AI-Driven Carbon-Aware Orchestration for Sustainable Cloud Deployments
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V5I2P117Keywords:
Carbon-Aware Computing, AI Orchestration, Sustainable Cloud, Carbon Intensity Forecasting, Green Software Engineering, Energy-Aware Scheduling, Workload Optimization, Cloud Sustainability, Renewable Energy Matching, Carbon Footprint ReductionAbstract
As organizations digitally transform at a rapid pace, the need for carbon-aware computing has become very evident. This trend is leading to a complete rethink of how workloads are deployed and managed by cloud adopters. In spite of significant progress made to improve cloud efficiency, the issue of energy remains a major challenge because of fluctuating energy demands, limited visibility into real-time carbon intensity, and the awkwardness of aligning performance goals with an environmental impact. This paper shows how AI can help to close this gap by providing a dynamic carbon-aware orchestration that can move or schedule workloads based on real-time or predicted emissions data from different regions and cloud providers. Our approach is a combination of carbon intensity feeds, forecasting, and an AI orchestration-driven engine capable of making deployment decisions that balance sustainability, cost, and latency. The results reveal that AI-driven orchestration is not only capable of pinpointing the greenest compute windows and locations but can also change automatically with the prevailing situations, thus being more effective than static rule-based methods. Moreover, the study uncovers additional benefits such as the increased transparency of the environmental impact, improved decision-making for DevOps teams, and a recyclable architecture that can evolve with EMA and AI forecasting model developments. The importance of carbon-aware orchestration will become even more pronounced as the demand for cloud services keeps on increasing. Thus, AI will have an indispensable role in enabling sustainable cloud operations.
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