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Resource allocation problem and artificial intelligence: the state-of-the-art review (2009–2023) and open research challenges

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Abstract

With the increasing growth of information through smart devices, enhancing the quality of human life necessitates the adoption of various computational paradigms including, cloud, fog, and edge in the Internet of Things (IoT) network. Among these paradigms, cloud computing, as an emerging technology, extends cloud layer services to the network edge. This enables resource allocation operations to occur closer to the end user, thereby reducing resource processing time and network traffic overhead. Consequently, the resource allocation problem for cloud service providers, in terms of presenting a suitable platform using computational paradigms, is considered a challenge. Also, Energy Efficiency, Heterogeneity, and Scalability are problems of the cloud computing environment. To solve these problems, resource allocation approaches are divided into two methods: auction-based methods (aimed at increasing profits for service providers while ensuring user satisfaction and usability) and optimization-based methods (focused on energy, cost, network exploitation, runtime, and time delay reduction). Hence, this paper presents a comprehensive literature study (CLS) on artificial intelligence methods such as machine and deep learning for resource allocation optimization in computing environments, such as cloud computing, fog computing, and edge computing. Since deep learning methods are widely used in resource allocation problems, this paper also explores resource allocation approaches based on deep learning techniques, such as deep reinforcement learning, Q-learning, reinforcement learning, and online learning, as well as classical learning methods like Bayesian learning. As a main important achievement, the use of deep reinforcement learning-based methods has increased in the fog paradigm in the past few years.

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Authors and Affiliations

  1. Department of Computer Engineering, Babol Branch, Islamic Azad University, Babol, Iran

    Javad Hassannataj Joloudari

  2. Department of Computer Engineering, Technical and Vocational University (TVU), Tehran, Iran

    Javad Hassannataj Joloudari

  3. Department of Computer Engineering, University of Birjand, Birjand, Iran

    Javad Hassannataj Joloudari & Hamid Saadatfar

  4. Department of Information Technology, Mazandaran University of Science and Technology, Babol, Iran

    Sanaz Mojrian & Sahar Khanjani Shirkharkolaie

  5. Department of Computer Engineering, Qom University of Technology, Qom, Iran

    Issa Nodehi

  6. Department of Electronic Engineering, Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran

    Fatemeh Fazl

  7. Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC, 3216, Australia

    Roohallah Alizadehsani & H. M. Dipu Kabir

  8. National Heart Centre Singapore, Singapore, 169609, Singapore

    Ru-San Tan

  9. Duke-NUS Medical School, Singapore, Singapore

    Ru-San Tan

  10. School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Australia

    U. Rajendra Acharya

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  1. Javad Hassannataj Joloudari
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Appendix

Appendix

Table 4 describes the abbreviations and acronyms related to intelligent computing environments in this study.

Table 4 List of abbreviations related to intelligent computing environments
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Hassannataj Joloudari, J., Mojrian, S., Saadatfar, H. et al. Resource allocation problem and artificial intelligence: the state-of-the-art review (2009–2023) and open research challenges. Multimed Tools Appl 83, 67953–67996 (2024). https://doi.org/10.1007/s11042-024-18123-0

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