Artificial Intelligence for Scalable Cloud Systems: Innovations in Resource Optimization
Keywords:
Artificial Intelligence, Cloud Computing, Resource Optimization, Dynamic ProvisioningAbstract
Resource optimization for intelligent and scalable solution for exponential growth of cloud computing demands which ensures performance efficiency, cost-effectiveness, and reliability. The objective of this paper is to explore the integration of artificial intelligence (AI) techniques which includes machine learning, deep reinforcement learning, and predictive analytics in cloud infrastructure management for dynamic resource provisioning, workload prediction, and anomaly detection.
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References
M. Armbrust, A. Fox, R. Griffith, et al., "A view of cloud computing," Commun. ACM, vol. 53, no. 4, pp. 50–58, 2010.
M. Satyanarayanan, "The emergence of edge computing," Computer, vol. 50, no. 1, pp. 30–39, Jan. 2017.
D. P. Ali, M. Abolhasan, and A. S. Jay, "Machine learning for cloud resource management: Challenges, methodologies, and future directions," IEEE Access, vol. 9, pp. 118259–118282, 2021.
X. Liu, Z. Liu, and L. Wang, "Resource optimization for cloud computing using machine learning techniques: A survey," Future Gener. Comput. Syst., vol. 118, pp. 188–209, Apr. 2021.
A. G. Gotsman, A. O. Hodge, and D. Y. Liu, "Reinforcement learning for dynamic cloud resource provisioning," IEEE Trans. Parallel Distrib. Syst., vol. 32, no. 4, pp. 954–965, Apr. 2021.
Y. Zhang, Q. Li, and Y. Yang, "Energy-efficient cloud computing with machine learning and reinforcement learning algorithms," IEEE Trans. Cloud Comput., vol. 10, no. 4, pp. 1221–1233, Oct.-Dec. 2022.
S. Bhat, S. Rajendran, and V. Iyer, "Autonomous cloud resource optimization using deep reinforcement learning," IEEE Trans. Cloud Comput., vol. 10, no. 6, pp. 1031–1043, Nov.-Dec. 2022.
C. Zhang, Y. Zhang, and X. Liu, "Predictive scaling for cloud systems: A deep learning approach," IEEE Trans. Netw. Service Manag., vol. 18, no. 3, pp. 2156–2173, Sep. 2021.
M. Da Silva, "Cloud computing and the need for self-optimizing systems," IEEE Cloud Comput., vol. 6, no. 2, pp. 30–38, May-June 2020.
A. M. Rahman, M. D. Qadir, and A. J. Leith, "AI-driven resource management for efficient cloud computing," IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 2334–2341, Jun. 2021.
F. B. Bastani and M. H. Ghodrat, "Federated learning for cloud resource optimization: A survey," IEEE Access, vol. 9, pp. 143987–144010, 2021.
S. Kumar, M. Gupta, and A. Soni, "Hybrid cloud resource management using machine learning for optimal resource allocation," IEEE Trans. Netw. Service Manag., vol. 19, no. 2, pp. 1122–1135, Jun. 2022.
L. Tan, Z. Xiao, and Q. Zhang, "AI and cloud computing convergence for efficient resource allocation," IEEE Commun. Mag., vol. 59, no. 4, pp. 96–103, Apr. 2021.
A. H. Zahir and R. E. Barakabitze, "AI-based resource scheduling in cloud computing: Trends, challenges, and opportunities," IEEE Internet Things J., vol. 8, no. 1, pp. 49–58, Jan. 2021.
M. K. Sharma and S. K. Gupta, "AI-driven decision-making in cloud systems: A resource management perspective," IEEE Access, vol. 8, pp. 81977–81991, 2020.
N. Y. Chong, P. Chen, and X. Li, "Cloud computing and big data: A review of scalable AI resource allocation," IEEE Trans. Big Data, vol. 7, no. 2, pp. 372–383, Jun. 2020.
G. A. Nand, "Energy-aware resource scheduling and AI: Techniques for scalable cloud infrastructure," IEEE Access, vol. 9, pp. 124455–124470, 2021.
M. O. Ferreira, P. A. Palang, and V. Bhaskaran, "Energy-aware AI solutions for efficient cloud computing," IEEE Trans. Comput., vol. 70, no. 7, pp. 1127–1137, Jul. 2021.
M. H. Parvez and K. Z. Ibrahim, "AI for cloud data management and optimization in federated clouds," IEEE Cloud Comput., vol. 9, no. 1, pp. 18–27, Jan.-Feb. 2022.
M. Siddiqui, A. K. Soni, and M. Shankar, "AI-driven fault-tolerant resource management in cloud systems," IEEE Trans. Cloud Comput., vol. 11, no. 5, pp. 1228–1239, Sep.-Oct. 2023.
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