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Measuring the Characteristics of Hypervisor I/O Scheduling in the Cloud for Virtual Machine Performance Interference

Measuring the Characteristics of Hypervisor I/O Scheduling in the Cloud for Virtual Machine Performance Interference

Ziye Yang, Haifeng Fang, Yingjun Wu, Chunqi Li
Copyright: © 2013 |Volume: 5 |Issue: 4 |Pages: 25
ISSN: 1938-0259|EISSN: 1938-0267|EISBN13: 9781466635715|DOI: 10.4018/ijghpc.2013100102
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MLA

Yang, Ziye, et al. "Measuring the Characteristics of Hypervisor I/O Scheduling in the Cloud for Virtual Machine Performance Interference." IJGHPC vol.5, no.4 2013: pp.5-29. http://doi.org/10.4018/ijghpc.2013100102

APA

Yang, Z., Fang, H., Wu, Y., & Li, C. (2013). Measuring the Characteristics of Hypervisor I/O Scheduling in the Cloud for Virtual Machine Performance Interference. International Journal of Grid and High Performance Computing (IJGHPC), 5(4), 5-29. http://doi.org/10.4018/ijghpc.2013100102

Chicago

Yang, Ziye, et al. "Measuring the Characteristics of Hypervisor I/O Scheduling in the Cloud for Virtual Machine Performance Interference," International Journal of Grid and High Performance Computing (IJGHPC) 5, no.4: 5-29. http://doi.org/10.4018/ijghpc.2013100102

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Abstract

In virtualized environments, the customers who purchase virtual machines (VMs) from a third-party cloud would expect that their VMs run in an isolated manner. However, the performance of a VM can be negatively affected by co-resident VMs. In this paper, the authors propose vExplorer, a distributed VM I/O performance measurement and analysis framework, where one can use a set of representative I/O operations to identify the I/O scheduling characteristics within a hypervisor, and potentially leverage this knowledge to carry out I/O based performance attacks to slow down the execution of the target VMs. The authors evaluate their prototype on both Xen and VMware platforms with four server benchmarks and show that vExplorer is practical and effective. The authors also conduct similar tests on Amazon’s EC2 platform and successfully slow down the performance of target VMs.

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