A PPR-based energy-efficient VM consolidation in cloud computing
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2024.15.3.17Keywords:
Cloud environment, Energy consumption, Energy-efficient approach, VM consolidation, VM migrationDimensions Badge
Issue
Section
License
Copyright (c) 2024 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The tendency to do more jobs while consuming less energy is crucial to energy efficiency in the cloud environment. To use less energy while performing more tasks at the best throughput, this study provides an energy-efficient technique (PPR_DWMMT_1.1) for VM consolidation in a cloud domain. Our approach uses the PPR to determine the upper threshold for overload detection and the lower threshold for underload detection. Additionally, PPR_DWMMT_1.1 considers the overall workload utilisation of the data centre when selecting a lower threshold, which could reduce VM migrations. Our proposed method, PPR DWMMT 1.1, is compared to the simulation results of the four reference techniques, IQR_MMT_1.5, LR_MC_1.2, MAD_MU_2.5, and THR_RS_0.8. Our solution has been demonstrated to use less energy, trigger fewer host shutdowns and live migrations, and achieve the best performance when compared to the other four approaches.Abstract
How to Cite
Downloads
Similar Articles
- Koyel Naskar, Urmi Satyan, Celebration and protest in art: a Comparative Study of Australia’s Corroboree and West Bengal’s Gambhira as Forms of Socio-Cultural Expression , The Scientific Temper: Vol. 15 No. spl-2 (2024): The Scientific Temper
- Manan Pathak, Dishang Trivedi Trivedi, Field-effect limits and design parameters for hybrid HVDC – HVAC transmission line corridors , The Scientific Temper: Vol. 16 No. Spl-1 (2025): The Scientific Temper
- Pankaj Gupta, Niyati Chaudhary, Model Building with Antecedents and Consequences of Workplace Bullying: A SPAR-4-SLR approach using ADO-TCCM Framework with Bibliometric Analysis , The Scientific Temper: Vol. 17 No. 01 (2026): The Scientific Temper
- M. Rajalakshmi, V. Sulochana, Enhancing deep learning model performance in air quality classification through probabilistic hyperparameter tuning with tree-structured Parzen estimators , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- S. Ramkumar, K. Aanandha Saravanan, Martin Joel Rathnam, M. Revathy, Integration of AI and agent-based modeling for simulating human-ecological systems , The Scientific Temper: Vol. 16 No. 03 (2025): The Scientific Temper
- Priyanka Dutta, Rianka Sarkar, A Sustainable Approach: Navigating through the Mishing Tribe’s Indigenous Knowledge and Disaster Management Strategies , The Scientific Temper: Vol. 16 No. 12 (2025): The Scientific Temper
- Vikas Yadav, Parul Nangia, Bisphenol-A Induced Changes in Blood Indices of Channa punctatus and Alleviation with Vitamin C , The Scientific Temper: Vol. 12 No. 1&2 (2021): The Scientific Temper
- Vijai K. Visvanathan, Karthikeyan Palaniswamy, Thanarajan Kumaresan, Green ammonia: catalysis, combustion and utilization strategies , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
- R. Porselvi, D. Kanchana, Beulah Jackson, L. Vigneash, Dynamic resource management for 6G vehicular networks: CORA-6G offloading and allocation strategies , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Suprabha Amit Kshatriya, Jaymin K Bhalani, Fire and smoke detection with high accuracy using YOLOv5 , The Scientific Temper: Vol. 16 No. 06 (2025): The Scientific Temper
<< < 15 16 17 18 19 20 21 22 23 24 > >>
You may also start an advanced similarity search for this article.

