Enhancing Energy Efficiency in Distributed Systems through Code Refactoring and Data Analytics
Takudzwa Fadziso, Aditya Manikyala, Hari Priya Kommineni, Satya Surya MKLG Gudimetla Naga Venkata · Asia Pacific Journal of Energy and Environment · 2023
This research examines code restructuring and data analytics to improve distributed system energy efficiency. The main goal is to optimize software design and use data-driven insights to decrease energy usage without compromising performance. The secondary data-based assessment examines code refactoring methods like algorithm optimization and memory management and data analytics tools like predictive models and real-time monitoring. Key findings show that code refactoring streamlines algorithms, reduces redundant processes, and improves task distribution. At the same time, data analytics enables adaptive energy management through predictive forecasting, anomaly detection, and dynamic resource allocation. Combining these methods yields a scalable distributed energy efficiency solution. However, ongoing data processing energy costs and integration complexity persist. The report emphasizes the need for incentives for technology investments, training, and established best practices to promote energy-efficient distributed systems. These results indicate that a balanced strategy combining code optimization and powerful data analytics may maintain and improve energy efficiency in the continually changing distributed computing ecosystem.