A cloud edge-based two-level hybrid scheduling learning model in cloud manufacturing

Abstract

In the Industry 4.0, edge industrial services such as smart robotic services are widely used in smart factory. The workflow of these services mainly consists of task decomposition and resource allocation. The long scheduling time, high communication delay and load imbalance among edge nodes are the challenging problems. Traditional cloud manufacturing platforms are difficult to meet the new requirements. It is hard for the existing scheduling methods to maintain a balance between algorithm complexity and performance. Training scheduling data by deep learning has become a feasible method to achieve fast prediction of the scheduling results. In this paper, a cloud edge-based two-level hybrid scheduling learning model is put forward at first. Then an improved bat scheduling algorithm with interference factors and variable step size (VSSBA) is proposed. And then, according to the historical scheduling data, the improved long and short-term memory networks (LSTM) model is put forward for fast prediction of the cloud-edge collaborative scheduling results. Experiments show that our proposed learning model can improve the performance of the cloud manufacturing platform in real-life applications efficiently. Finally, future research issues and challenges are identified.

KEYWORDS:

• Cloud manufacturing
• machine learning
• two-level hybrid scheduling
• LSTM
• bat algorithm

Acknowledgements

This work was supported in part by National Natural Science Foundation of China (grant number 61672461 and 61672463) and Natural Science Foundation of Zhejiang Province, China (Grant No. LY18F020035).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported in part by National Natural Science Foundation of China [No. 61672461 and No. 61672463], and in part by Natural Science Foundation of Zhejiang Province, China [No. LY18F020035].