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@article{207661,
        author = {Abdul Shahul and Milan Kumar Sahu and Dr. Bharathi M.P},
        title = {Machine Learning-Based Prediction of Resource Utilization in Cloud Data Centres},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {3},
        pages = {2212-2221},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207661},
        abstract = {Scalable demand-based computing resources are provided by cloud computing in large scale data centers with various virtual machines and containers. Cloud resource management is quite difficult because of heterogeneity in workloads causing low utilization of resources, higher cost, wastage of energy, and violation of service level agreement. This paper proposes a machine learning approach to predicting CPU utilization in cloud data centres with heterogeneous infrastructure using the 10,000 cloud resource utilization records. Data preprocessing includes data cleaning, label encoding, and feature selection. The proposed model employs the use of the Random Forest Regression (RFR) algorithm to model non-linear relationships among the features in order to predict CPU utilization percentage. Machine learning metrics like the Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and the R2 score have been used to measure the performance of the proposed model. The current research work has also focused on analyzing the feature importance of predicting CPU utilization percentage. The analysis shows that memory utilization, number of active tasks, network utilization, and power consumption are the key features to predict CPU utilization percentage.},
        keywords = {Cloud Computing, Resource Utilization Prediction, Random Forest Regression, Machine Learning, Cloud Data Centres, SLA Management, Energy Efficiency.},
        month = {August},
        }