Adopting Cloud-Based Techniques to Reduce Energy Consumption: Toward a Greener Cloud
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Adopting Cloud-Based Techniques to Reduce Energy Consumption: Toward a Greener Cloud

Authors: Sandesh Achar

Abstract:

The cloud computing industry has set new goals for better service delivery and deployment, so anyone can access services such as computation, application, and storage anytime. Cloud computing promises new possibilities for approaching sustainable solutions to deploy and advance their services in this distributed environment. This work explores energy-efficient approaches and how cloud-based architecture can reduce energy consumption levels amongst enterprises leveraging cloud computing services. Adopting cloud-based networking, database, and server machines provide a comprehensive means of achieving the potential gains in energy efficiency that cloud computing offers. In energy-efficient cloud computing, virtualization is one aspect that can integrate several technologies to achieve consolidation and better resource utilization. Moreover, the Green Cloud Architecture for cloud data centers is discussed in terms of cost, performance, and energy consumption, and appropriate solutions for various application areas are provided.

Keywords: Greener Cloud, cloud computing, energy efficiency, energy consumption, metadata tags, Green Cloud Advisor.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.7570368

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[1] Abdulsalam, S., Lakomski, D., Gu, Q., Jin, T., & Zong, Z. (2014). Program energy efficiency: The impact of language, compiler, and Implementation choices. In Proceedings of the International Green Computing Conference, (pp. 1-6). Dallas.
[2] Abd-El-Atty, B., Iliyasu, A., Alaskar, H., & El-Latif. (2020). A Robust Quasi-Quantum Walks-based Steganography Protocol for Secure Transmission of Images on Cloud-based E-healthcare Platforms. Sensors.
[3] Achar, S (2022). How Adopting a Cloud-Based Architecture has Reduced the Energy Consumption Levels. International Journal of Information Technology and Management Information Systems (IJITMIS), 13(1), 15-23.
[4] Khalifeh, A. G. (2012). Cloud Computing and Sustainability: Energy Efficiency Aspects. School of Information Science, Computer and Electrical Engineering Halmstad University.
[5] Usman, M. J., Ismail, A. S., Abdul-Salaam, G., Chizari, H., Kaiwartya, O., Gital, A. Y., ... & Dishing, S. I. (2019). Energy-efficient nature-inspired techniques in cloud computing datacenters. Telecommunication Systems, 71(2), 275-302.
[6] Xu, M., Toosi, A., & Buyya, R. (2020). A self-adaptive approach for managing applications and harnessing renewable energy for sustainable cloud computing. IEEE Trans. Sustain. Comput., 544-558.
[7] Limbachiya, N. (2019, 06 12). Green Cloud Computing: Techniques every QA should know. Retrieved from Entrepreneur Asia Pacific: https://www.entrepreneur.com/article/335172
[8] Ali, S. A., Affan, M., & Alam, M. (2019, January). A study of efficient energy management techniques for cloud computing environment. In 2019 9th international conference on cloud computing, data science & engineering (confluence) (pp. 13-18). IEEE
[9] Zhou, Q., Xu, M., Gill, S. S., Gao, C., Tian, W., Xu, C., & Buyya, R. (2020, May). Energy efficient algorithms based on VM consolidation for cloud computing: comparisons and evaluations. In 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID) (pp. 489-498). IEEE.
[10] Mishra, S. K., Sahoo, S., Sahoo, B., & Jena, S. K. (2020). Energy-efficient service allocation techniques in cloud: A survey. IETE Technical Review, 37(4), 339-352.
[11] Chaurasia, N., Kumar, M., Chaudhry, R., & Verma, O. P. (2021). Comprehensive survey on energy-aware server consolidation techniques in cloud computing. The Journal of Supercomputing, 77(10), 11682-11737.
[12] Mekala, M. S., & Viswanathan, P. (2019). Energy-efficient virtual machine selection based on resource ranking and utilization factor approach in cloud computing for IoT. Computers & Electrical Engineering, 73, 227-244.
[13] Kaur, A., Singh, V. P., & Gill, S. S. (2018, August). The future of cloud computing: opportunities, challenges and research trends. In 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (pp. 213-219). IEEE.
[14] Abid, A., Manzoor, M. F., Farooq, M. S., Farooq, U., & Hussain, M. (2020). Challenges and issues of resource allocation techniques in cloud computing. KSII Transactions on Internet and Information Systems (TIIS), 14(7), 2815-2839.
[15] Vakilinia, S. (2018). Energy efficient temporal load aware resource allocation in cloud computing datacenters. Journal of Cloud Computing, 7(1), 1-24.
[16] Mishra, S. K., Khan, M. A., Sahoo, S., & Sahoo, B. (2019). Allocation of energy-efficient task in cloud using DVFS. International Journal of Computational Science and Engineering, 18(2), 154-163.
[17] Stavrinides, G. L., & Karatza, H. D. (2019). An energy-efficient, QoS-aware and cost-effective scheduling approach for real-time workflow applications in cloud computing systems utilizing DVFS and approximate computations. Future Generation Computer Systems, 96, 216-226.
[18] Kendrick, P., Baker, T., Maamar, Z., Hussain, A., Buyya, R., & Al-Jumeily, D. (2018). An efficient multi-cloud service composition using a distributed multiagent-based, memory-driven approach. IEEE Transactions on Sustainable Computing, 6(3), 358-369.
[19] Gourisaria, M. K., Patra, S. S., & Khilar, P. M. (2018). Energy saving task consolidation technique in cloud centers with resource utilization threshold. In Progress in Advanced Computing and Intelligent Engineering (pp. 655-666). Springer, Singapore.