A cloud–edge–device collaborative offloading scheme with heterogeneous tasks and its performance evaluation
Author:
Publisher
Zhejiang University Press
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing
Link
https://link.springer.com/content/pdf/10.1631/FITEE.2300128.pdf
Reference44 articles.
1. Ai LH, Tan B, Zhang JD, et al., 2023. Dynamic offloading strategy for delay-sensitive task in mobile-edge computing networks. IEEE Int Things J, 10(1):526–538. https://doi.org/10.1109/JIOT.2022.3202797
2. Akhlaqi MY, Hanapi ZM, 2023. Task offloading paradigm in mobile edge computing—current issues, adopted approaches, and future directions. J Netw Comput Appl, 212:103568. https://doi.org/10.1016/j.jnca.2022.103568
3. Bai XJ, Jin SF, 2022. Performance analysis of an energy-saving strategy in cloud data centers based on a MMAP[K]/M[K]/N1 + N2 non-preemptive priority queue. Fut Gener Comput Syst, 136:205–220. https://doi.org/10.1016/j.future.2022.06.004
4. Chahoud M, Otoum S, Mourad A, 2023. On the feasibility of federated learning towards on-demand client deployment at the edge. Inform Process Manag, 60(1):103150. https://doi.org/10.1016/j.ipm.2022.103150
5. Djigal H, Xu J, Liu LF, et al., 2022. Machine and deep learning for resource allocation in multi-access edge computing: a survey. IEEE Commun Surv Tutor, 24(4):2449–2494. https://doi.org/10.1109/COMST.2022.3199544
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