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
- V. Umadevi, S. Ranganathan, IoT based energy aware local approximated MapReduce fuzzy clustering for smart healthcare data transmission , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Sabeerath K, Manikandasaran S. Sundaram, BTEDD: Block-level tokens for efficient data deduplication in public cloud infrastructures , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- R. Rita Jenifer, V. Sinthu Janita, Energy-aware Security Optimized Elliptic Curve Digital Signature Algorithm for Universal IoT Networks , The Scientific Temper: Vol. 16 No. 09 (2025): The Scientific Temper
- B. S. E. Zoraida, J. Jasmine Christina Magdalene, Smart grid precision: Evaluating machine learning models for forecasting of energy consumption from a smart grid , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Annalakshmi D, C. Jayanthi, A secured routing algorithm for cluster-based networks, integrating trust-aware authentication mechanisms for energy-efficient and efficient data delivery , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- A. Jabeen, A. R. M. Shanavas, Hazard regressive multipoint elitist spiral search optimization for resource efficient task scheduling in cloud computing , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- V. Selvi, T. S. Poornappriya, R. Balasubramani, Cloud computing research productivity and collaboration: A scientometric perspective , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Chinnadurai U, A. Vinayagam, Energy efficient routing with cluster approach in wireless networks – A literature review , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- A. Jafar Ali, G. Ravi, D.I. George Amalarethinam, AI-Integrated Swarm-Powered Self-Scheduling Routing for Heterogeneous Wireless Sensor Networks to Maximize Network Lifetime , The Scientific Temper: Vol. 16 No. 12 (2025): The Scientific Temper
- V. Baby Deepa, R. Jeya, Dynamic resource allocation with otpimization techniques for qos in cloud computing , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.

