2023, Volume 22, Issue 3
Engineering >> 2023, Volume 22, Issue 3 doi: 10.1016/j.eng.2021.08.022
Flexible Resource Scheduling for Software-Defined Cloud Manufacturing with Edge Computing
a School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100191, China
b School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100191, China
c College of Mathematics and Computer Science, Yan’an University, Yan’an 716000, China
d School of Economics and Management, University of Chinese Academy of Science, Beijing 100190, China
e Department of Production Engineering, KTH Royal Institute of Technology, Stockholm 10044, Sweden
f State Key Lab of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
g Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong 999077, China
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Abstract
This research focuses on the realization of rapid reconfiguration in a cloud manufacturing environment to enable flexible resource scheduling, fulfill the resource potential and respond to various changes. Therefore, this paper first proposes a new cloud and software-defined networking (SDN)-based manufacturing model named software-defined cloud manufacturing (SDCM), which transfers the control logic from automation hard resources to the software. This shift is of significance because the software can function as the “brain” of the manufacturing system and can be easily changed or updated to support fast system reconfiguration, operation, and evolution. Subsequently, edge computing is introduced to complement the cloud with computation and storage capabilities near the end things. Another key issue is to manage the critical network congestion caused by the transmission of a large amount of Internet of Things (IoT) data with different quality of service (QoS) values such as latency. Based on the virtualization and flexible networking ability of the SDCM, we formalize the time-sensitive data traffic control problem of a set of complex manufacturing tasks, considering subtask allocation and data routing path selection. To solve this optimization problem, an approach integrating the genetic algorithm (GA), Dijkstra’s shortest path algorithm, and a queuing algorithm is proposed. Results of experiments show that the proposed method can efficiently prevent network congestion and reduce the total communication latency in the SDCM.
Keywords
Cloud manufacturing ; Edge computing ; Software-defined networks ; Industrial internet of things ; Industry 4.0
SupplementaryMaterials
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