Home / Current Issue / Paper 1702965
Sustainable Data Engineering Practices for Cloud Migration
Subject area: Science,Engineering and Technology · Area of research: Cloud Migration
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.
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How to cite this paper
@article{1702965,
author = {Sunil Gudavalli, Chandrasekhara Mokkapati, Dr. Umababu Chinta, Niharika Singh, Om Goel; Aravind Ayyagari},
title = {Sustainable Data Engineering Practices for Cloud Migration},
journal = {Iconic Research And Engineering Journals},
year = {2021},
volume = {5},
number = {5},
pages = {269-287},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1702965.pdf},
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.},
month = {November},
}