Acknowledgement
이 논문은 2022학년도 평택대학교 학술연구비의 지원에 의하여 연구되었음.
References
- L. Sun, X. Jiang, H. Ren, and Y. Guo, "Edge-Cloud computing and artificial intelligence in internet of medical things: Architecture, technology and application," IEEE Access, Vol.8, pp.101079-101092, 2020. https://doi.org/10.1109/ACCESS.2020.2997831
- U. F. Mustapha, A.-W. Alhassan, D.-N. Jiang, and G.-L. Li, "Sustainable aquaculture development: A review on the roles of cloud computing, internet of things and artificial intelligence (CIA)," Reviews in Aquaculture, Vol.13, No.4, pp.2076-2091, 2021. https://doi.org/10.1111/raq.12559
- Y. Pan and L. Zhang, "Roles of artificial intelligence in construction engineering and management: A critical review and future trends," Automation in Construction, Vol.122, pp.103517, 2021.
- G. Ananthanarayanan et al., "Real-time video analytics: The killer app for edge computing," Computer, Vol.50, No.10, pp.58-67, 2017. https://doi.org/10.1109/MC.2017.3641638
- K. Cao, Y. Liu, G. Meng, and Q. Sun, "An overview on edge computing research," IEEE Access, Vol.8, pp.85714-85728, 2020. https://doi.org/10.1109/ACCESS.2020.2991734
- W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, "Edge computing: Vision and challenges," IEEE Internet of Things Journal, Vol.3, No.5, pp.637-646, 2016. https://doi.org/10.1109/JIOT.2016.2579198
- Q. Luo, S. Hu, C. Li, G. Li, and W. Shi, "Resource scheduling in edge computing: A survey," IEEE Communications Surveys & Tutorials, Vol.23, No.4, pp.2131-2165, 2021. https://doi.org/10.1109/COMST.2021.3106401
- X. Li, J. Wan, H. N. Dai, M. Imran, M. Xia, and A. Celesti, "A hybrid computing solution and resource scheduling strategy for edge computing in smart manufacturing," IEEE Transactions on Industrial Informatics, Vol.15, No.7, pp.4225-4234, 2019. https://doi.org/10.1109/TII.2019.2899679
- S. Kunal, A. Saha, and R. Amin, "An overview of cloud-fog computing: Architectures, applications with security challenges," Security and Privacy, Vol.2, No.4, pp.e72, 2019.
- D. Kimovski, R. Matha, J. Hammer, N. Mehran, H. Hellwagner, and R. Prodan, "Cloud, fog, or edge: Where to compute?," IEEE Internet Computing, Vol.25, No.4, pp.30-36, 2021. https://doi.org/10.1109/MIC.2021.3050613
- V. Prokhorenko and M. A. Babar, "Architectural resilience in cloud, fog and edge systems: A survey," IEEE Access, Vol.8, pp.28078-28095, 2020. https://doi.org/10.1109/ACCESS.2020.2971007
- G. Rjoub, J. Bentahar, O. Abdel Wahab, and A. Saleh Bataineh, "Deep and reinforcement learning for automated task scheduling in large-scale cloud computing systems," Concurrency and Computation: Practice and Experience, Vol.33, No.23, pp.e5919, 2021.
- Y. Ran, H. Hu, X. Zhou, and Y. Wen, "DeepEE: Joint optimization of job scheduling and cooling control for data center energy efficiency using deep reinforcement learning," 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp.645-655, 2019.
- A. Alqahtani, Y. Li, P. Patel, E. Solaiman, and R. Ranjan, "End-to-End service level agreement specification for IoT applications," in 2018 International Conference on High Performance Computing & Simulation (HPCS), 16-20 July 2018, pp.926-935, 2018.
- Q. Liang, P. Shenoy, and D. Irwin, "AI on the Edge: Characterizing AI-based IoT applications using specialized edge architectures," in 2020 IEEE International Symposium on Workload Characterization (IISWC), 27-30 Oct. 2020, pp. 145-156, 2020.
- X. Xie et al., "A transferable approach for partitioning machine learning models on multi-chip-modules," Proceedings of Machine Learning and Systems, Vol.4, pp. 370- 381, 2022.
- S. Shen, V. V. Beek, and A. Iosup, "Statistical characterization of business-critical workloads hosted in cloud datacenters," in 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, 4-7 May 2015, pp.465-474, 2015.
- J. McChesney, N. Wang, A. Tanwer, E. De Lara, and B. Varghese, "Defog: Fog computing benchmarks," in Proceedings of the 4th ACM/IEEE Symposium on Edge Computing, pp.47-58, 2019.
- S. Tuli, S. Ilager, K. Ramamohanarao, and R. Buyya, "Dynamic scheduling for stochastic edge-cloud computing environments using A3C learning and residual recurrent neural networks," IEEE Transactions on Mobile Computing, Vol.21, No.3, pp.940-954, 2022. https://doi.org/10.1109/TMC.2020.3017079