AI-Powered Predictive Analytics for Dynamic Cloud Resource Optimization: A Technical Implementation Framework
DOI:
https://doi.org/10.32628/CSEIT251112122Keywords:
Cloud Resource Optimization, Artificial Intelligence, Predictive Analytics, Machine Learning, Resource Management SystemsAbstract
This article explores the transformative impact of AI-driven predictive analytics on cloud resource optimization, presenting a comprehensive technical implementation framework. The article shows how artificial intelligence and machine learning architectures revolutionize traditional cloud management approaches through advanced predictive capabilities and dynamic resource allocation. It examines the evolution from static threshold-based systems to sophisticated AI-driven solutions, analyzing their implementation strategies across various organizational contexts. The article delves into multiple optimization domains, including capacity provisioning, cost management, performance enhancement, and energy efficiency, while presenting real-world applications and impact analyses across different industries. Through extensive case studies and empirical evidence, the article demonstrates how organizations leverage AI-powered solutions to address complex cloud resource management challenges, achieve operational efficiencies, and maintain competitive advantages in the digital marketplace. The article also explores future developments and provides strategic recommendations for organizations implementing cloud optimization frameworks, emphasizing the importance of standardized approaches, stakeholder engagement, and sustainable practices in cloud resource management.
Downloads
References
H. Anwar Basha et al., "Real-Time Challenges and Opportunities for an Effective Resource Management in Multi-cloud Environment," SN Computer Science, vol. 5, no. 1, pp. 1-15, 2024. https://link.springer.com/article/10.1007/s42979-023-02578-3
Restack, "AI Optimization Vs Traditional Algorithms: A Comprehensive Analysis," IEEE Transactions on Cloud Computing, vol. 12, no. 3, pp. 45-60, 2024. https://www.restack.io/p/ai-optimization-answer-ai-vs-traditional-algorithms-cat-ai
Marshall D. Jackson et al., "A Bayesian Framework for Supporting Predictive Analytics over Big Transportation Data," IEEE International Conference on Cloud Computing, pp. 234-245, 2021. https://ieeexplore.ieee.org/document/9529442
Francoise Caron, "Seamless Integration between Real-time Analyses and Systems Engineering with the PST Approach," IEEE International Systems Conference, pp. 156-167, 2020. https://ieeexplore.ieee.org/abstract/document/9275907
T. Sai Kumar Reddy, "Data Integration Strategies in Hybrid Cloud Environments," IEEE Transactions on Cloud Computing, vol. 15, no. 4, pp. 178-189, 2024. https://www.ijsr.net/archive/v11i3/SR22032114643.pdf
Ashkan Paya et al., "Energy-Aware Load Balancing and Application Scaling for the Cloud Ecosystem," IEEE Transactions on Cloud Computing, vol. 5, no. 2, pp. 318-331, 2017. https://ieeexplore.ieee.org/document/7018917
Lachlan L. H. Andrew, "Algorithms for Dynamic Capacity Provisioning," IEEE Transactions on Cloud Computing, vol. 2, no. 3, pp. 145-156, 2014. https://ieeexplore.ieee.org/document/6245948
Xin Chen al., "Cost-Efficient Request Scheduling and Resource Provisioning in Multiclouds for Internet of Things," IEEE, https://ieeexplore.ieee.org/document/8877832
Christian Davatz, "An Approach and Case Study of Cloud Instance Type Selection for Multi-tier Web Applications," 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), pp. 123-134, 2017. https://ieeexplore.ieee.org/document/7973740
D. E. Ajeh, J. Ellman, and S. Keogh, "A Cost Modelling System for Cloud Computing," 14th International Conference on Computational Science and Its Applications (ICCSA), pp. 567-578, 2014. https://ieeexplore.ieee.org/abstract/document/6976666
IEEE Computer Society, "IEEE Std. 2301-2020 Guide for Cloud Portability and Interoperability Profiles (CPIP)," IEEE Computer Society, pp. 1-150, 2020. https://ieeexplore.ieee.org/document/9169938
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.