Green AutoML: Energy-Efficient AI Deployment Across the Edge-Fog-Cloud Continuum

Dua, Amit; Singh Aujla, Gagangeet; Jindal, Anish; Sun, Hongjian

Abstract

The increasing demand for machine learning (ML) technologies has led to a significant rise in energy consumption and environmental impact, particularly within the context of distributed computing environments like the Edge-Fog-Cloud Continuum. This paper addresses the critical challenge of optimizing ML processes not only for performance but also for sustainability by introducing a novel Green Automated Machine Learning (AutoML) framework. The proposed framework integrates energy-aware task allocation into the AutoML pipeline, strategically distributing computational tasks across edge, fog, and cloud layers to minimize energy usage and carbon emissions without compromising model accuracy. To achieve this, the framework incorporates real-time monitoring and dynamic task allocation based on energy consumption, latency, and carbon footprint, utilizing a hierarchical approach that leverages the unique strengths of each layer within the continuum. The framework is supported by mathematical models that quantify energy consumption, communication latency, and environmental impact, offering a comprehensive metric for evaluating the sustainability of ML deployments. Through extensive simulations and real-world experiments, the framework demonstrates substantial improvements in energy efficiency and significant reductions in environmental footprint compared to traditional AutoML approaches. This research contributes to the advancement of sustainable AI by providing a practical solution for deploying ML models in a manner that balances performance with environmental responsibility.

Citation

Dua, A., Singh Aujla, G., Jindal, A., & Sun, H. (2024, December). Green AutoML: Energy-Efficient AI Deployment Across the Edge-Fog-Cloud Continuum. Presented at IEEE Global Communications Conference - Workshop on Next-Gen Healthcare Fusion (NgHF): AI-driven Secure Integrated Networks for Healthcare IoT Systems, Cape Town, South Africa

• Presentation Conference Type: Conference Paper (published)
• Conference Name: IEEE Global Communications Conference - Workshop on Next-Gen Healthcare Fusion (NgHF): AI-driven Secure Integrated Networks for Healthcare IoT Systems
• Start Date: Dec 8, 2024
• End Date: Dec 12, 2024
• Acceptance Date: Nov 12, 2024
• Deposit Date: Dec 12, 2024
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Keywords: Index Terms-Green Computing; AutoML; Edge-Fog-Cloud Continuum; Energy-Aware Optimization; Environmental Impact; Sustainable AI
• Public URL: https://durham-repository.worktribe.com/output/3216503
• Publisher URL: https://ieeexplore.ieee.org/xpl/conhome/1000308/all-proceedings