Abstract
With the widespread use of industrial Internet technology in intelligent production lines, the number of task requests generated by smart terminals is growing exponentially. Achieving rapid response to these massive tasks becomes crucial. In this paper we focus on the multi-objective task scheduling problem of intelligent production lines and propose a task scheduling strategy based on task priority. First, we set up a cloud-fog computing architecture for intelligent production lines and built the multi-objective function for task scheduling, which minimizes the service delay and energy consumption of the tasks. In addition, the improved hybrid monarch butterfly optimization and improved ant colony optimization algorithm (HMA) are used to search for the optimal task scheduling scheme. Finally, HMA is evaluated by rigorous simulation experiments, showing that HMA outperformed other algorithms in terms of task completion rate. When the number of nodes exceeds 10, the completion rate of all tasks is greater than 90%, which well meets the real-time requirements of the corresponding tasks in the intelligent production lines. In addition, the algorithm outperforms other algorithms in terms of maximum completion rate and power consumption.
Highlights
Based on the above studies, we find that the task scheduling problem in the cloud-fog computing environment is a research hotspot in the IoT field, and the existing research cannot meet the requirements of low latency and low power consumption for multi-priority task scheduling in intelligent production lines
For the requirement of ultra-low latency, we establish a mathematical model for intelligent production line task scheduling to achieve ultra-low latency and low power consumption of time-sensitive tasks
Conclusions the requirement of ultra-low latency, we establish a mathematical model for intelligent production line task scheduling to achieve ultra-low latency and low power consumption of time-sensitive tasks
Summary
Zhenyu Yin 1,2,3, *,† , Fulong Xu 1,2,3,† , Yue Li 1,2,3 , Chao Fan 1,2,3 , Feiqing Zhang 1,2,3 , Guangjie Han 4,5 and Yuanguo Bi 6,7. Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China. Liaoning Key Laboratory of Domestic Industrial Control Platform Technology on Basic Hardware and Software, Shenyang 110168, China. Changzhou Key Laboratory of Internet of Things Technology for Intelligent River and Lake, Changzhou 213022, China. Engineering Research Center of Security Technology of Complex Network System, Ministry of Education, Shenyang 110167, China.
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