A Synthesis of Green Architectural Tactics for ML-Enabled Systems
- URL: http://arxiv.org/abs/2312.09610v1
- Date: Fri, 15 Dec 2023 08:53:45 GMT
- Title: A Synthesis of Green Architectural Tactics for ML-Enabled Systems
- Authors: Heli J\"arvenp\"a\"a, Patricia Lago, Justus Bogner, Grace Lewis, Henry
Muccini, Ipek Ozkaya
- Abstract summary: We provide a catalog of 30 green architectural tactics for ML-enabled systems.
An architectural tactic is a high-level design technique to improve software quality.
To enhance transparency and facilitate their widespread use, we make the tactics available online in easily consumable formats.
- Score: 10.300491626897502
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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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