Artificial Intelligence Techniques for Dynamic Offloading in Cloud Continuum Environment: A Review
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-70011-8_38
Reference20 articles.
1. Ale, L., Zhang, N., Fang, X., Chen, X., Shaohua, W., Li, L.: Delay-aware and energy-efficient computation offloading in mobile-edge computing using deep reinforcement learning. IEEE Trans. Cognit. Commun. Netw. 7(3), 881–892 (2021)
2. Alli, A.A., Alam, M.M.: Secoff-fciot: machine learning based secure offloading in fog-cloud of things for smart city applications. Internet of Things 7, 100070 (2019)
3. Chen, J., Zongling, W.: Dynamic computation offloading with energy harvesting devices: a graph-based deep reinforcement learning approach. IEEE Commun. Lett. 25(9), 2968–2972 (2021)
4. di Martino, B., Pezzullo, G.J., Low, W., Ljungberg, P., Saha, S.: Survey on reference architecture for cloud continuum and multi-access edge computing (MEC) in 5G networks. In: AINA-2024, vol. 5, pp. 141–150. Springer, Cham (2024). https://doi.org/10.1007/978-3-031-57931-8_14
5. Eom, H., Figueiredo, R., Cai, H., Zhang, Y., Huang, G.: Malmos: machine learning-based mobile offloading scheduler with online training. In: 2015 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, pp. 51–60. IEEE (2015)
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