Data Offloading in Heterogeneous Dynamic Fog Computing Network: A Contextual Bandit Approach

Shan, Yuchen; Wang, Hui; Cao, Zihao; Yury, Kozyrev

doi:10.1109/iccci51764.2021.9486800

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…The study presented in [74] addresses the problem of data offloading in heterogeneous and dynamic fog computingenabled wireless sensor networks. The authors model the data offloading problem as a contextual MAB problem that uses the heterogeneity of sensor nodes (SNs) as contextual information.…”

Section: B Contextual Bandit Learning Based Distributed Computationmentioning

confidence: 99%

Bandit Learning-Based Distributed Computation in Fog Computing Networks: A Survey

Tran-Dang,

Kwon,

Kim

2023

IEEE Access

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Fog computing is a decentralized computing infrastructure that extends the capabilities of cloud computing closer to the edge of the network. In a fog computing network (FCN), computing resources, such as processing power, storage, and networking, are distributed at various points in the network, including edge devices, fog nodes (FNs) such as access points, gateways, and local servers. This architecture allows resource-limited end devices to offload part of their computational tasks to nearby FNs to achieve the reduced response delay services and energy efficiency. However, the high dynamics and complicated heterogeneity of fog computing environment in many application scenarios result in the uncertainty of network information that is raised as a critical challenge to design efficient computation offloading strategies. Meanwhile, existing solutions such as centralized optimization, matching and game theory-based decentralized offloading are inadequate to be adopted in this context because they require the perfect knowledge of system parameters. Considering as a promising approach to deal with the information uncertainty issues, bandit learning has used recently to develop distributed computation offloading (DCO) algorithms for the FCNs. In this paper, we aim at reviewing such of these state-of-the-art DCO solutions and elaborate their advantages and limitations. Additionally, we identify open research challenges and provide future directions for research in this area. Our survey shows that bandit learning is a promising approach for efficient computation offloading in fog computing, and we expect that future research will continue to explore its potential for improving the performance and energy efficiency of fog computing-enabled systems.

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Section: B Contextual Bandit Learning Based Distributed Computationmentioning

confidence: 99%

Bandit Learning-Based Distributed Computation in Fog Computing Networks: A Survey

Tran-Dang,

Kwon,

Kim

2023

IEEE Access

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

Tran-Dang,

Kim

2023

Lecture Notes on Data Engineering and Communications Technologies

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A Computationally Efficient and QoS-Aware Data Offloading Framework for Biased Fog Networks

Shukla,

Sood,

Pandey

2024

IEEE Trans. Circuits Syst. II

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Data Offloading in Heterogeneous Dynamic Fog Computing Network: A Contextual Bandit Approach

Cited by 4 publications

References 17 publications

Bandit Learning-Based Distributed Computation in Fog Computing Networks: A Survey

Bandit Learning-Based Distributed Computation in Fog Computing Networks: A Survey

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

A Computationally Efficient and QoS-Aware Data Offloading Framework for Biased Fog Networks

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