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.
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This work was supported by the Science and Technology Commission of Shanghai Municipality under Grant 18511106500.
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Zhou, MT., Ren, TF., Dai, ZM., Feng, XY. (2021). Real-Time Task Scheduling in Smart Factories Employing Fog Computing. In: Peñalver, L., Parra, L. (eds) Industrial IoT Technologies and Applications. Industrial IoT 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 365. Springer, Cham. https://doi.org/10.1007/978-3-030-71061-3_2
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DOI: https://doi.org/10.1007/978-3-030-71061-3_2
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