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Virtualization I/O optimization based on shared memory

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

With the development and popularization of cloud computing, more and more services and applications are migrated to cloud for the sake of low cost, high availability and excellent performance. As the foundation of cloud computing, virtualization technology integrates and reallocates the computing capability, storage and network resource fairly among virtual machines and provides a full-featured, isolated and reliable hardware environment for various operating systems. Owe to the virtualization technology, computing capability of virtual machines has achieved fantastic performance, some even achieve near native speed. However, low I/O performance is still a bottleneck, especially in I/O intensive applications. The leading causes include redundant data copy and frequent VM exits. Focusing on network I/O optimization, we design and implement virtsocket, a new network socket library in virtualization scenario which utilizes shared memory for data transmission. A ring buffer data structure stores I/O requests of virtual machine which is triggered to issue all requests with only one hypercall according to scheduler. Data referred in the I/O requests is read directly from virtual machine memory by host machine kernel module with interfaces provided by modified hypervisor. Experimental results show that throughput is improved by hundreds of times when compared with original virtualization scenario, and the latency also achieves a remarkable reduction. Both throughput and latency performance exceed existing para-virtualization solutions.

源语言英语
主期刊名Proceedings - 2013 IEEE International Conference on Big Data, Big Data 2013
出版商IEEE Computer Society
70-77
页数8
ISBN(印刷版)9781479912926
DOI
出版状态已出版 - 2013
已对外发布
活动2013 IEEE International Conference on Big Data, Big Data 2013 - Santa Clara, CA, 美国
期限: 6 10月 20139 10月 2013

出版系列

姓名Proceedings - 2013 IEEE International Conference on Big Data, Big Data 2013

会议

会议2013 IEEE International Conference on Big Data, Big Data 2013
国家/地区美国
Santa Clara, CA
时期6/10/139/10/13

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