AI-Driven Optimization of Cloud Resources Allocation for Cost-Effective Scaling

Authors

Keywords:

cloud computing, resource allocation, AI optimization

Abstract

The fast progress of cloud computing transformed IT resource management through its combination of exceptional flexibility and scalability characteristics. The management of cloud resources requires complex solutions because performance needs to align with financial goals. The research investigates artificial intelligence-based optimization approaches to cloud resource management with a specific analysis of machine learning technology for process decision support. Evaluating several methods and their practical applications shows how AI technology can significantly transform cloud resource administration and create more efficient and economical scalability.

Organizations use artificial intelligence to process large datasets, which allows them to make immediate decisions for their cloud resource allocation. Implementing static rules and heuristic-based methods in traditional methods leads to unsatisfactory resource usage and creates operational expenses. Computer systems employing AI capabilities adjust resource distribution using ongoing demand information, ownership data, and external elements for enhanced real-time operations. Organizations achieve better system performance with enhanced user satisfaction by using adaptable resource management, leading to improved efficiency.

Businesses adopting cloud migration create a critical necessity for developing efficient resource management systems. The study examines how reinforcement learning perfectly matches predictive analytics and optimization algorithms for optimizing cloud resource distribution systems. Organizations succeed in saving costs through these advanced methodologies, which support high performance standards. Organizations must implement AI optimization programs because they enable maximum return on cloud spending investments to drive sustainable digital growth.

References

Armbrust, M., et al. (2010). Above the Clouds: A Berkeley View of Cloud Computing. University of California, Berkeley. This foundational paper discusses the implications of cloud computing and resource management strategies.

Zhang, Y., et al. (2019). Efficient Resource Allocation in Cloud Computing Environments Using AI-Driven Predictive Analytics. Applied and Computational Engineering. This study proposes a hybrid predictive model combining XGBoost and LSTM networks for efficient resource allocation.

Kumar, A., et al. (2020). Optimization Techniques for Cloud Resource Allocation: A Review. Journal of Cloud Computing: Advances, Systems and Applications. This paper reviews various optimization techniques for cloud resource management, including genetic algorithms and particle swarm optimization.

Ranjan, R., et al. (2022). AI-Driven Resource Management in Cloud Computing: Challenges and Opportunities. Future Generation Computer Systems. This article discusses the challenges in implementing AI-driven resource management solutions in cloud environments.

Huang, J., et al. (2020). Data Privacy and Security in AI-Driven Cloud Resource Management. Journal of Information Security and Applications. This paper addresses the privacy and security concerns of using AI in cloud resource allocation.

Zhou, Y., et al. (2020). Explainable AI in Cloud Computing: Enhancing Trust and Transparency. IEEE Transactions on Cloud Computing. This study explores methods to improve the interpretability of AI-driven decisions in cloud environments.

Li, X., et al. (2021). Integrating Edge Computing and AI for Enhanced Cloud Resource Management. Journal of Network and Computer Applications. This paper discusses the potential of combining edge computing with AI techniques for better resource allocation.

García, A., et al. (2019). Multi-Cloud Resource Management: Challenges and Solutions. Cloud Computing: Principles and Paradigms. This article reviews the complexities of managing resources across multiple cloud platforms and the role of AI in addressing these challenges.

Bhatia, S., et al. (2021). Machine Learning Techniques for Cloud Resource Management: A Review. Journal of Cloud Computing: Advances, Systems and Applications. This paper reviews machine learning approaches for optimizing resource allocation in cloud environments.

Ranjan, R., et al. (2020). AI-Driven Optimization Techniques for Cloud Resource Allocation: A Survey. ACM Computing Surveys. This survey comprehensively overviews various AI techniques applied to cloud resource allocation.

Singh, A., et al. (2021). Reinforcement Learning for Dynamic Resource Allocation in Cloud Computing. Journal of Systems and Software. This study investigates applying reinforcement learning techniques for adaptive resource management in cloud systems.

Gupta, R., et al. (2020). Energy-Efficient Resource Allocation in Cloud Computing Using AI Techniques. Energy Reports. This article discusses AI methods to optimize energy consumption in cloud resource management.

Chen, L., et al. (2018). Predictive Analytics for Cloud Resource Management: A Machine Learning Approach. Journal of Cloud Computing: Advances, Systems and Applications. This paper presents a machine learning framework for predictive analytics in cloud resource allocation.

Sahu, M., et al. (2019). Survey on Resource Management Techniques in Cloud Computing. International Journal of Computer Applications. This survey examines various resource management strategies employed in cloud environments.

Alharbi, A., et al. (2019). Cloud Computing Resource Management: A Survey. Journal of King Saud University - Computer and Information Sciences. This paper reviews existing approaches to resource management in cloud computing, including traditional and AI-driven methods.

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Published

16-08-2024

How to Cite

[1]
D. V. Talati, “AI-Driven Optimization of Cloud Resources Allocation for Cost-Effective Scaling”, J. of Art. Int. Research, vol. 4, no. 2, pp. 113–130, Aug. 2024.