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Bandit Learning for Distributed Task Offloading in Fog Computing Networks: Literature Review, Challenges, and Open Research Issues

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Advances in Networked-based Information Systems (NBiS 2023)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 183))

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Abstract

This paper provides an overview of distributed task offloading approaches in fog computing using bandit learning. Task offloading is a pivotal technique that allows mobile devices to delegate part of their computational tasks to nearby fog nodes, thus improving the system performance in terms of service delay and energy consumption. However, efficient computation offloading is challenging due to the dynamic and heterogeneous nature of the fog environment. This paper reviews the state-of-the-art techniques for distributed computation offloading using bandit learning and highlight their advantages and limitations. Additionally, we identify open research challenges and provide future directions for research in this area.

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Acknowledgements

This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program (IITP-2023-2020-0-01612) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation)”, and the National Research Foundation of Korea (NRF) funded by MSIT (RS-2023-00249687).

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Authors and Affiliations

  1. Kumoh National Institute of Technology, Gumi-si, Korea

    Hoa Tran-Dang & Dong-Seong Kim

Authors
  1. Hoa Tran-Dang
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  2. Dong-Seong Kim
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Correspondence to Hoa Tran-Dang .

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Editors and Affiliations

  1. Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

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Tran-Dang, H., Kim, DS. (2023). Bandit Learning for Distributed Task Offloading in Fog Computing Networks: Literature Review, Challenges, and Open Research Issues. In: Barolli, L. (eds) Advances in Networked-based Information Systems. NBiS 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 183. Springer, Cham. https://doi.org/10.1007/978-3-031-40978-3_21

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