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An efficient energy-aware and service quality improvement strategy applied in cloud computing

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Abstract

Reducing the energy consumption while guaranteeing the quality of service (QoS) in the cloud data centers is challenge task for cloud providers. Dynamic virtual machine (VM) consolidation technology is regarded as a promising approach to satisfy goals. Considering dynamic workload of physical machine (PM) results in VM migration and high resources utilization of PM results in resources contention among VMs that affects working performance of VMs. Hence, it is vital to provide an efficient approach for dynamic VM placement during the consolidation to achieve the objectives while alleviating resources contention among VMs in the data centers. In this paper, the proposed strategy called LBVMP aims to build a novel conception consisting of a balancing flat surface of a PM in terms of CPU, RAM, bandwidth (BW) and another proportion flat surface that the remaining resources capacity of the targeted PM was divided by the request resources (CPU, RAM and BW) of a VM. Then LBVMP calculates the distance between two plats to evaluate VM allocation solutions. Extensive experimental results based on the CloudSim simulator demonstrate that compared with the state-of-the-art algorithm BCAVMP, the proposed strategy enables to reduce the cloud data centers of energy consumption, the number of migrations, SLAV, ESV by an average of 3.50%, 9.40%, 78.40%, 79.91%, respectively.

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Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

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Funding

This work was supported in part by Science and Technology R&D Project of Henan Province (Grant No. 212102210078) and the Key Science and Technology Project of Henan Province (Grant No. 201300210400).

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  1. Junyang Yu has contributed equally to this work.

Authors and Affiliations

  1. School of Software, Henan University, Kaifeng, 475000, Henan, China

    Jinjiang Wang, Junyang Yu, Yixin Song, Xin He & Yalin Song

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  1. Jinjiang Wang
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  2. Junyang Yu
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  3. Yixin Song
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  4. Xin He
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by JY, YS and JW. The first draft of the manuscript was written by JW and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Junyang Yu.

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Wang, J., Yu, J., Song, Y. et al. An efficient energy-aware and service quality improvement strategy applied in cloud computing. Cluster Comput 26, 4031–4049 (2023). https://doi.org/10.1007/s10586-022-03795-w

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