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A novel disk I/O scheduling framework of virtualized storage system

  • Dingding Li
  • Mianxiong Dong
  • Yong Tang
  • Kaoru Ota
Article

Abstract

Modern data centers usually use virtual machine technology to host various big data applications in a single physical machine, not only enhancing the server utilization, but also providing them with the hardware-level isolation. However, in a typical virtualized environment an extra software layer called virtual machine monitor (VMM) is often interposed between the hardware resource and guest operating system (virtual machine, VM), shielding the specific user-process semantic inside a running VM. As a result, it obstructs the disk I/O scheduler of VMM to acquire the accurate information of a user-process (often a big data application), and thus proposes a challenge on the I/O request scheduling as well as the disk resource management at the granularity of VM user-process. Eventually, the disk I/O performance of a virtualized system is sub-optimal. This paper introduces an improved disk I/O scheduling framework for the virtualized system. It aims at bridging the semantic gap between physical disk I/O scheduler and VM user-process, providing a fair sharing of disk I/O resource among concurrent VMs. At the same time, it improves the overall disk I/O performance through a novel method for creating the image file of VM. Besides, an extra scheduling algorithm is proposed to further refine the storage performance. Finally, we implement these improvements on Xen hypervisor and conduct extensive experiments to verify our framework. The experimental result shows that our work improve the performance of read-intensive, write-intensive and mixed workloads up to 9, 10.7 and 20% respectively.

Keywords

Big data Virtual machine Disk I/O scheduling 

Notes

Acknowledgements

This work was funded by the National Natural Science Foundation of China under Grant Numbers 61502180 and 61772211, by the Natural Science Foundation of Guangdong Province, China under Grant Numbers 2014A030310238, 2015B010129009, 2016A030313441 and 2017A030303074, by the Pearl River S&T Nova Program of Guangzhou under Grant Numbers 201710010189 and 201604016007, by JSPS KAKENHI Grant Numbers JP16K00117, JP15K15976, and KDDI Foundation.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer ScienceSouth China Normal UniversityGuangzhouChina
  2. 2.Department of Information and Electronic EngineeringMuroran Institute of TechnologyMuroranJapan

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