Cloud-Edge Collaboration in Industrial Internet of Things: A Joint Offloading Scheme based on Resource Prediction
- Publisher:
- Institute of Electrical and Electronics Engineers
- Publication Type:
- Journal Article
- Citation:
- IEEE Internet of Things Journal, 2022, 9, (18), pp. 17014-17025
- Issue Date:
- 2022-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Cloud-Edge Collaboration in Industrial Internet of Things A Joint Offloading Scheme based on Resource Prediction.pdf | Published version | 6 MB |
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With the continuous addition of an abundant of heterogeneous devices, the limitation of task delay has become an obstacle to the development of the Industrial Internet of Things(IIoT). Task offloading based on edge computing can provide low-latency computing services for these tasks. However, in the actual IIoT scenario, contrast to cloud computing, edge computing has limited resources and computing capabilities. Resource-constrained edge resources can’t meet the offloading requirements of massive industrial devices. In this paper, we propose an optimal joint offloading scheme based on resource occupancy prediction for the problem of computing offloading with limited edge resources. The scheme is divided into two parts including edge resource occupancy prediction and task offloading. Simultaneously, considering multitask and the limitations of edge resources, gate recurrent unit is used to predict the occupancy of edge resources. Formulating an optimal strategy of task offloading by using a reinforcement learning algorithm according to the network state and predicted results. The simulation results show that the scheme can effectively reduce the average delay of tasks, while minimizing the task offloading failure rate.
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