Cloud Carbon Footprint Tracker for Sustainable Practices
Keywords:
Type Carbon Footprint Tracker, Azure Cost Management, Sustainability Reporting, Machine Learning, Artificial Intelligence, Cloud Computing, Carbon Emission Reduction, Energy Efficient ComputingAbstract
In today’s digital era, businesses heavily rely on cloud-based solutions to support their operations, leading to substantial carbon emissions due to the extensive use of cloud resources. As organizations increasingly adopt cloud computing, the environmental impact of these infrastructures has become a growing concern. To address this issue, this paper presents a Carbon Footprint Tracker for Cloud Resources, a comprehensive solution that leverages cloud analytics and artificial intelligence (AI) to measure, analyze, and minimize carbon emissions. Our proposed system integrates seamlessly with Azure Cost Management APIs, Azure Monitor, and advanced Machine Learning models to provide organizations with real-time insights into their cloud consumption and its associated carbon footprint. By analyzing usage patterns and optimizing resource allocation, the system offers data-driven recommendations to enhance sustainability. Additionally, it generates detailed sustainability reports, enabling businesses to track their environmental impact and make informed decisions toward greener cloud strategies. Through this innovative approach, enterprises can effectively reduce their carbon footprint, improve operational efficiency, and align with global sustainability goals. By embracing eco-friendly cloud practices, organizations can contribute to a more sustainable future, ensuring responsible and energy-efficient cloud usage.
References
Liu, Y., Xu, Z., & Lu, W. (2020). Towards green cloud computing: A comprehensive survey on sustainability, energy efficiency, and resource management. In Future Generation Computer Systems (pp. 145-164).
Zhou Z., & Zhang X. (2018). Green cloud computing: A survey on energy efficiency, sustainability, and resource management. Energy Reports, 4, 107-118.
Yousef M., & Keshav S. (2021). Carbon footprint reduction in cloud computing through resource optimization and energy efficiency. IEEE/ACM CLOUD, 290-299.
Khan I., & Ameen A. (2020). AI and machine learning for cloud energy optimization: An approach to sustainability. Journal of Cloud Computing: Advances, Systems, and Applications, 9(1), 1-13.
IBM Research. (2022). Cloud Optimization Using AI and Predictive Analytics. IBM Journal of Research & Development, 66(4), 23-38.
Aamerkhan G., Umerkhan G., Mohammed A.A. Enhancing Cybersecurity Through Artificial Intelligence: Techniques, Challenges, and Future Directions
Microsoft. (2023). Azure Cost Management Documentation. Retrieved from: https://learn.microsoft.com/en-us/azure/cost-management-billing/
Microsoft. (2023). Azure Monitor Documentation. Retrieved from: https://learn.microsoft.com/en-us/azure/monitoring/
AWS. (2022). AWS Carbon Footprint Calculator. Retrieved from: https://aws.amazon.com/sustainability/carbon-footprint-calculator/
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