Carbon Footprint Optimization in Cloud Data Centers Using Cloud Computing

Prathyusha Gudise; Arulmozhi P

Carbon Footprint Optimization in Cloud Data Centers Using Cloud Computing

Author(s)	Ms. Prathyusha Gudise, Prof. Dr. Arulmozhi P
Country	India
Abstract	The rapid growth of cloud-based applications has increased the computational workload handled by modern data centers. Continuous operation of servers, storage systems, networking equipment, and supporting infrastructure results in substantial energy consumption and contributes to carbon emissions. Conventional cloud scheduling generally concentrates on performance, resource utilization, execution time, and cost, while environmental factors are often considered separately. This work presents a carbon-aware cloud resource management framework that combines machine learning-based prediction with adaptive resource allocation. The proposed framework uses cloud workload and energy-consumption datasets obtained from Kaggle. Python is used for data preparation and predictive analysis, while MATLAB is used for optimization, simulation, and performance evaluation. Two algorithms, Carbon-Aware Adaptive Resource Balancing (CAARB) and Dynamic Green Load Prediction Optimizer (DGLPO), are introduced. CAARB adjusts virtual-machine allocation according to workload requirements, resource availability, and estimated carbon impact. DGLPO uses predicted workload and energy requirements to support proactive scheduling and reduce unnecessary resource activation. Carbon emissions are estimated from energy consumption and carbon intensity. The resulting framework is evaluated using energy consumption, carbon emissions, resource utilization, operational cost, execution time, makespan, prediction accuracy, and Quality of Service (QoS). Results reported in the project indicate reduced energy usage and estimated carbon footprint, improved resource utilization, and more adaptive workload scheduling compared with conventional approaches. The framework provides a practical foundation for developing more sustainable cloud data center management systems.
Keywords	Cloud computing, carbon footprint, green computing, cloud data centers, machine learning, resource allocation, workload prediction, CAARB, DGLPO.
Field	Engineering
Published In	Volume 8, Issue 5, September-October 2026
Published On	2026-09-24

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International Journal For Multidisciplinary Research

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Carbon Footprint Optimization in Cloud Data Centers Using Cloud Computing

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