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A multi-dimensional double descending maximum padding priority algorithm for cloud data centers

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Abstract

With the development of big data technologies, green cloud data centers have become a key factor in academia and industry. An energy-efficient cloud data center can save costs for cloud computing users. However, the problem of virtual machine mapping has always been a core problem. In the most existing research, the energy consumption generated by cloud data centers has become an important bottleneck restricting the technology of cloud computing. This paper establishes a consumption model of cloud data center energy and a virtual machine mapping rule. Based on this, a multi-dimensional double descending maximum padding priority (MD3MP2) virtual machine mapping algorithm is proposed. The algorithm can not only solve the one-dimensional virtual machine mapping problem of homogeneous data centers, but also successfully solve the multi-dimensional virtual machine mapping problem of homogeneous data centers. Finally, the algorithm is compared with four other algorithms. The experimental results show that the MD3MP2 algorithm is better than the compared algorithms.

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Acknowledgements

This work was supported by the National Key Research and Development Program [No. 2018YFB0203902] and the Science and Technology Program of Xi'an [No. 2020KJRC0101].

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, China

    Bin Liang, Xiaoshe Dong, Yufei Wang & Longxiang Wang

  2. School of Computer Science, Xi’an Shiyou University, Xi’an, China

    Bin Liang

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  1. Bin Liang
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  2. Xiaoshe Dong
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  3. Yufei Wang
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  4. Longxiang Wang
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Correspondence to Xiaoshe Dong.

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Liang, B., Dong, X., Wang, Y. et al. A multi-dimensional double descending maximum padding priority algorithm for cloud data centers. J Supercomput 77, 14011–14038 (2021). https://doi.org/10.1007/s11227-021-03842-0

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