Abstract
The abstract starts by introducing the growing significance of sustainability within IT infrastructures and cloud-based solutions. As companies increasingly shift their data and applications to the cloud, there is a pressing need to address environmental concerns. Traditional cloud migration strategies are primarily focused on achieving technical performance and cost efficiency, often overlooking sustainability. This research proposes a framework that integrates sustainable practices into the cloud migration lifecycle, offering an approach that is environmentally conscious without compromising on performance and scalability. To achieve this, the study examines existing literature on both cloud migration strategies and sustainable IT practices, identifying a gap where sustainability often lacks consideration. By bridging this gap, the research aims to establish a set of best practices for data engineering that minimizes energy consumption, optimizes storage, and reduces redundant data processing. This framework considers several aspects, including data architecture, resource allocation, workload distribution, and energy-efficient coding practices, to minimize the environmental impact of cloud operations. The methodology section of the research involves both theoretical and practical components, utilizing simulated cloud environments to test the proposed sustainable practices. This simulated setup allows for controlled experiments, wherein key performance indicators such as energy consumption, latency, and processing speed are measured and analyzed. The study applies two approaches in these simulations: a traditional migration approach and an optimized, sustainability-focused one. By comparing results from both setups, the study highlights tangible differences in energy usage, resource allocation, and efficiency. The results show that adopting sustainable data engineering practices can significantly reduce energy costs and resource consumption without compromising cloud performance. For example, optimizing data storage by reducing duplicate data or using energy-efficient resource allocation can lead to measurable improvements in both sustainability and cost savings. This is particularly relevant for industries handling large datasets, such as finance, healthcare, and e-commerce, where data processing demands are high.
Keywords
Cloud Migration, Data Engineering, Sustainable Practices, Data Management, Environmental Impact, Resource Optimization, Scalability, Automation.
Citations
IRE Journals:
Sunil Gudavalli , Chandrasekhara Mokkapati , Dr. Umababu Chinta , Niharika Singh , Om Goel; Aravind Ayyagari
"Sustainable Data Engineering Practices for Cloud Migration" Iconic Research And Engineering Journals Volume 5 Issue 5 2021 Page 269-287
IEEE:
Sunil Gudavalli , Chandrasekhara Mokkapati , Dr. Umababu Chinta , Niharika Singh , Om Goel; Aravind Ayyagari
"Sustainable Data Engineering Practices for Cloud Migration" Iconic Research And Engineering Journals, 5(5)
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IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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