A Synthesis of Green Architectural Tactics for ML-Enabled Systems

Grace Lewis; Heli J\"arvenp\"a\"a; Henry Muccini; Ipek Ozkaya; Justus Bogner; Patricia Lago

A Synthesis of Green Architectural Tactics for ML-Enabled Systems

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arxiv 2312.09610 v3 pith:LHVVV2TF submitted 2023-12-15 cs.SE cs.LG

Heli Järvenpää , Patricia Lago , Justus Bogner , Grace Lewis , Henry Muccini , Ipek Ozkaya This is my paper · ORCID

classification cs.SEcs.LG

keywords ml-enabledsystemstacticsgreenarchitecturaladoptionavailabledesigning

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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.

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