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This paper introduces a novel framework for cloud resource management that leverages resource oversubscription and heterogeneous service pricing to enhance profitability and operational efficiency for cloud providers. By employing an adaptive ensemble machine learning model for predicting resource utilization and clustering similar VMs using Fuzzy C-means, the framework minimizes resource wastage and optimizes VM deployment. Experimental results demonstrate a significant reduction in electricity costs by 55.56% and improvements in resource utilization and profits by up to 60% and 51.18%, respectively.
A smart cloud resource management framework can cut electricity costs by over half while boosting profits and efficiency through advanced predictive modeling and VM clustering.
This article proposed a novel industry cloud resource management framework that exploits resource oversubscription and heterogeneous service pricing models to maximize profitability and operational efficiency for industry cloud providers. The framework proposes an adaptive ensemble machine learning driven prediction model for proactive estimation of resource utilization of Virtual Machines (VM)s-based on previous resource utilization of respective users’ VMs to minimize resource wastage due to oversubscription by them. Accordingly, the VMs having similar predicted resource usage are grouped using Fuzzy C-means clustering. This helps to determine the required number of VMs with specific configuration to be deployed before executing user requests. Concurrently, the framework incorporates two distinct categories of cloud service pricing models, namely the <italic>Delay Sensitive Model</italic> and the <italic>Best-Effort Model</italic>. Accordingly, the user requests are classified and executed by selecting the most suitable VMs, with the goal of maximizing revenue and reducing electricity costs in cloud data centers (<inline-formula><tex-math notation="LaTeX">$\mathbb{C}\mathbb{D}\mathbb{C}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant="double-struck">C</mml:mi><mml:mi mathvariant="double-struck">D</mml:mi><mml:mi mathvariant="double-struck">C</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="saxena-ieq1-3445379.gif"/></alternatives></inline-formula>s). Experimental simulation and comparison against state-of-the-art methods, using two benchmark VM traces, validates the performance of proposed framework. It significantly reduces electricity bills by 55.56%, power consumption and active servers by up to 60.7% and 51%, respectively, while improving resource utilization and profits by up to 60% and 51.18%, respectively.