Research Article

Resource Allocation in The Cloud Environment with Supervised Machine learning for Effective Data Transmission

Authors

  • Mohammad Elham Ebadi Computer Science and Software Engineering Faculty, Hohai University, Nanjing, China
  • Wang Yu Computer Science and Software Engineering Faculty, Hohai University, Nanjing, China
  • Khoshal Rahman Rahmani Computer Science Faculty, Kunduz University, Kunduz, Afghanistan
  • Musawer Hakimi Computer Science Department, Samangan University, Samangan, Afghanistan

Abstract

Resource allocation in the cloud environment for 5G applications can be explained by referring to the strategic distribution and necessary assignment of computing resources such as virtual machines, storage, and network bandwidth that meet the dynamic demands of applications and services. The framework proposed is on resource allocation in the cloud environment by BRoML for 5G applications. In the proposed BRoML model, the Backtracking Regularized model is incorporated for the effective calculation of the resources in the cloud environment. The optimization is performed for the effective computation of resources in the cloud environment through the computed resources. Using the estimated optimized values, a machine learning model can be trained and tested to classify resource allocation. In this regard, the simulation analysis is compared to BRoML with traditional schemes like SVM and RF. The result shows that BRoML has a higher resource utilization while exhibiting lower latency, higher increased throughput, and a better efficiency score overall. Machine learning techniques and optimization mechanisms give flexibility and intelligence to BRoMl in solving resource allocation issues within cloud computing. These results reinforce the view that BRoML can create a strong impact on the development process of cloud computing with its dynamic, intelligent solution in resource allocation optimization under various scenarios.

Article information

Journal

Journal of Computer Science and Technology Studies

Volume (Issue)

6 (3)

Pages

22-34

Published

2024-07-26

How to Cite

Mohammad Elham Ebadi, Wang Yu, Khoshal Rahman Rahmani, & Musawer Hakimi. (2024). Resource Allocation in The Cloud Environment with Supervised Machine learning for Effective Data Transmission. Journal of Computer Science and Technology Studies, 6(3), 22–34. https://doi.org/10.32996/jcsts.2024.6.3.3

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Keywords:

Cloud Environment, Resource Allocation, Machine Learning, Optimization, Backtracking