A Synthesis of Green Architectural Tactics for ML-Enabled Systems
Heli Järvenpää Patricia Lago Justus Bogner Grace Lewis Henry Muccini Ipek Ozkaya
The rapid adoption of artificial intelligence (AI) and machine learning (ML)
has generated growing interest in understanding their environmental impact and
the challenges associated with designing environmentally friendly ML-enabled
systems. While Green AI research, i.e., research that tries to minimize the
energy footprint of AI, is receiving increasing attention, very few concrete
guidelines are available on how ML-enabled systems can be designed to be more
environmentally sustainable. In this paper, we provide a catalog of 30 green
architectural tactics for ML-enabled systems to fill this gap. An architectural
tactic is a high-level design technique to improve software quality, in our
case environmental sustainability. We derived the tactics from the analysis of
51 peer-reviewed publications that primarily explore Green AI, and validated
them using a focus group approach with three experts. The 30 tactics we
identified are aimed to serve as an initial reference guide for further
exploration into Green AI from a software engineering perspective, and assist
in designing sustainable ML-enabled systems. To enhance transparency and
facilitate their widespread use and extension, we make the tactics available
online in easily consumable formats. Wide-spread adoption of these tactics has
the potential to substantially reduce the societal impact of ML-enabled systems
regarding their energy and carbon footprint.