Do Developers Adopt Green Architectural Tactics for ML-Enabled Systems? A Mining Software Repository Study

• URL: http://arxiv.org/abs/2410.06708v1
• Date: Wed, 9 Oct 2024 09:27:07 GMT
• Title: Do Developers Adopt Green Architectural Tactics for ML-Enabled Systems? A Mining Software Repository Study
• Authors: Vincenzo De Martino, Silverio Martínez-Fernández, Fabio Palomba,
• Abstract summary: Machine learning (ML) and artificial intelligence (AI) technologies are increasingly prevalent in society. Green AI has emerged as a response, advocating for reducing the computational demands of AI while maintaining accuracy. This paper presents a mining software repository study that evaluates the adoption of green tactics in 168 open-source ML projects on GitHub.
• Score: 10.997873336451498
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As machine learning (ML) and artificial intelligence (AI) technologies become increasingly prevalent in society, concerns about their environmental sustainability have grown. Developing and deploying ML-enabled systems, especially during training and inference, are resource-intensive, raising sustainability issues. Green AI has emerged as a response, advocating for reducing the computational demands of AI while maintaining accuracy. While recent research has identified various green tactics for developing sustainable ML-enabled systems, there is a gap in understanding the extent to which these tactics are adopted in real-world projects and whether additional, undocumented practices exist. This paper addresses these gaps by presenting a mining software repository study that evaluates the adoption of green tactics in 168 open-source ML projects on GitHub. In doing so, we introduce a novel mining mechanism based on large language models to identify and analyze green tactics within software repositories. Our results provide insights into the adoption of green tactics found in the literature and expand previous catalogs by providing 12 new tactics, with code examples to support wider implementation. This study contributes to the development of more sustainable ML systems by identifying adopted green tactics that offer substantial environmental benefits with minimal implementation effort. It provides practical insights for developers to green their systems and offers a path for future research to automate the integration of these tactics.

Related papers

• Green Federated Learning: A new era of Green Aware AI [11.536626349203361]
  Federated Learning (FL) presents new opportunities to address this need. It's crucial to furnish researchers, stakeholders, and interested parties with a roadmap to navigate and understand existing efforts and gaps in green-aware AI algorithms. This survey primarily aims to achieve this objective by identifying and analyzing over a hundred FL works.
  arXiv  Detail & Related papers  (2024-09-19T09:54:18Z)
• Estimating the Energy Footprint of Software Systems: a Primer [56.200335252600354]
  quantifying the energy footprint of a software system is one of the most basic activities. This document aims to be a starting point for researchers who want to begin conducting work in this area.
  arXiv  Detail & Related papers  (2024-07-16T11:21:30Z)
• Towards Green AI: Current status and future research [0.3749861135832072]
  We aim to broaden the discourse on Green AI by investigating the current status of approaches to both environmental assessment and ecodesign of AI systems. We conduct an exemplary estimation of the carbon footprint of relevant compute hardware and highlight the need to further investigate methods for Green AI. We envision that AI could be leveraged to mitigate its own environmental challenges, which we denote as AI4greenAI.
  arXiv  Detail & Related papers  (2024-05-01T08:10:01Z)
• Learn to Code Sustainably: An Empirical Study on LLM-based Green Code Generation [7.8273713434806345]
  We evaluate the sustainability of auto-generate codes produced by generative commercial AI language models. We compare the performance and green capacity of human-generated code and code generated by the three AI language models.
  arXiv  Detail & Related papers  (2024-03-05T22:12:01Z)
• Open-world Machine Learning: A Review and New Outlooks [83.6401132743407]
  This paper aims to provide a comprehensive introduction to the emerging open-world machine learning paradigm. It aims to help researchers build more powerful AI systems in their respective fields, and to promote the development of artificial general intelligence.
  arXiv  Detail & Related papers  (2024-03-04T06:25:26Z)
• A Synthesis of Green Architectural Tactics for ML-Enabled Systems [9.720968127923925]
  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.
  arXiv  Detail & Related papers  (2023-12-15T08:53:45Z)
• On the Opportunities of Green Computing: A Survey [80.21955522431168]
  Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades. The needs for high computing power brings higher carbon emission and undermines research fairness. To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
  arXiv  Detail & Related papers  (2023-11-01T11:16:41Z)
• A Systematic Review of Green AI [8.465228064780744]
  Green AI is the study of AI environmental sustainability. The topic experienced a considerable growth from 2020 onward. From this review emerges that the time is suitable to adopt other Green AI research strategies.
  arXiv  Detail & Related papers  (2023-01-26T11:41:46Z)
• Towards Green Automated Machine Learning: Status Quo and Future Directions [71.86820260846369]
  AutoML is being criticised for its high resource consumption. This paper proposes Green AutoML, a paradigm to make the whole AutoML process more environmentally friendly.
  arXiv  Detail & Related papers  (2021-11-10T18:57:27Z)
• Technology Readiness Levels for Machine Learning Systems [107.56979560568232]
  Development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. We have developed a proven systems engineering approach for machine learning development and deployment. Our "Machine Learning Technology Readiness Levels" framework defines a principled process to ensure robust, reliable, and responsible systems.
  arXiv  Detail & Related papers  (2021-01-11T15:54:48Z)
• Technology Readiness Levels for AI & ML [79.22051549519989]
  Development of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. Engineering systems follow well-defined processes and testing standards to streamline development for high-quality, reliable results. We propose a proven systems engineering approach for machine learning development and deployment.
  arXiv  Detail & Related papers  (2020-06-21T17:14:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.