International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 12 | Dec 2025
p-ISSN: 2395-0072
www.irjet.net
Resource Allocation Using Machine Learning Sanskruti Yadav1, Prof. Prashant Govardhan2, Khushi Parihar 3, Lakshata Malvi4, Ankit Verma5, Jayant Tarane6 2Professor, CSE , Priyadarshini College of Engineering Nagpur, Maharashtra, India 13456UG Student, CSE , Priyadarshini College of Engineering Nagpur, Maharashtra, India
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Abstract -With the increasing demand for cloud services,
algorithms—Random Forest, XGBoost, and Logistic Regression—are implemented and comparatively evaluated. The methodology includes data preprocessing, model training, and performance assessment using accuracy and classification reports. The study aims to enhance resource utilization and cost efficiency through intelligent ML-based provisioning strategies.
optimizing resource utilization is paramount to minimize costs and improve performance. Traditional resource allocation methods often struggle with dynamic workloads and fail to fully leverage available resources. This work explores the application of various machine learning models to predict optimal resource configurations based on diverse workload characteristics. We analyze a dataset containing features such as CPU usage, memory usage, network usage. Several classification algorithms, including Random Forest Classifier, XGBoost Classifier, and Logistic Regression, are implemented and evaluated to determine their effectiveness in predicting the 'Optimized Resource Allocation'. The results demonstrate that machine learning models can effectively learn complex relationships within the data and provide accurate predictions for resource allocation, offering a data-driven approach to improve cloud infrastructure efficiency. This research highlights the potential of machine learning to automate and optimize resource management in cloud environments, leading to significant improvements in performance and cost savings. The comparison of different models provides insights into their suitability for this specific task, guiding future efforts in developing intelligent resource allocation systems. The findings contribute to the growing body of work on applying artificial intelligence to solve real-world cloud computing challenges.
2. Methodology of Review This review is based on academic research published between 2016 and 2025, sourced from reputed digital libraries such as IEEE Xplore, SpringerLink, Google Scholar, ResearchGate, and ScienceDirect. The studies were selected based on three primary criteria: they must focus on cloud resource management or provisioning, apply machine learning or artificial intelligence techniques, and evaluate performance using measurable metrics such as resource utilization, cost efficiency, or system throughput. A broad range of research articles were analyzed to understand the evolution of intelligent and data-driven cloud resource allocation strategies. Among these, several studies were identified as highly relevant due to their use of supervised learning models, reinforcement learning, and hybrid optimization techniques for dynamic workload management. These selected works were critically examined to compare algorithmic approaches, system architectures, evaluation methodologies, and performance outcomes. The review also highlights existing challenges, including scalability, model generalization, and real-time adaptability, which motivate the need for more efficient ML-based resource provisioning solutions.
Key Words: Resource allocation, algorithms, Cloud computing, Machine learning
1. INTRODUCTION
3. Literature Review
The rapid evolution of cloud computing platforms such as AWS, Azure, and GCP has exposed the limitations of traditional static resource provisioning, which often leads to over-provisioning or under-provisioning and increased operational costs. The growing heterogeneity of cloud workloads further complicates efficient resource management, necessitating adaptive and data-driven approaches. Machine learning (ML) offers an effective solution by leveraging historical workload and performance data to predict future resource demands. This research formulates optimal cloud resource provisioning as a supervised ML classification problem, where workloads are categorized into predefined optimized resource allocation levels based on CPU, memory, and network usage. Three classification
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Impact Factor value: 8.315
3.1 Evaluating Machine Learning Prediction Systems and their Impact on Proactive Resource Provisioning for Cloud Environments Kirchoff et al. (2024) evaluated machine learning prediction techniques to optimize proactive resource provisioning in cloud environments. Their study found that ensemble methods like Random Forest and XGBoost provided higher accuracy and reliability compared to linear models. While effective in reducing resource wastage and improving system performance, the approach relied heavily on historical workload data, which may not capture sudden spikes.
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