An Investigation into the Performance Optimization of Cloud Computing Systems Using Machine Learning Algorithms

• Author(s): Sujon Sarkar
• Paper ID: 1707965
• Page: 1357-1366
• Published Date: 12-05-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 8 Issue 10 April-2025

Abstract

Cloud computing is now the very backbone through which modern digital services operate, allowing on-demand access to computational resources and storage in an elastic manner. Nevertheless, the complexity, heterogeneity, and dynamic workload patterns of cloud environments have led to extensive challenges in performance management. Traditional static or rule-based optimization techniques that are often used are generally unable to accommodate real-time demand changes. Some performance issues resulting from this include inefficient resource utilization, service degradation, and increased operational costs. The objective of this study is the fusion of machine learning (ML) algorithms into improving the overall performance and efficiency of cloud computing systems. Some of the key performance indicators used in this study include CPU and Memory Utilization, latency, throughput, network traffic, and energy consumption. A comparative analysis has been carried out using the different ML paradigms, including supervised learning (for example, regression, decision trees), unsupervised learning (for example, clustering, anomaly detection), and reinforcement learning, to analyze their performance in different core optimization tasks, including dynamic workload balancing, auto-scaling prediction, fault tolerance, and proactive maintenance. A multi-layer a framework addressing real-time performance monitoring and adaptive decision-making via ML models trained on historical and streaming cloud telemetry data has been developed. A variety of case studies where data has been obtained from public cloud providers as a simulated environment show substantial improvements in both resource efficiency and system responsiveness. In addition, this research outlines trade-offs in model complexity and computational overhead and discusses what this could mean for the practical deployment of ML in production-grade cloud systems. Overall, this investigation confirms that intelligent, autonomous cloud operations can be achieved through ML-based optimization strategies far exceeding traditional heuristics. Hence, it opens a path toward creating self-optimizing cloud platforms satisfying the requirements of increasingly complex, real-time, and mission-critical applications.

Keywords

Cloud Computing, Performance Optimization, Machine Learning (ML), Resource Utilization, Proactive Maintenance, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Real-Time Monitoring, Elastic Infrastructure, Case Studies, Public Cloud Providers, Intelligent Cloud Operations, Autonomous Systems.

Citations

IRE Journals:
Sujon Sarkar "An Investigation into the Performance Optimization of Cloud Computing Systems Using Machine Learning Algorithms" Iconic Research And Engineering Journals Volume 8 Issue 10 2025 Page 1357-1366

IEEE:
Sujon Sarkar "An Investigation into the Performance Optimization of Cloud Computing Systems Using Machine Learning Algorithms" Iconic Research And Engineering Journals, 8(10)