Green Cloud Computing: Strategies for Carbon-Neutral Data Centres

The untamed growth of cloud services generated substantial data center demands that caused major concerns about electricity consumption leading to greenhouse gas emissions. An evaluation takes place to identify various software solutions combined with operational methods that allow large-scale cloud facilities to achieve carbon neutrality objectives. The research investigates the effectiveness of power distribution units equipped with advanced liquid cooling systems integrated with modular rectifier-to-inverter configurations for obtaining zero-level PUE metrics. Systems optimizing resource management through DVFS as well as heterogeneous CPU–GPU scheduling and containerized microservices orchestration achieve maximum efficient computation energy usage. Portable Photovoltaic storage and wind power forecasting systems need to operate with intelligent demand-management protocols that shift time-sensitive operations when renewable resource production exceeds facility requirements. The article employs blockchain‑verified renewable‑energy credit approaches to function as carbon-offset methods while providing independent assessment about direct air capture at installation sites. The last portion shows automated virtual machine relocation systems which track carbon intensities by following changes in regional power emission rate levels. The prototype analysis proves that at least 45% of carbon emissions from cloud infrastructure deployment can decrease while maintaining existing Service Level Agreements for sustainable cloud infrastructure construction.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-25
DOI
https://doi.org/10.5281/zenodo.15352988
Primary Topic
Molecular Biology Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Green Cloud Computing: Strategies for Carbon-Neutral Data Centres

Zenodo (CERN European Organization for Nuclear Research)
Molecular Biology Techniques and Applications
article

Green Cloud Computing: Strategies for Carbon-Neutral Data Centres

article en

Abstract

The untamed growth of cloud services generated substantial data center demands that caused major concerns about electricity consumption leading to greenhouse gas emissions. An evaluation takes place to identify various software solutions combined with operational methods that allow large-scale cloud facilities to achieve carbon neutrality objectives. The research investigates the effectiveness of power distribution units equipped with advanced liquid cooling systems integrated with modular rectifier-to-inverter configurations for obtaining zero-level PUE metrics. Systems optimizing resource management through DVFS as well as heterogeneous CPU–GPU scheduling and containerized microservices orchestration achieve maximum efficient computation energy usage. Portable Photovoltaic storage and wind power forecasting systems need to operate with intelligent demand-management protocols that shift time-sensitive operations when renewable resource production exceeds facility requirements. The article employs blockchain‑verified renewable‑energy credit approaches to function as carbon-offset methods while providing independent assessment about direct air capture at installation sites. The last portion shows automated virtual machine relocation systems which track carbon intensities by following changes in regional power emission rate levels. The prototype analysis proves that at least 45% of carbon emissions from cloud infrastructure deployment can decrease while maintaining existing Service Level Agreements for sustainable cloud infrastructure construction.

Zenodo (CERN European Organization for Nuclear Research)
Shri Venkateshwara University (IN)
Industry, innovation and infrastructure
Openalex Percentile: Top 57%
Molecular Biology Techniques and Applications
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Green Cloud Computing: Strategies for Carbon-Neutral Data Centres · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS