Achieving cloud resource optimization with trust-based access control: A novel ML strategy for enhanced performance
Cloud computing continues to rise, increasing the demand for more intelligent, rapid, and secure resource management. This paper presents AdaPCA—a novel method that integrates the adaptive capabilities of AdaBoost with the dimensionality-reduction efficacy of PCA. What is the objective? Enhance trus...
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| Language: | English |
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Elsevier
2025-12-01
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| Series: | MethodsX |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016125003061 |
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| author | Bala Subramanian C Bharathi ST Shanmugapriya S |
| author_facet | Bala Subramanian C Bharathi ST Shanmugapriya S |
| author_sort | Bala Subramanian C |
| collection | DOAJ |
| description | Cloud computing continues to rise, increasing the demand for more intelligent, rapid, and secure resource management. This paper presents AdaPCA—a novel method that integrates the adaptive capabilities of AdaBoost with the dimensionality-reduction efficacy of PCA. What is the objective? Enhance trust-based access control and resource allocation decisions while maintaining a minimal computational burden. High-dimensional trust data frequently hampers systems; however, AdaPCA mitigates this issue by identifying essential aspects and enhancing learning efficacy concurrently. To evaluate its performance, we conducted a series of simulations comparing it with established methods such as Decision Trees, Random Forests, and Gradient Boosting. We assessed execution time, resource use, latency, and trust accuracy. Results show that AdaPCA achieved a trust score prediction accuracy of 99.8 %, a resource utilization efficiency of 95 %, and reduced allocation time to 140 ms, outperforming the benchmark models across all evaluated parameters. AdaPCA had superior performance overall—expedited decision-making, optimized resource utilization, reduced latency, and the highest accuracy in trust evaluation among the evaluated models. AdaPCA is not merely another model; it represents a significant advancement towards more intelligent and safe cloud systems designed for the future. • Introduces AdaPCA, a novel hybrid approach that integrates AdaBoost with PCA to optimize cloud resource allocation and improve trust-based access control. • Outperforms conventional techniques such as Decision Tree, Random Forest, and Gradient Boosting by attaining superior trust accuracy, expedited execution, enhanced resource utilization, and reduced latency. • Presents an intelligent, scalable, and adaptable architecture for secure and efficient management of cloud resources, substantiated by extensive simulation experiments. |
| format | Article |
| id | doaj-art-930486accb6c4c9b9302a9ee2ce01375 |
| institution | OA Journals |
| issn | 2215-0161 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj-art-930486accb6c4c9b9302a9ee2ce013752025-08-20T02:35:22ZengElsevierMethodsX2215-01612025-12-011510346110.1016/j.mex.2025.103461Achieving cloud resource optimization with trust-based access control: A novel ML strategy for enhanced performanceBala Subramanian C0Bharathi ST1Shanmugapriya S2Computer Science and Engineering, Kalasalingam Academy of Research and Education, Srivilliputhur, IndiaCorresponding author.; Computer Science and Engineering, Kalasalingam Academy of Research and Education, Srivilliputhur, IndiaComputer Science and Engineering, Kalasalingam Academy of Research and Education, Srivilliputhur, IndiaCloud computing continues to rise, increasing the demand for more intelligent, rapid, and secure resource management. This paper presents AdaPCA—a novel method that integrates the adaptive capabilities of AdaBoost with the dimensionality-reduction efficacy of PCA. What is the objective? Enhance trust-based access control and resource allocation decisions while maintaining a minimal computational burden. High-dimensional trust data frequently hampers systems; however, AdaPCA mitigates this issue by identifying essential aspects and enhancing learning efficacy concurrently. To evaluate its performance, we conducted a series of simulations comparing it with established methods such as Decision Trees, Random Forests, and Gradient Boosting. We assessed execution time, resource use, latency, and trust accuracy. Results show that AdaPCA achieved a trust score prediction accuracy of 99.8 %, a resource utilization efficiency of 95 %, and reduced allocation time to 140 ms, outperforming the benchmark models across all evaluated parameters. AdaPCA had superior performance overall—expedited decision-making, optimized resource utilization, reduced latency, and the highest accuracy in trust evaluation among the evaluated models. AdaPCA is not merely another model; it represents a significant advancement towards more intelligent and safe cloud systems designed for the future. • Introduces AdaPCA, a novel hybrid approach that integrates AdaBoost with PCA to optimize cloud resource allocation and improve trust-based access control. • Outperforms conventional techniques such as Decision Tree, Random Forest, and Gradient Boosting by attaining superior trust accuracy, expedited execution, enhanced resource utilization, and reduced latency. • Presents an intelligent, scalable, and adaptable architecture for secure and efficient management of cloud resources, substantiated by extensive simulation experiments.http://www.sciencedirect.com/science/article/pii/S2215016125003061AdaBoostCloud computingDimensionality reductionMachine learningPrincipal Component AnalysisResource optimization |
| spellingShingle | Bala Subramanian C Bharathi ST Shanmugapriya S Achieving cloud resource optimization with trust-based access control: A novel ML strategy for enhanced performance MethodsX AdaBoost Cloud computing Dimensionality reduction Machine learning Principal Component Analysis Resource optimization |
| title | Achieving cloud resource optimization with trust-based access control: A novel ML strategy for enhanced performance |
| title_full | Achieving cloud resource optimization with trust-based access control: A novel ML strategy for enhanced performance |
| title_fullStr | Achieving cloud resource optimization with trust-based access control: A novel ML strategy for enhanced performance |
| title_full_unstemmed | Achieving cloud resource optimization with trust-based access control: A novel ML strategy for enhanced performance |
| title_short | Achieving cloud resource optimization with trust-based access control: A novel ML strategy for enhanced performance |
| title_sort | achieving cloud resource optimization with trust based access control a novel ml strategy for enhanced performance |
| topic | AdaBoost Cloud computing Dimensionality reduction Machine learning Principal Component Analysis Resource optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2215016125003061 |
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