Performance Measurement and Configuration Optimization of Virtual Machines Based on the Bayesian Network

Hao, Jia; Zhang, Binbin; Yue, Kun; Wang, Juan; Wu, Hao

doi:10.1007/978-3-319-68542-7_55

Cited by 3 publications

(1 citation statement)

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“…However, studies like Ongaro et al, 2 Somani and Chaudhary, 3 and Padala et al 4 have reported that the current implementation of virtual machine monitors could not provide effective performance isolation mechanisms among VMs, and thus, the performance interference cannot be eliminated completely. It has been pointed out in previous studies [5][6][7][8][9] that the VM performance is influenced quite a lot by co-located VMs running simultaneously. Performance interference due to physical resource competition may lead to VM performance degradation, whose extent is closely dependent of the characteristics of VM runtime environments, such as the type of the applications running on other VMs and the number of VMs running at the same time.…”

Section: Introductionmentioning

confidence: 99%

Measuring performance degradation of virtual machines based on the Bayesian network with hidden variables

Hao

Zhang

Yue

et al. 2018

Int J Communication

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In the virtualized environment, multiple virtual machines (VMs) sharing the same physical host are vulnerable to resource competition, which may cause performance interference among VMs and thus lead to VM performance degradation. This paper focuses on measuring CPU, memory, I/O, and the overall VM performance degradation caused by the performance interference according to the properties in the runtime environment of VMs. To this end, we adopt Bayesian network (BN), as the framework for uncertainty representation and inference, and construct a VM property-performance BN (VPBN) with hidden variables, which represent the unobserved performance degradation of CPU, memory, and I/O, respectively. Then, we present the method to measure performance degradation of VMs by probabilistic inferences with the VPBN. Experimental results show the accuracy and efficiency of our method.

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