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A smart collaborative framework for dynamic multi-task offloading in IIoT-MEC networks

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Abstract

The rapid development of Industrial Internet of Things (IIoT) has brought unprecedented opportunities to the industry informatization. However, facing with billions access of IIoT devices, the traditional IIoT architecture based on cloud computing is no longer suitable in terms of flexibility, efficiency and elasticity. Multi-access Edge Computing (MEC) has been seen as a enabling technology to process massive time-sensitive tasks. Meanwhile, the multi-task collaborative offloading is an urgent problem for IIoT-MEC networks. In this paper, a Smart Collaborative Framework (SCF) scheme is designed to achieve dynamic service prediction and make multi-task offloading decisions. First, a theoretical model, including a Hierarchical Spatial-Temporal Monitoring (HSTM) module and a Fine-grained Resource Scheduling (FRS) module, is established. Hybrid deep learning algorithms are applied to the monitoring module from spatial-temporal dimensions. Besides, both mixed game and improved queuing theories are adopted to enhance offloading efficiency in the FRS module. Second, a specific framework and an implementation process are designed for illustrating scheme details. Third, a prototype environment are created with optimal parameter settings. The validation results demonstrated that the SCF scheme can achieve better task awareness, abnormality inference and task offloading compared to other candidate algorithms. The proposed model has enhanced 7.8% and 8.5% in accuracy and detection rate, and optimized the offloading efficiency.

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Acknowledgements

This work was supported in part by the Fundamental Research Funds for Central Universities under Grant 2022RC006, in part by the the National Natural Science Foundation of China under Grant 62201029, and in part by the China Postdoctoral Science Foundation under Grant Grant BX20220029 and 2022M710007.

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Authors and Affiliations

  1. National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing, 100029, China

    Zhengyang Ai, Pengxiao Li & Lei Shi

  2. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China

    Weiting Zhang

  3. College of Computer Science, Chongqing University, Chongqing, China

    Mingyan Li

Authors
  1. Zhengyang Ai
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  2. Weiting Zhang
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  3. Mingyan Li
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  4. Pengxiao Li
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  5. Lei Shi
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Contributions

Zhengyang Ai, Weiting Zhang and Mingyan Li wrote the main manuscript text, Pengxiao Li and Lei Shi prepared the figure 3, figure 4 and figure 5-8. Finally, all authors reviewed the manuscript.

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Correspondence to Weiting Zhang.

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Ai, Z., Zhang, W., Li, M. et al. A smart collaborative framework for dynamic multi-task offloading in IIoT-MEC networks. Peer-to-Peer Netw. Appl. 16, 749–764 (2023). https://doi.org/10.1007/s12083-022-01441-1

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