Green Software

Research Area

Green Software, Machine learning, Software Architecture  


The objective is to address the issue of rising energy consumption and carbon emissions caused by the growing dependence on technology. The aim is to develop environmentally sustainable software by leveraging machine learning techniques. The primary objective is to enhance the green performance of machine learning algorithms, and subsequently explore how they can be utilized to optimize energy consumption and reduce waste. 


The increasing reliance on technology has led to a significant increase in energy consumption and carbon emissions, making it imperative to environmentally sustainable design software. One promising approach to achieving this goal is making software “green" using machine learning. Machine learning algorithms can optimize software design and minimize energy consumption and waste, thereby reducing the environmental impact of software. 

This research aims to explore the potential of machine learning in creating environmentally sustainable software. The first step will be investigating ways to make machine learning “green" by optimizing energy consumption and reducing waste. The second phase of the research will focus on using machine learning algorithms to optimize energy consumption and reduce waste in the software development process.  


Energy efficiency, Green AI, CO2 emission, Accuracy