CLOUD RESOURCE OPTIMIZATION SYSTEM
AUTHORS:
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DR.B.Swaminathan
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UG Scholar , Associate professor Department of Computer Science and Engineering – CTIS Jain
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Abstract
Cloud computing environments encounter considerable challenges in effectively allocating resources due to varying demands from users and applications. As businesses progressively transition workloads to cloud infrastructure, achieving optimal resource utilization becomes essential for maintaining service quality and cost-effectiveness. This paper introduces a Cloud Resource Optimization System, an interactive web application designed to aid users in enhancing cloud resource utilization based on real-time input parameters such as CPU usage, memory usage, disk storage, and task priority. The system performs dynamic analyses of resource consumption and offers customized optimization recommendations to enhance performance, lower costs, and ensure system stability. In contrast to traditional static resource management systems, our model prioritizes interactivity and task- specific guidance through rule- based dynamic analysis.
Furthermore, the system improves user decision-making via a visual and user-friendly interface, providing immediate insights for various cloud workloads. This methodology effectively bridges the divide between manual monitoring and automated management by delivering actionable intelligence for proficient cloud resource planning. Our implementation illustrates that adaptive and interactive optimization systems can greatly enhance operational efficiency within cloud computing environments.
Our implementation illustrates that adaptive and
interactive optimization systems can greatly enhance operational efficiency within cloud computing environments. By providing tailored and accurate recommendations based on real resource usage patterns, the proposed system aids in reducing resource waste, optimizing expenses, and facilitating scalable cloud operations. This paper emphasizes how these interactive solutions can act as a practical bridge between manual cloud resource management and sophisticated automated orchestration tools, thereby making cloud optimization attainable for users without expert knowledge.
The system performs real-time analysis of resource usage and offers customized optimization recommendations aimed at enhancing performance, lowering expenses, and maintaining system stability. In contrast to traditional static models, our system prioritizes interactivity and provides task-specific advice through rule-based dynamic analysis.
Furthermore, the system improves user decision-making via a visual and user-friendly interface, providing immediate insights for various cloud workloads. This methodology effectively bridges the divide between manual monitoring and automated management by delivering actionable intelligence for proficient cloud resource planning. Our implementation illustrates that adaptive and interactive optimization systems can greatly enhance operational efficiency within cloud computing environments.
Our implementation illustrates that adaptive and
interactive optimization systems can greatly enhance operational efficiency within cloud computing environments. By providing tailored and accurate recommendations based on real resource usage patterns, the proposed system aids in reducing resource waste, optimizing expenses, and facilitating scalable cloud operations. This paper emphasizes how these interactive solutions can act as a practical bridge between manual cloud resource management and sophisticated automated orchestration tools, thereby making cloud optimization attainable for users without expert knowledge.
The system performs real-time analysis of resource usage and offers customized optimization recommendations aimed at enhancing performance, lowering expenses, and maintaining system stability. In contrast to traditional static models, our system prioritizes interactivity and provides task-specific advice through rule-based dynamic analysis.
Keywords
Resource optimization system Data processing Reinforcement learning
Article Information
PUBLICATION DETAILS
Review Type:
0
License:
CC BY-NC 4.0
Volume/Issue:
Volume Volume 10, Issue 01
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Feb 23 2026
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Praveen, V. R. ,. A. ,. (2026). Cloud Resource Optimization System. International Journal of Science, Strategic Management and Technology, Volume 10(01). https://doi.org/10.55041/ijsmt.v2i2.123
Praveen, Vanshika. "Cloud Resource Optimization System." International Journal of Science, Strategic Management and Technology, vol. Volume 10, no. 01, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i2.123.
Praveen, Vanshika. "Cloud Resource Optimization System." International Journal of Science, Strategic Management and Technology Volume 10, no. 01 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i2.123.
References
1.Wang, Y., & Yang, X. (2025). Intelligent Resource Allocation Optimization for Cloud Computing via Machine Learning. arXiv preprint arXiv:2504.03682, 1-15.
2.Deochake, S. (2023). Cloud Cost Optimization: A Comprehensive Review of Strategies and Case Studies. arXiv preprint arXiv:2307.12479, 1-22.
3.Xu, Z., Yan, F. Y., & Yu, M. (2024).Zeal:Rethinking Large-Scale Resource Allocation with "Decouple and Decompose". arXiv preprint arXiv:2412.11447, 1-12.
4.Arabnejad, H., Petcu, D., & Fahringer, T. (2017). Cost-Efficient Resource Allocation Using Workflows in Multi-Cloud Environments. Future Generation Computer Systems, 71, 129-147.
5.Garg, S. K., & Buyya, R. (2012).NetworkCloudSim: Modelling
Parallel Applications in Cloud Simulations. Proceedings of the 4th IEEE/ACM International Conference on Utility and Cloud Computing, 105-113.
6.Zhang, Q., Cheng, L., & Boutaba, R. (2010). Cloud Computing: State-of-the-Art and Research Challenges. Journal of Internet Services and Applications, 1(1), 7-18.
7.Singh, R., & Kaur, P. (2019). Cost Optimization Strategies in Cloud Computing Environments. International Journal of Cloud Applications and Computing, 9(4), 45-58.
8.Huang, M., & Zhang, Y. (2019). Resource Usage Prediction and Optimization in Cloud Systems. IEEE Access, 7, 160899-160910.
9.Roy, A., & Banerjee, T. (2021). Multi- Objective Resource Optimization in Cloud Computing Environments. International Journal of Cloud Computing and Services Science, 10(1), 45-60.
10.Tan, W., & Li, Z. (2021). Enhancing Cloud Resource Efficiency with Automated Suggestions. IEEE Cloud Computing, 8(5), 55-63.
2.Deochake, S. (2023). Cloud Cost Optimization: A Comprehensive Review of Strategies and Case Studies. arXiv preprint arXiv:2307.12479, 1-22.
3.Xu, Z., Yan, F. Y., & Yu, M. (2024).Zeal:Rethinking Large-Scale Resource Allocation with "Decouple and Decompose". arXiv preprint arXiv:2412.11447, 1-12.
4.Arabnejad, H., Petcu, D., & Fahringer, T. (2017). Cost-Efficient Resource Allocation Using Workflows in Multi-Cloud Environments. Future Generation Computer Systems, 71, 129-147.
5.Garg, S. K., & Buyya, R. (2012).NetworkCloudSim: Modelling
Parallel Applications in Cloud Simulations. Proceedings of the 4th IEEE/ACM International Conference on Utility and Cloud Computing, 105-113.
6.Zhang, Q., Cheng, L., & Boutaba, R. (2010). Cloud Computing: State-of-the-Art and Research Challenges. Journal of Internet Services and Applications, 1(1), 7-18.
7.Singh, R., & Kaur, P. (2019). Cost Optimization Strategies in Cloud Computing Environments. International Journal of Cloud Applications and Computing, 9(4), 45-58.
8.Huang, M., & Zhang, Y. (2019). Resource Usage Prediction and Optimization in Cloud Systems. IEEE Access, 7, 160899-160910.
9.Roy, A., & Banerjee, T. (2021). Multi- Objective Resource Optimization in Cloud Computing Environments. International Journal of Cloud Computing and Services Science, 10(1), 45-60.
10.Tan, W., & Li, Z. (2021). Enhancing Cloud Resource Efficiency with Automated Suggestions. IEEE Cloud Computing, 8(5), 55-63.
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This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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