Dynamic Resource Allocation on the Edge: A Causal and Contextually-Aware Machine Learning Approach
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-66336-9_21
Reference23 articles.
1. Al Qassem, L.M., Stouraitis, T., Damiani, E., Elfadel, I.A.M.: Proactive random-forest autoscaler for microservice resource allocation. IEEE Access 11, 2570–2585 (2023)
2. Chen, Y., Liu, Z., Zhang, Y., Yuan, W., Chen, X., Zhao, L.: Deep reinforcement learning-based dynamic resource management for mobile edge computing in industrial internet of things. IEEE Trans. Industr. Inf. 17(7), 4925–4934 (2020)
3. Deng, X., Li, J., Shi, L., Wei, Z., Zhou, X., Yuan, J.: Wireless powered mobile edge computing: Dynamic resource allocation and throughput maximization. IEEE Trans. Mob. Comput. 21(6), 2271–2288 (2020)
4. Gong, C., He, W., Wang, T., Gani, A., Qi, H.: Dynamic resource allocation scheme for mobile edge computing. J. Supercomput., 1–21 (2023)
5. Kaur, I., Lydia, E.L., Nassa, V.K., Shrestha, B., Nebhen, J., Malebary, S., Joshi, G.P.: Generative adversarial networks with quantum optimization model for mobile edge computing in IoT big data. Wirel. Personal Commun., 1–21 (2021)
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