- Volume 15 Issue 5
DOI QR Code
Intelligent Resource Management Schemes for Systems, Services, and Applications of Cloud Computing Based on Artificial Intelligence
- Lim, JongBeom (Dept. of Game & Multimedia Engineering, Korea Polytechnic University) ;
- Lee, DaeWon (Dept. of Computer Engineering, Seokyeong University) ;
- Chung, Kwang-Sik (Dept. of Computer Science, Korea National Open University) ;
- Yu, HeonChang (Dept. of Computer Science & Engineering, Korea University)
- Received : 2019.07.12
- Accepted : 2019.09.03
- Published : 2019.10.31
Recently, artificial intelligence techniques have been widely used in the computer science field, such as the Internet of Things, big data, cloud computing, and mobile computing. In particular, resource management is of utmost importance for maintaining the quality of services, service-level agreements, and the availability of the system. In this paper, we review and analyze various ways to meet the requirements of cloud resource management based on artificial intelligence. We divide cloud resource management techniques based on artificial intelligence into three categories: fog computing systems, edge-cloud systems, and intelligent cloud computing systems. The aim of the paper is to propose an intelligent resource management scheme that manages mobile resources by monitoring devices' statuses and predicting their future stability based on one of the artificial intelligence techniques. We explore how our proposed resource management scheme can be extended to various cloud-based systems.
Artificial Intelligence;Cloud Computing;Edge-Cloud Systems;Fog Computing;Resource Management
Supported by : National Research Foundation of Korea (NRF)
- M. Yao, M. Sohul, V. Marojevic, and J. H. Reed, "Artificial intelligence defined 5G radio access networks," IEEE Communications Magazine, vol. 57, no. 3, pp. 14-20, 2019.
- M. H. ur Rehman, I. Yaqoob, K. Salah, M. Imran, P. P. Jayaraman, and C. Perera, "The role of big data analytics in industrial Internet of Things," Future Generation Computer Systems, vol. 99, pp. 247-259, 2019. https://doi.org/10.1016/j.future.2019.04.020
- Y. Zhang, X. Ma, J. Zhang, M. S. Hossain, G. Muhammad, and S. U. Amin, "Edge intelligence in the cognitive Internet of Things: improving sensitivity and interactivity," IEEE Network, vol. 33, no. 3, pp. 58-64, 2019.
- A. H. Sodhro, S. Pirbhulal, and V. H. C. de Albuquerque, "Artificial intelligence-driven mechanism for edge computing-based industrial applications," IEEE Transactions on Industrial Informatics, vol. 15, no. 7, pp. 4235-4243, 2019. https://doi.org/10.1109/TII.2019.2902878
- Y. Dai, D. Xu, S. Maharjan, G. Qiao, and Y. Zhang, "artificial intelligence empowered edge computing and caching for Internet of vehicles," IEEE Wireless Communications, vol. 26, no. 3, pp. 12-18, 2019.
- R. Donida Labati, A. Genovese, V. Piuri, F. Scotti, and S. Vishwakarma, "Computational intelligence in cloud computing," in Recent Advances in Intelligent Engineering. Cham: Springer International Publishing, 2020, pp. 111-127.
- M. Satyanarayanan and N. Davies, "Augmenting cognition through edge computing," Computer, vol. 52, no. 7, pp. 37-46, 2019.
- A. Kaplan and M. Haenlein, "Siri, Siri, in my hand: who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence," Business Horizons, vol. 62, no. 1, pp. 15-25, 2019. https://doi.org/10.1016/j.bushor.2018.08.004
- H. Gacanin and M. Wagner, "Artificial intelligence paradigm for customer experience management in nextgeneration networks: challenges and perspectives," IEEE Network, vol. 33, no. 2, pp. 188-194, 2019. https://doi.org/10.1109/MNET.2019.1800015
- W. C. Chien, C. F. Lai, and H. C. Chao, "Dynamic resource prediction and allocation in C-RAN with edge artificial intelligence," IEEE Transactions on Industrial Informatics, vol. 15, no. 7, pp. 4306-4314, 2019. https://doi.org/10.1109/TII.2019.2913169
- Z. Li, L. Liu, and D. Kong, "Virtual machine failure prediction method based on AdaBoost-Hidden Markov model," in Proceedings of 2019 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), Changsha, China, 2019, pp. 700-703.
- A. A. Mutlag, M. K. Abd Ghani, N. Arunkumar, M. A. Mohammed, and O. Mohd, "Enabling technologies for fog computing in healthcare IoT systems," Future Generation Computer Systems, vol. 90, pp. 62-78, 2019. https://doi.org/10.1016/j.future.2018.07.049
- Q. D. La, M. V. Ngo, T. Q. Dinh, T. Q. S. Quek, and H. Shin, "Enabling intelligence in fog computing to achieve energy and latency reduction," Digital Communications and Networks, vol. 5, no. 1, pp. 3-9, 2019. https://doi.org/10.1016/j.dcan.2018.10.008
- A. M. Rahmani, T. N. Gia, B. Negash, A. Anzanpour, I. Azimi, M. Jiang, and P. Liljeberg, "Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: a fog computing approach," Future Generation Computer Systems, vol. 78, pp. 641-658, 2018. https://doi.org/10.1016/j.future.2017.02.014
- A. Kumari, S. Tanwar, S. Tyagi, and N. Kumar, "Fog computing for Healthcare 4.0 environment: opportunities and challenges," Computers & Electrical Engineering, vol. 72, pp. 1-13, 2018. https://doi.org/10.1016/j.compeleceng.2018.08.015
- Q. Cui, Z. Gong, W. Ni, Y. Hou, X. Chen, X. Tao, and P. Zhang, "Stochastic online learning for mobile edge computing: learning from changes," IEEE Communications Magazine, vol. 57, no. 3, pp. 63-69, 2019.
- Z. Yin, H. Chen, and F. Hu, "An advanced decision model enabling two-way initiative offloading in edge computing," Future Generation Computer Systems, vol. 90, pp. 39-48, 2019. https://doi.org/10.1016/j.future.2018.07.031
- Y. Liu, C. Yang, L. Jiang, S. Xie, and Y. Zhang, "Intelligent edge computing for IoT-based energy management in smart cities," IEEE Network, vol. 33, no. 2, pp. 111-117, 2019.
- L. Hu, Y. Miao, G. Wu, M. M. Hassan, and I. Humar, "iRobot-Factory: an intelligent robot factory based on cognitive manufacturing and edge computing," Future Generation Computer Systems, vol. 90, pp. 569-577, 2019. https://doi.org/10.1016/j.future.2018.08.006
- W. C. Chien, H. H. Cho, C. F. Lai, F. H. Tseng, H. C. Chao, M. M. Hassan, and A. Alelaiwi, "Intelligent architecture for mobile HetNet in B5G," IEEE Network, vol. 33, no. 3, pp. 34-41, 2019.
- W. Zhang, Z. Zhang, S. Zeadally, H. C. Chao, and V. C. M. Leung, "MASM: A multiple-algorithm service model for energy-delay optimization in edge artificial intelligence," IEEE Transactions on Industrial Informatics, vol. 15, no. 7, pp. 4216-4224, 2019. https://doi.org/10.1109/TII.2019.2897001