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I/O access frequency-aware cache method on KVM/QEMU

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Abstract

Together with the rapid development of IT technology, cloud computing has been considered as the next generation’s computing infrastructure. One of the essential part of cloud computing is the virtual machine technology that enables to reduce the data center cost with better resource utilization. Especially, virtual desktop infrastructure (VDI) is receiving explosive attentions from IT markets because of its advantages of easier software management, greater data protection, and lower cost. However, sharing physical resources in VDI to consolidate multiple guest virtual machines (VMs) on a host has a tradeoff that can lead to significant I/O degradation. Optimizing I/O virtualization overhead is a challenging task because it needs to scrutinize multiple software layers between guest VMs and host where those VMs are executing. In this paper, we present a hypervisor-level cache, called hyperCache, which is possible to provide a shortcut in KVM/QEMU. It intercepts I/O requests in the hypervisor and analyses their I/O access patterns to select data retaining high access frequency. Also, it has a capability of maintaining the appropriate cache memory size by utilizing the cache block map. Our experimental results demonstrate that our method improves I/O bandwidth by up to 4.7x over the existing QEMU.

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIP) (NRF-2014R1A2A2A01002614). Also, this work is supported by the MSIP, Korea, under the Global IT Talent support program (IITP-2016-H0905-15-1005) supervised by the IITP.

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  1. College of Electronics and Information Engineering, Sejong university, 98 Gunja-dong, Gwangjin-gu, Seoul, Korea

    Taehoon Kim, Jaechun No, Zhegao Piao & Seong Joon Yoo

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  1. Taehoon Kim
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  2. Jaechun No
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  3. Zhegao Piao
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  4. Seong Joon Yoo
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Correspondence to Jaechun No.

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Kim, T., No, J., Piao, Z. et al. I/O access frequency-aware cache method on KVM/QEMU. Cluster Comput 20, 2143–2155 (2017). https://doi.org/10.1007/s10586-017-0937-x

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