🤖 AI Summary
This study addresses the scarcity of practical approaches for effectively measuring and reducing carbon emissions in real-world computing systems. Conducted on a public cloud platform, this work implements green software practices on an online production service by innovatively integrating the cost-driven mechanisms of serverless architectures with carbon emission estimation. Through energy-efficiency optimization techniques, the system’s carbon footprint is significantly reduced. The project demonstrates that cost optimization can serve as an effective indirect driver for improving energy efficiency. Furthermore, it distills a set of reusable and actionable principles for green software engineering, offering empirical insights and methodological support for sustainable software development in industrial settings.
📝 Abstract
The need and theoretical methods for measuring and reducing CO2 emitted by computing systems are well understood, but real-world examples are still limited. We describe a journey towards green software for a live product running on a public cloud. We discuss practical solutions found, in particular using the cost implications of serverless systems to drive efficiency. We end with some `green software'principles that worked well in this project.