Toward Green Clouds: Sustainable Practices and Energy-Efficient Solutions in Cloud Computing

Authors

  • Ravikiran Mahadasa Infosys, India
  • Pavani Surarapu California State University, USA

DOI:

https://doi.org/10.18034/apjee.v3i2.713

Keywords:

Green Cloud Computing, Sustainable Practices, Energy Efficiency, Environmental Impact, Green Technology

Abstract

This article explores the imperative shift "Toward Green Clouds," investigating sustainable practices and energy-efficient solutions in cloud computing. Examining the environmental impact of traditional cloud infrastructures, the study identifies critical energy consumption patterns, carbon emissions, and resource depletion. Strategies for enhancing energy efficiency, including advanced cooling technologies, server virtualization, and renewable energy integration, are elucidated as pivotal components for mitigating environmental consequences. The article introduces conceptual frameworks rooted in ecological modernization and triple bottom line considerations, providing a structured roadmap for stakeholders. It underscores the significance of policy interventions, Green Cloud Certification Programs, and continuous improvement initiatives. The major findings highlight a transformative journey toward environmentally responsible cloud computing practices, emphasizing a balance between technological innovation and ecological stewardship for the realization of "Green Clouds."

Metrics

Metrics Loading ...

Downloads

Download data is not yet available.

References

Anderson, J., & Smith, R. (2014). Sustainable data centers: A review of current strategies. Journal of Green Computing, 2(3), 145-162.

Baddam, P. R., & Kaluvakuri, S. (2016). The Power and Legacy of C Programming: A Deep Dive into the Language. Technology & Management Review, 1, 1-13.

Bynagari, N. B. (2014). Integrated Reasoning Engine for Code Clone Detection. ABC Journal of Advanced Research, 3(2), 143-152. DOI: https://doi.org/10.18034/abcjar.v3i2.575

Bynagari, N. B. (2015). Machine Learning and Artificial Intelligence in Online Fake Transaction Alerting. Engineering International, 3(2), 115-126. DOI: https://doi.org/10.18034/ei.v3i2.566

Ganapathy, A. (2015). AI Fitness Checks, Maintenance and Monitoring on Systems Managing Content & Data: A Study on CMS World. Malaysian Journal of Medical and Biological Research, 2(2), 113-118. DOI: https://doi.org/10.18034/mjmbr.v2i2.553

Johnson, P., & Davis, R. (2013). Energy-efficient data center architectures: A comparative study. International Journal of Green IT, 7(1), 28-42.

Mahadasa, R. (2015). Blockchain Integration in Cloud Computing: A Promising Approach for Data Integrity and Trust. Technology & Management Review, 1, 14-20.

Neogy, T. K., & Paruchuri, H. (2014). Machine Learning as a New Search Engine Interface: An Overview. Engineering International, 2(2), 103-112. DOI: https://doi.org/10.18034/ei.v2i2.539

Paruchuri, H. (2015). Application of Artificial Neural Network to ANPR: An Overview. ABC Journal of Advanced Research, 4(2), 143-152. DOI: https://doi.org/10.18034/abcjar.v4i2.549

Sustainable Technology Research Group. (2012). Renewable energy integration in cloud data centers: Challenges and opportunities. Journal of Sustainable Technology, 7(4), 210-225.

Vadlamudi, S. (2015). Enabling Trustworthiness in Artificial Intelligence - A Detailed Discussion. Engineering International, 3(2), 105-114. DOI: https://doi.org/10.18034/ei.v3i2.519

Downloads

Published

2016-12-17

Issue

Vol. 3 No. 2 (2016): July - December Issue

Section

Articles

License

Copyright (c) 2016 Ravikiran Mahadasa, Pavani Surarapu

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

The CC BY-NC 4.0 license lets others remix, tweak, and build upon your work non-commercially, and although their new works must also acknowledge & be non-commercial, they don’t have to license their derivative works on the same terms.
|| View License Deed ||
|| View Legal Code ||
 
Publishing paper with Asia Pacific Journal of Energy and Environment means that the author or authors retain the copyright in the paper.
This journal is granted an exclusive noncommercial reuse license by the author(s), but the author(s) are able to put the paper onto a website, distribute it to colleagues, give it to students, use it in your thesis, etc, so long as the use is not directed at commercial advantage or toward private monetary gain.
The author(s) can reuse the figures and tables and other information contained in their paper published by Asia Pacific Journal of Energy and Environment in future papers or work without having to ask anyone for permission, provided that the figures, tables or other information that is included in the new paper or work properly references the published paper as the source of the figures, tables or other information, and the new paper or work is not direct at private monetary gain or commercial advantage.

How to Cite

Mahadasa, R., & Surarapu, P. (2016). Toward Green Clouds: Sustainable Practices and Energy-Efficient Solutions in Cloud Computing. Asia Pacific Journal of Energy and Environment, 3(2), 83-88. https://doi.org/10.18034/apjee.v3i2.713