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Residual Capacity-Aware Virtual Machine Assignment for Reducing Network Loads in Multi-tenant Data Center Networks

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Abstract

This paper proposes a residual capacity-aware virtual machine (VM) assignment scheme for multi-tenant data center networks. In multi-tenant data centers, tenants submit their resource requirements and the data centers provide VMs that are assigned to physical servers according to the requirements. These VMs communicate with each other to execute distributed processing. The performance of such distributed processing depends on the amount of traffic communicated by the VMs because the increase in traffic volume causes network congestion, which leads to packet losses and high transmission delay. Therefore, we need an appropriate VM assignment strategy that avoids the generation of network congestion in order to satisfy the requirements of the tenants. The proposed scheme performs VM assignment that reduces the network loads caused by traffic injected into data center networks, taking into account the traffic volume among VMs and the residual capacities of physical servers. Through simulation experiments, we demonstrate that the proposed scheme reduces the network loads efficiently.

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Acknowledgements

This research was partially supported by Grant-in-Aid for Scientific Research (C) of the Japan Society for the Promotion of Science under Grant No. 18K11282.

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Authors and Affiliations

  1. Faculty of Science and Engineering, Doshisha University, Kyoto, Japan

    Tomotaka Kimura

  2. Faculty of Engineering, Tokyo University of Science, Tokyo, Japan

    Takaya Suzuki & Masahiro Muraguchi

  3. Faculty of Engineering Science, Kansai University, Osaka, Japan

    Kouji Hirata

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  1. Tomotaka Kimura
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  2. Takaya Suzuki
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  3. Kouji Hirata
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  4. Masahiro Muraguchi
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Correspondence to Tomotaka Kimura.

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Kimura, T., Suzuki, T., Hirata, K. et al. Residual Capacity-Aware Virtual Machine Assignment for Reducing Network Loads in Multi-tenant Data Center Networks. J Netw Syst Manage 27, 949–971 (2019). https://doi.org/10.1007/s10922-019-09492-1

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