Machine learning-based solutions for resource management in fog computing
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-16399-2.pdf
Reference80 articles.
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2. Abdullah M, Iqbal W, Mahmood A, Bukhari F, Erradi A (2021) Predictive autoscaling of microservices hosted in fog microdata center. IEEE Syst J 15(1):1275–1286. https://doi.org/10.1109/JSYST.2020.2997518
3. Ahvar E, Ahvar S, Mann ZA, Crespi N, Glitho R, Garcia-Alfaro J (2021) Deca: A dynamic energy cost and carbon emission-efficient application placement method for edge clouds. IEEE Access 9:70192–70213. https://doi.org/10.1109/ACCESS.2021.3075973
4. Ahvar E, Ahvar S, Raza SM, Manuel Sanchez Vilchez J, Lee GM (2021) Next generation of sdn in cloud-fog for 5g and beyond-enabled applications: Opportunities and challenges. Network 1(1):28–49. https://doi.org/10.3390/network1010004
5. Albalawi M, Alkayal E, Barnawi A, Boulares M (2022) Load balancing based on many-objective particle swarm optimization algorithm with support vector regression in fog computing. J Eng Appl Sci Technol 138
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