# Optimizing Energy Efficiency in Cloud Data Centers:
## 8 Conclusions and Future Work  
Energy consumption is a persistent challenge in cloud data centers, which prompted us to develop an energy-sensitive management algorithm. Our approach focuses on optimizing virtual machine deployment to minimize the number of active physical machines, thereby reducing overall energy consumption in cloud environments. Leveraging reinforcement-learning techniques, our methodology aims to achieve significant energy savings, aligning with sustainable practices crucial to modern data center operations. Our research highlights the effectiveness of machine learning in achieving energy savings targets. Through rigorous experimentation and validation, we have demonstrated that reinforcement learning can adaptively optimize resource allocation, ensuring efficient use of IT resources while maintaining service level agreements (SLAs). This capability not only reduces operational costs but also contributes to environmental sustainability by reducing the carbon footprint associated with data center operations. In the future, our efforts will extend to optimizing thermal energy management and improving cooling systems in data centers. These advances are essential if we are to meet the multiple challenges posed by escalating energy demand and heat dissipation issues in cloud infrastructures. By delving deeper into these areas, we aim to develop innovative solutions that improve both the ecological footprint and operational efficiency of cloud computing environments.  
In conclusion, our ongoing research seeks to advance the field of energy-efficient data center management through the integration of advanced machine learning techniques and sustainable infrastructure practices. By continuously refining our methodologies and exploring new avenues for improvement, we strive to contribute meaningfully to the long-term sustainability and performance optimization of cloud data centers worldwide.  
In this study, we used the SLA_TAH metric to track hosts that experienced overload, as it is widely adopted in the literature and provides a straightforward indication based on CPU saturation. However, other factors such as memory usage, disk I/O, and network bandwidth can also contribute to host overload. In future work, we plan to adopt a more general and comprehensive metric that takes into account these multi-resource aspects, in order to better reflect the real performance state of cloud hosts and support more accurate and adaptive management decisions.  
We also plan to broaden our research to include the optimization of thermal energy management and the enhancement of cooling systems within data centers. These initiatives are driven by the critical need to further address energy consumption challenges and enhance the overall efficiency of cloud computing infrastructure. By exploring these areas in depth, we aim to develop innovative solutions that not only reduce environmental impact but also bolster the sustainability and performance of data center operations. Additionally, we plan to test our approach on larger-scale simulations, potentially using tools like CloudSim Plus or iCanCloud. We are also exploring integration with real-time cloud orchestration frameworks to assess performance in production-like environments.  
A.A.: Conceptualization, Methodology, Software, Formal analysis, Writing-original draft, Visualization, Investigation, Data Curation. Y.S.: Conceptualization, Methodology, Supervision, Visualization, Investigation, Formal analysis, Writing--review & editing, Data Curation. S.E.K.: Conceptualization, Methodology, Visualization, Investigation, Project administration, Formal analysis, Writing--review & editing, Data Curation. All authors have read and agreed to the published version of the manuscript. This research has no funding.  
Data Availability Statement: Data is contained within the article.  
The authors declare that they have no competing interests.