GEECO: Green Data Centers for Energy Optimization and Carbon Footprint Reduction

Abstract

Cloud computing has revolutionized data storage, processing, and access in modern data center operations. Conventional data centers use enormous amounts of energy for server operation, power supply, and cooling. The processors produce heat while processing the data and therefore increase the center’s carbon footprint, and the rising energy usage and carbon emissions caused by data centers pose serious environmental challenges. Under these circumstances, energy-efficient green data centers are being used as a phenomenal source of sustainable modernization. This study proposes the implementation of the Green Energy Efficiency and Carbon Optimization (GEECO) model for enhancing energy usage. Within the data center, the GEECO model dynamically adjusts workload distribution and task assignment to balance performance and manage service-level reconciliation. The ability to identify possibilities for energy optimization and carbon emission reduction is possible through real-time monitoring of energy usage and workload demand. The results revealed a considerable increase in energy efficiency, with significant decreases in energy usage and related costs. The GEECO model provides a significant improvement in energy consumption and carbon emission reduction for the different introduced scenarios. This model’s introduction to practical application would be made possible by these improvements in the quantitative results. The approach of this study also has a positive impact on the environment by reducing carbon emissions. The resilience and practicality of the solution are also analyzed, highlighting the probability of widespread adoption and its associated improvements in the advancement of sustainable cloud computing.

Sustainable Engineering of Machine Learning-Enabled Systems: A Systematic Mapping Study

“…Studies such as [83,104,157] introduce data management frameworks that significantly improve the efficiency and performance of MLES, which illustrate the crucial aspects of data management in bettering ML systems. The work by Mondal et al [157] on the GEECO model prioritizes the environmental sustainability of data centers, underlining the significance of eco-friendly practices in data processing and storage. Snorkel by Ratner et al [83] introduces an approach to rapidly generate labeled training data through weak supervision, which becomes a pivotal part of the retraining process.…”