Optimizing energy efficiency in MEC networks: a deep learning approach with Cybertwin-driven resource allocation-Reference-Cited by-同舟云学术

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Optimizing energy efficiency in MEC networks: a deep learning approach with Cybertwin-driven resource allocation

Published:2024-08-03 Issue:1 Volume:13 Page:
ISSN:2192-113X
Container-title:Journal of Cloud Computing
language:en
Short-container-title:J Cloud Comp

Author:

Lilhore Umesh Kumar,Simaiya Sarita,Dalal Surjeet,Faujdar Neetu,Alroobaea Roobaea,Alsafyani Majed,Baqasah Abdullah M.,Algarni Sultan

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1186/s13677-024-00688-8.pdf

Reference41 articles.

1. Yu C, Quan W, Gao D, Zhang Y, Liu K, Wu W, Zhang H, Shen X (2021) Reliable cyber twin-driven concurrent multipath transfer with deep reinforcement learning. IEEE Internet Things J 8(22):16207–16218

2. Juneja S, Gahlan M, Dhiman G, Kautish S (2021) Futuristic Cybertwin architecture for 6G technology to support internet of everything. Sci Program 2021:1–7

3. Rodrigues TK, Liu J, Kato N (2021) Application of cyber twin for offloading in mobile multi-access edge computing for 6G networks. IEEE Internet Things J 8(22):16231–16242

4. Violos J, Pagoulatou T, Tsanakas S, Tserpes K, Varvarigou T (2021) Predicting Resource Usage in Edge Computing Infrastructures with CNN and a Hybrid Bayesian Particle Swarm Hyper-parameter Optimization Model. https://doi.org/10.1007/978-3-030-80126-7_40.

5. Du R, Liu C, Gao Y, Hao P, Wang Z (2022) Collaborative cloud-edge-end task offloading in NOMA-enabled mobile edge computing using deep learning. J Grid Comput 20(2):14

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