Cloud data centers form the backbone of modern digital infrastructure but are also among the largest consumers of electrical energy. Rapid growth in cloud services has intensified concerns related to energy consumption, carbon emissions, and operational sustainability. Efficient resource scheduling has emerged as a key mechanism for reducing energy usage while maintaining service quality. This research paper investigates energy-efficient resource scheduling strategies in sustainable cloud data centers. A systematic scheduling framework is proposed that integrates workload characterization, virtual machine allocation, and dynamic power management. Simulation-based analysis demonstrates that energy-aware scheduling significantly reduces power consumption and improves resource utilization without violating quality-of-service constraints. The study highlights practical implications for cloud service providers and contributes to the development of greener and more sustainable cloud computing environments.

This study examines energy-efficient resource scheduling for sustainable cloud data centers, aiming to reduce energy consumption while preserving acceptable Quality of Service (QoS). It proposes an integrated scheduling framework that combines workload classification, virtual machine consolidation, and dynamic power management. The framework was evaluated using a simulation environment reflecting realistic cloud data center configurations. Results show that the proposed approach significantly lowers total energy consumption compared with First-Come-First-Serve, non-energy-aware, and DVFS-based scheduling methods. Energy savings are primarily achieved by consolidating workloads onto fewer physical servers and placing underutilized hosts into low-power or sleep states. Consequently, the framework increases average server utilization, indicating more efficient use of computing resources. Importantly, these energy reductions do not lead to performance degradation. The proposed scheduler maintains a low SLA violation rate, demonstrating that QoS requirements are satisfied even under aggressive energy optimization. This ability to balance energy efficiency with service reliability highlights the practical applicability of the framework in real cloud environments. Overall, the findings confirm that intelligent and integrated resource scheduling is essential for improving the sustainability of cloud data centers. By jointly optimizing workloads, utilization, and power control, providers achieve major energy savings while preserving dependable, high-quality cloud services at scale globally.

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