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Energy efficient VM scheduling for big data processing in cloud computing environments

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Abstract

Recently, the cloud computing platform has come to be widely used to analyze large amounts of data collected in real-time from SNS or IoT sensors. In order to analyze big data, a large number of VMs are created in the cloud server, and that many PMs are needed to handle it. When VMs are allocated to PMs in cloud computing, each VM is allocated by a VM scheduling algorithm. However, existing scheduling algorithms waste substantial PM resources due to the low density of VM. This waste of resources dramatically reduces the energy efficiency of the entire cloud server. Therefore, minimizing idle PMs by increasing the density of VMs allocated to PMs is critical for VM scheduling. In this paper, a VM relocation method is suggested to improve the energy efficiency by increasing the density of VMs using the Knapsack algorithm. In addition, it is possible through the proposed method to achieve efficient VM relocation in a short period by improving the Knapsack algorithm. Therefore, we proposed the effective resource management method of cloud cluster for big data analysis.

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Acknowledgements

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF-2017R1A2B4010570) and by the Soonchunhyang University Research Fund.

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Authors and Affiliations

  1. Department of Computer Science, Soonchunhyang University, Asan-Si, Chungnam-do, Republic of Korea

    SangWook Han

  2. Department of Medical Information Technology Engineering, Soonchunhyang University, Asan-Si, Chungnam-do, Republic of Korea

    SeDong Min

  3. Department of Computer Software and Engineering, Soonchunhyang University, Asan-Si, Chungnam-do, Republic of Korea

    HwaMin Lee

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  1. SangWook Han
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  2. SeDong Min
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Correspondence to HwaMin Lee.

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Han, S., Min, S. & Lee, H. Energy efficient VM scheduling for big data processing in cloud computing environments. J Ambient Intell Human Comput (2019). https://doi.org/10.1007/s12652-019-01361-8

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