lead to significant energy waste. In contrast, the JIT method performs checkpoints precisely when a power failure is imminent by continuously monitoring the current energy level. However, this monitoring process also consumes a significant amount of energy. To leverage the advantages of both checkpointing methods while addressing their shortcomings, we propose an adaptive checkpoint system which selects the most suitable method during runtime based on the current energy status and task execution conditions. Experimental results indi-cate that our system can achieve an average speed improvement of 1.7 times compared to using only a single method.