Adaptive scheduling strategy of fog computing tasks with different priority for intelligent production lines

Abstract Fog computing has been widely deployed in intelligent production lines to provide real-time computing services for terminal devices to alleviate the transmission problem between cloud and terminal. Nevertheless, when there are many services provided in a single fog node, how to choose an optimal data processing path is a challenge to minimize optimization latency and power consumption while ensuring the reliable transmission of data with different priorities. To address the problems as mentioned above in this paper, we first propose a joint evaluation model of time latency and power consumption based on task priority. Then, we formulate a fog computing adaptive scheduling algorithm (FCAS) based on dynamic programming to obtain the optimal path for processing data with different priorities at fog nodes. Experimental results illustrate that our proposed evaluation model and algorithm have lower power consumption, high efficiency, and higher reliability.