Research Article
Real-Time Task Scheduling in Smart Factories Employing Fog Computing
@INPROCEEDINGS{10.1007/978-3-030-71061-3_2, author={Ming-Tuo Zhou and Tian-Feng Ren and Zhi-Ming Dai and Xin-Yu Feng}, title={Real-Time Task Scheduling in Smart Factories Employing Fog Computing}, proceedings={Industrial IoT Technologies and Applications. 4th EAI International Conference, Industrial IoT 2020, Virtual Event, December 11, 2020, Proceedings}, proceedings_a={INDUSTRIALIOT}, year={2021}, month={7}, keywords={Smart factory Fog computing ARQ protocol Genetic algorithm Resource scheduling}, doi={10.1007/978-3-030-71061-3_2} }- Ming-Tuo Zhou
Tian-Feng Ren
Zhi-Ming Dai
Xin-Yu Feng
Year: 2021
Real-Time Task Scheduling in Smart Factories Employing Fog Computing
INDUSTRIALIOT
Springer
DOI: 10.1007/978-3-030-71061-3_2
Abstract
With the development of the new generation of information technology, traditional factories are gradually transforming into smart factories. How to meet the low-latency requirements of task processing in smart factories so as to improve factory production efficiency is still a problem to be studied. For real-time tasks in smart factories, this paper proposes a resource scheduling architecture combined with cloud and fog computing, and establishes a real-time task delay optimization model in smart factories based on the ARQ (Automatic Repeat-request) protocol. For the solution of the optimization model, this paper proposes the GSA-P (Genetic Scheduling Arithmetic With Penalty Function) algorithm to solve the model based on the GSA (Genetic Scheduling Arithmetic) algorithm. Simulation experiments show that when the penalty factor of the GSA-P algorithm is set to 6, the total task processing delay of the GSA-P algorithm is about 80% lower than that of the GSA-R(Genetic Scheduling Arithmetic Reasonable) algorithm, and 66% lower than that of the Joines & Houck method algorithm; In addition, the simulation results show that the combined cloud and fog computing method used in this paper reduces the total task delay by 18% and 7% compared with the traditional cloud computing and pure fog computing methods, respectively.
