Computation Offloading Method for Large-Scale Factory Access in Edge-Edge Collaboration Mode

Computation Offloading Method for Large-Scale Factory Access in Edge-Edge Collaboration Mode

Junfeng Man (Hunan First Normal University, China), Longqian Zhao (Nanjing University of Aeronautics and Astronautics, China), Bowen Xu (Virginia Polytechnic Institute and State University, USA), Cheng Peng (Hunan University of Technology, China), Junjie Jiang (Hunan University of Technology, China), and Yi Liu (Hunan University of Technology, China)
Copyright: © 2023 |Pages: 29
DOI: 10.4018/JDM.318451
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Large-scale manufacturing enterprises have complex business processes in their production workshops, and the edge-edge collaborative business model cannot adapt to the traditional computation offloading methods, which leads to the problem of load imbalance. For this problem, a computation offloading algorithm based on edge-edge collaboration mode for large-scale factory access is proposed, called the edge and edge collaborative computation offloading (EECCO) algorithm. First, the method partitions the directed acyclic graphs (DAGs) on edge server and terminal industrial equipment, then updates the tasks using a synchronization policy based on set theory to improve the accuracy effectively, and finally achieves load balancing through processor allocation. The experimental results show that the method shortens the processing time by improving computational resource utilization and employs a heterogeneous distributed system to achieve high computing performance when processing large-scale task sets.
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Industrial cloud-edge collaboration is the trending networking paradigm involving seamless connectivity in heterogeneous industrial environments to aggregate distributed industrial devices into a shared resource pool and mobilizes these industrial devices for local manufacturing. In the edge-side cloud collaborative computing in industrial environments, information flows are a dynamic exchange process that includes industrial device-to-network, industrial device-to-infrastructure, and industrial device-to-device to enable collaborative data sensing and information analysis (Cai et al., 2022; Mustafa et al., 2022; Xu & Zhu, 2022).

The purpose of computation offloading is to obtain the best system throughput and high-performance computing. Traditional computation offloading algorithms have limitations in the processing and analysis of high-dimensional and high-precision data. The minimum completion time algorithm, for example, significantly improves the total task completion rate, but the resources computed by the task scheduler and the maintenance cost of task information are high. The particle swarm optimization algorithm can achieve better cloud computing results, but this algorithm’s main disadvantage is that it does not consider the user budget. Market-driven computation offloading algorithms feature low cost and short response times but lack scalability and long completion time. Tasks in industrial scenarios have the characteristics of multiplicity, multimodality, and small batches. For example, in the sheet metal industry, the factory focuses on the automatic cutting, punching, forming, shearing, and nesting processes for sheet metal parts. Usually, there are 20 to 200 jobs available for scheduling the sheet metal industry’s production queue. Therefore, it is necessary to realize multi-site collaboration and multi-task parallel in the upstream and downstream of the work chain to realize the factory floor’s intelligent integrated application. In the coming decade, more and more factories adopting the industry 4.0 model will develop worldwide. At the same time, along with the current situation of increasing task types and task sizes, there is an urgent need to give a computation offloading method for large-scale factory access in the edge-edge collaboration mode, to improve the efficiency of task execution effectively, scientifically reduce the procurement funds of enterprises, and avoid the waste of limited resources, which is the focus of this research and the problem to be solved in this paper (Materwala et al., 2022; Sheikh Sofla et al., 2022; Sun et al., 2022; Zhang et al., 2022).

This paper presents a reliable solution for collaborative operation scenarios between edge-side servers and cloud-side servers deployed in large factories. This paper proposes a task scheduling method for large-scale factory access under cloud collaborative computing architecture by learning the previous research results to solve the above problems. The main contributions of this paper are as follows:

  • 1.

    A task division method in the edge-edge collaboration mode is proposed to solve computation offloading’s difficulties due to the variability in practical scenarios.

  • 2.

    A computation offloading method for large-scale factory access in edge-edge collaboration mode is proposed to solve high response delays caused by the increase of terminal industrial devices.

  • 3.

    The simulation results verify the feasibility of the architecture in real industrial scenarios.

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