Industrial IoT Technologies and Applications. 4th EAI International Conference, Industrial IoT 2020, Virtual Event, December 11, 2020, Proceedings

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
Ming-Tuo Zhou1, Tian-Feng Ren1, Zhi-Ming Dai1, Xin-Yu Feng1
  • 1: Chinese Academy of Sciences

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.