Abstract

Predictive resource provisioning plays a crucial role in optimizing the performance and cost-effectiveness of cloud-based big data systems by dynamically allocating resources based on anticipated workload demands. This paper investigates machine learning models tailored for predictive resource provisioning in cloud-based big data systems. We explore various machine learning algorithms and techniques, including time series forecasting, regression analysis, and neural networks, for predicting resource requirements based on historical usage patterns, system metrics, and external factors. Additionally, we examine the integration of machine learning models with cloud orchestration platforms and auto-scaling mechanisms to enable proactive resource provisioning and workload management. Through empirical evaluation and case studies, we assess the accuracy, scalability, and efficiency of different machine learning models for predictive resource provisioning in real-world cloud-based big data systems. Furthermore, we discuss challenges, limitations, and future research directions in leveraging machine learning for predictive resource provisioning and optimization in cloud environments. This study aims to provide insights and guidelines for practitioners and researchers in harnessing the power of machine learning to enhance the performance and scalability of cloud-based big data systems through predictive resource provisioning.

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