Sustainable Energy Management Framework for Cloud Services - Malik Shahzad Ahmed Iqbal; Mohd Haroon (Utilitas Mathematica, 2025)
The proposed framework integrates energy-aware scheduling algorithms, dynamic resource allocation, and renewable energy sources to minimize power consumption in cloud data centres. By implementing AI-driven workload balancing and virtual machine (VM) migration techniques, SEMF effectively reduces energy wastage while ensuring optimal resource utilization. The framework incorporates real-time energy monitoring and predictive analytics to proactively adjust power consumption based on workload fluctuations. Our experimental analysis demonstrates that SEMF outperforms traditional energy management approaches, achieving a reduction in energy consumption by up to 30% while maintaining system performance. The integration of green energy sources further enhances the framework’s sustainability, reducing dependency on non-renewable energy. By implementing SEMF, cloud service providers can achieve a balance between energy efficiency and performance, leading to reduced operational costs and a lower carbon footprint. This research highlights the potential of intelligent energy management strategies in creating a sustainable, eco-friendly cloud computing environment.