Minimizing the operation cost of distributed green data centers with energy storage under carbon capping

He, Hong; Shen, Hong

doi:10.1016/j.jcss.2020.11.004

Cited by 8 publications

(2 citation statements)

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“…It will also provide an overview of green computing and cloud computing and several fields of green IT. However, two factors make it difficult to accomplish carbon capping by 50 percent while keeping costs to a minimum: One of them is that it is challenging to decide when to schedule online batteries in order to minimize operating costs due to dynamically shifting elements such as energy prices, workload arrivals, and renewable energy [20]. This study also examines the environmental viability of cloud computing and proposes strategies to reduce carbon emissions through the adoption of green energy sources [30].…”

Section: Literature Reviewmentioning

confidence: 99%

“…They offered a unique I/O offset mechanism and a power-fairness credit sequencing methodology to enable quick I/O performance for promoting energy saving. The most recent report [20] states that Google's carbon footprint for 2015 was 2.99 million metric tons, which is 79.04% more than it was for 2011. Therefore, the urgent issue facing sustainable data centers is how to lessen carbon emissions.…”

Section: Introductionmentioning

confidence: 99%

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GEECO: Green Data Centers for Energy Optimization and Carbon Footprint Reduction

Mondal,

Faruk,

Rajbongshi

et al. 2023

Sustainability

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Cloud computing has revolutionized data storage, processing, and access in modern data center operations. Conventional data centers use enormous amounts of energy for server operation, power supply, and cooling. The processors produce heat while processing the data and therefore increase the center’s carbon footprint, and the rising energy usage and carbon emissions caused by data centers pose serious environmental challenges. Under these circumstances, energy-efficient green data centers are being used as a phenomenal source of sustainable modernization. This study proposes the implementation of the Green Energy Efficiency and Carbon Optimization (GEECO) model for enhancing energy usage. Within the data center, the GEECO model dynamically adjusts workload distribution and task assignment to balance performance and manage service-level reconciliation. The ability to identify possibilities for energy optimization and carbon emission reduction is possible through real-time monitoring of energy usage and workload demand. The results revealed a considerable increase in energy efficiency, with significant decreases in energy usage and related costs. The GEECO model provides a significant improvement in energy consumption and carbon emission reduction for the different introduced scenarios. This model’s introduction to practical application would be made possible by these improvements in the quantitative results. The approach of this study also has a positive impact on the environment by reducing carbon emissions. The resilience and practicality of the solution are also analyzed, highlighting the probability of widespread adoption and its associated improvements in the advancement of sustainable cloud computing.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%