Calculating Software's Energy Use and Carbon Emissions: A Survey of the State of Art, Challenges, and the Way Ahead

TL;DR

This survey reviews current methods and tools for measuring software and AI energy consumption and carbon emissions, highlighting challenges and proposing a collaborative way forward for sustainable computing.

Contribution

It introduces a taxonomy categorizing measurement approaches and compares existing tools, addressing gaps and challenges in assessing software's environmental impact.

Findings

Existing tools vary in granularity and scope

Challenges include measurement accuracy and component coverage

Community collaboration is essential for progress

Abstract

The proliferation of software and AI comes with a hidden risk: its growing energy and carbon footprint. As concerns regarding environmental sustainability come to the forefront, understanding and optimizing how software impacts the environment becomes paramount. In this paper, we present a state-of-the-art review of methods and tools that enable the measurement of software and AI-related energy and/or carbon emissions. We introduce a taxonomy to categorize the existing work as Monitoring, Estimation, or Black-Box approaches. We delve deeper into the tools and compare them across different dimensions and granularity - for example, whether their measurement encompasses energy and carbon emissions and the components considered (like CPU, GPU, RAM, etc.). We present our observations on the practical use (component wise consolidation of approaches) as well as the challenges that we have identified across the current state-of-the-art. As we start an initiative to address these challenges, we emphasize active collaboration across the community in this important field.