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<p>The capacity to perform more tasks with less energy is a crucial component of energy efficiency in the cloud environment. This study presents an energy-efficient approach (ppr_mmtod_1.0) for VM consolidation in a cloud environment, intending to lower energy consumption while completing more activities at the highest throughput. Our proposal uses the PPR to determine the upper threshold for overload detection. Furthermore, ppr_mmtod_1.0 takes into account the total data centre workload utilization when setting a lower threshold, which might decrease VM migrations. The simulation outcomes of our suggested technique, ppr_mmtod_1.0, are compared with those of the two reference approaches, iqr_mc_1.5 and lr_mmt_1.2. Compared to two existing approaches, iqr_mc_1.5 and lr_mmt_1.2, our strategy has been found to decrease average energy usage by 15.88% and 11.91%, respectively. Additionally, our method lessened the number of live migrations, resulting in less performance degradation of VMs.</p>

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A Virtual Machine Consolidation Approach for Efficient Energy Consumption in Cloud Computing Environment

  • 期刊论文

  • 发表日期：

    2025年8月28日

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  • Rahat Yezdani,
  • S. M. K. Quadri

摘要

The capacity to perform more tasks with less energy is a crucial component of energy efficiency in the cloud environment. This study presents an energy-efficient approach (ppr_mmtod_1.0) for VM consolidation in a cloud environment, intending to lower energy consumption while completing more activities at the highest throughput. Our proposal uses the PPR to determine the upper threshold for overload detection. Furthermore, ppr_mmtod_1.0 takes into account the total data centre workload utilization when setting a lower threshold, which might decrease VM migrations. The simulation outcomes of our suggested technique, ppr_mmtod_1.0, are compared with those of the two reference approaches, iqr_mc_1.5 and lr_mmt_1.2. Compared to two existing approaches, iqr_mc_1.5 and lr_mmt_1.2, our strategy has been found to decrease average energy usage by 15.88% and 11.91%, respectively. Additionally, our method lessened the number of live migrations, resulting in less performance degradation of VMs.