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A multi-objective QoS-aware IoT service placement mechanism using Teaching Learning-Based Optimization in the fog computing environment

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Abstract

The huge amount and diversity of data generated by Internet of Things (IoT) devices and the need to store and process this data led to the development of fog computing alongside cloud computing. Fog computing is a new paradigm for providing service at the edge of the network and close to end users, so that it can support real-time IoT applications. Because fog involves heterogeneous and distributed infrastructure with limited resources, so efficient resource allocation to satisfy Quality of Service (QoS) is challenging. IoT application placement mechanisms have been developed to address these issues, in which the subordinate services of these applications are mapped to fog nodes. Despite extensive research to solve the Service Placement Problem (SPP) in fog computing, efforts are still ongoing due to the importance of the issue. Hence, this paper proposes an efficient and autonomous mechanism for solving SPP using Teaching Learning-Based Optimization (TLBO) called SPP-TLBO. SPP-TLBO is a multi-objective QoS-aware algorithm that manages resources on distributed and localized fog domains. In addition to the above, we improve the performance of TLBO by configuring the evolution process with a shared parallel architecture. Besides, SPP-TLBO can save more resources to handle future requests by considering application deadlines and extracting the dynamic distribution of resources required over time. The proposed algorithm is evaluated by simulation on a synthetic fog environment. The simulation results show that SPP-TLBO improves system performance and is between 8 and 19% better efficiency compared to some advanced methods such as CSA-FSPP, FSP-ODMA and, WOA-FSP.

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Data sharing not applicable to this manuscript as no datasets were generated or analyzed during the current study.

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Acknowledgements

This work was supported in part by the Xuzhou Science and Technology Innovation Project under Grant KC19061, in part by Jiangsu Province Modern Education Technology Project under Grant 2021-R-92384, in part by Jiangsu University Foundation funded project (2020SJA1059), and in part by the Xuzhou Medical University Funded Project under Grant 2018KJ01 and Xjy201820.

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Authors and Affiliations

  1. School of Medical Information and Engineering of Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China

    Yan Sha, Hui Wang & Dan Wang

  2. Department of Computer Engineering, Qom Branch, Islamic Azad University, Qom, Iran

    Mostafa Ghobaei-Arani

  3. Production and Recycling of Materials and Energy Research Center, Qom Branch, Islamic Azad University, Qom, Iran

    Mostafa Ghobaei-Arani

Authors
  1. Yan Sha
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  2. Hui Wang
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  3. Dan Wang
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  4. Mostafa Ghobaei-Arani
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Contributions

YS, HW, DW, and MG-A conducted this research. YS: model design, analysis, interpretation, and writing original draft. HW: formal analysis, implementation, and project administration. DW: supervision, conceptualization, and writing review. MG-A: software, methodology, implementation, and writing review.

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Correspondence to Yan Sha, Dan Wang or Mostafa Ghobaei-Arani.

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Sha, Y., Wang, H., Wang, D. et al. A multi-objective QoS-aware IoT service placement mechanism using Teaching Learning-Based Optimization in the fog computing environment. Neural Comput & Applic 36, 3415–3432 (2024). https://doi.org/10.1007/s00521-023-09246-w

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