Code Refactoring for Energy-Saving Distributed Systems: A Data Analytics Approach

Aditya Manikyala

doi:10.18034/apjee.v11i1.780

Authors

Aditya Manikyala Java AWS Developer, DPR Solutions Inc., 20130 Lakeview center plaza, Ashburn, VA 20147, USA

DOI:

https://doi.org/10.18034/apjee.v11i1.780

Keywords:

Code Refactoring, Energy Efficiency, Distributed Systems, Data Analytics, Energy Profiling, Sustainable Computing, Performance Optimization, Green Computing

Abstract

This research uses data analytics and code refactoring to improve distributed system energy usage. The goal is to provide a framework for energy profiling, performance monitoring, and predictive analytics to discover inefficiencies and save energy. Secondary data analysis is used to analyze research and case studies on energy-aware refactoring and distributed computing data analytics. Energy profiling is essential for discovering inefficiencies, while algorithm improvement, intelligent job allocation, and redundancy reduction considerably cut energy use. Predictive analytics allows dynamic energy optimization, and real-time feedback loops optimize energy-saving measures. The report also notes data accuracy, computational overhead, and energy efficiency-system performance balance issues. The policy implications include industry standards, clear guidelines, and government incentives required to disseminate energy-efficient code. By promoting energy-aware refactoring, these rules might create more sustainable and cost-effective distributed systems. This study emphasizes data-driven energy efficiency in distributed systems and advances sustainable computing expertise.

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