Open Access

ARTICLE

Analysis and Prediction of Real-Time Memory and Processor Usage Using Artificial Intelligence (AI)

Kadriye Simsek Alan^*, Ayca Durgut, Helin Doga Demirel

Department of Mathematical Engineering, Faculty of Chemical and Metallurgical Engineering, Yildiz Technical University, Istanbul, 34220, Turkey

* Corresponding Author: Kadriye Simsek Alan. Email:

Journal on Artificial Intelligence 2025, 7, 397-415. https://doi.org/10.32604/jai.2025.071133

Received 01 August 2025; Accepted 17 September 2025; Issue published 20 October 2025

Abstract

Efficient utilization of processor and memory resources is essential for sustaining performance and energy efficiency in modern computing infrastructures. While earlier research has emphasized CPU utilization forecasting, joint prediction of CPU and memory usage under real workload conditions remains underexplored. This study introduces a machine learning–based framework for real-time prediction of CPU and RAM utilization using the Google Cluster Trace 2019 v3 dataset. The framework combines Extreme Gradient Boosting (XGBoost) with a MultiOutputRegressor (MOR) to capture nonlinear interactions across multiple resource dimensions, supported by a leakage-safe imputation strategy that prevents bias from missing values. Nested cross-validation was employed to ensure rigorous evaluation and reproducibility. Experiments demonstrated that memory usage can be predicted with higher accuracy and stability than processor usage. Residual error analysis revealed balanced error distributions and very low outlier rates, while regime-based evaluations confirmed robustness across both low and high utilization scenarios. Feature ablation consistently highlighted the central role of page cache memory, which significantly affected predictive performance for both CPU and RAM. Comparisons with baseline models such as linear regression and random forest further underscored the superiority of the proposed approach. To assess adaptability, an online prequential learning pipeline was deployed to simulate continuous operation. The system preserved offline accuracy while dynamically adapting to workload changes. It achieved stable performance with extremely low update latencies, confirming feasibility for deployment in environments where responsiveness and scalability are critical. Overall, the findings demonstrate that simultaneous modeling of CPU and RAM utilization enhances forecasting accuracy and provides actionable insights for cache management, workload scheduling, and dynamic resource allocation. By bridging offline evaluation with online adaptability, the proposed framework offers a practical solution for intelligent and sustainable cloud resource management.

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