A method to manage the energy consumption of cloud centers for predictability in neuro-fuzzy networks

Abstract With the advancement of rapid communication and information technology, the demand for efficient and reliable cloud computing services has gradually grown. Power consumption at the cloud data centers becomes a challenging issue for such demanding requirements since user workloads are mostly unpredictable and exhibit a dynamic nature. This paper proposes a hybrid technique for energy optimization in cloud data centers. Neuro-fuzzy networks, integrated into the workload prediction mechanism, are used together with the Ant Colony Optimization (ACO) framework for virtual machine (VM) migration and placement. The main goal of this work is the minimization of the number of active servers by fulfilling all user requests without increasing energy consumption. The proposed model leverages neuro-fuzzy networks for accurate workload predictions to achieve efficient real-time resource allocation and VM migration strategies that minimize energy waste. The empirical results indicate that the proposed approach minimizes energy consumption with a low request rejection rate. Key contributions of this work are as follows: the efficiency of resource allocation has been enhanced; the real-time load is better predictable; operational cost is reduced; and, consequently, the profitability of cloud service providers has increased. This framework further proposes to help increase customer satisfaction and competitiveness in the cloud market by ensuring that cloud services are delivered reliably and efficiently. The work is applied to the very important sustainability issue of cloud computing, providing a robust framework suitable for the dynamic and nonlinear behaviors of cloud environments. The results underlined the potential of predictive models combined with optimization algorithms for significant energy savings and operational efficiency in cloud data centers.