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Generative Multimodal AI-Driven Lifecycle Assessment and Carbon Optimization of Cloud Infrastructure
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 94 |
| Year of Publication: 2026 |
| Authors: Satyendra Kumar Pal, Vikas Kumar, Sandeep Kumar Vishwakarma |
10.5120/ijca2026926624
|
Satyendra Kumar Pal, Vikas Kumar, Sandeep Kumar Vishwakarma . Generative Multimodal AI-Driven Lifecycle Assessment and Carbon Optimization of Cloud Infrastructure. International Journal of Computer Applications. 187, 94 ( Mar 2026), 32-41. DOI=10.5120/ijca2026926624
Abstract
The rapidly increasing demand worldwide for energy driven by artificial intelligence (AI) and cloud computing has created an imperative for carbon-smart infrastructure management. This paper presents a Generative Multimodal AI Framework for Lifecycle Assessment (LCA) to predict and optimize the environmental impact of cloud data centers. The model incorporates heterogeneous data sources including sensor telemetry, configuration text, and infrastructure images through transformer-based fusion and a diffusion-based generative core. Lifecycle emissions are estimated and minimized in real time and dynamically employing reinforcement-learning optimization. A real-world operational and inventory evaluation shows that our proposed framework approaches a 30% faster convergence, 25% lower lifecycle emissions, and 15% higher energy efficiency than either baseline transformer or static lifecycle assessment approach. An Explainability analysis conducted using Shapley additive explanations (SHAP) show that physically interpretable variables such as rack utilization and cooling load largely dominated the factors that influenced lawful predictions, thus the predictions were transparent and reliable. Overall, the results, underline that generative modeling applied to lifecycle analytics can rethink the sustainability management process to transform from retrospective assessments to a forward-looking adaptive self-optimizing system. The framework presented here contributes a reproducible pathway for carbon-aware cloud operations and a scalable benchmark for AI-enabled sustainability computing.
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