Emmanuel Flores Huicochea, National Polytechnic Institute, Mexico

In this paper, we provide an in-depth exploration of a dynamic power-saving model that has been meticulously designed to enhance the management of virtual machines (VMs) within large-scale cloud data centers. Our model focuses on three key areas: the consolidation of VMs to maximize resource utilization, the balancing of workloads to prevent bottlenecks, and the auto-scaling of resources to effectively respond to fluctuating demand. To facilitate this, we incorporate advanced algorithms that analyze real-time data, enabling the system to make informed decisions about resource allocation. This ensures that VMs are not only consolidated efficiently to minimize idle resources but also that workloads are distributed evenly across the computing infrastructure to maintain optimal performance. In addition to the dynamic model, we present a comprehensive power-saving evaluation framework designed to rigorously assess the effectiveness of our power conservation initiatives. This framework employs a variety of metrics to evaluate energy efficiency, including but not limited to total energy consumption, cost savings, and carbon footprint reduction. What sets our models apart is their robustness; they are designed to adapt seamlessly to changes within the cloud environment. Whether new computing servers are being integrated into the system or existing ones are being decommissioned, our power-saving model ensures that the overall efficiency and sustainability of the cloud infrastructure are maintained. By employing this innovative approach, our research aims to not only improve the operational efficiency of cloud data centers but also contribute toward a more sustainable computing future, significantly reducing the environmental impact of cloud services.

APA:
Huicochea, E. F. (2021). Comprehensive Dynamic Power-Saving Model for Optimizing Energy Efficiency in Cloud Computing Systems. Journal of Science and Engineering Management, 2(3), 1-10. https://doi.org/10.33832/jsem.2021.2.3.01