Bayesian Networks-Based Selection Algorithm for Virtual Machine to Be Migrated

Yan, Chengyu; Li, Zhihua; Yu, Xinrong; Yu, Ning

doi:10.1109/bdcloud-socialcom-sustaincom.2016.89

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…Ramezani et al 28 and Shyam and Manvi 29 measured the VM performance based on massive VM properties from data center. Yan et al 30 proposed a BN-based selection algorithm to estimate the migration probability of VMs and calculate the potential total number of migrations. Li et al 31 developed a BN-based estimation model for lively migration and placement of VMs.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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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Dynamic Virtual Machine Consolidation Algorithms for Energy-Efficient Cloud Resource Management: A Review

Khan

Paplinski

Khan

et al. 2017

Sustainable Cloud and Energy Services

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Energy-Efficient and Load-Aware VM Placement in Cloud Data Centers

Lin

Cheng

et al. 2022

J Grid Computing

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VM consolidation has been proposed as an effective solution to improve resource utilization and energy efficiency through VM migration. Improper VM placement during consolidation may cause frequent VM migrations and constant on–off switching of PMs, which can significantly hurt QoS and increase energy consumption. Most existing algorithms on efficient VM placement suffer the problem of easily falling into a sub-optimum prematurely since they are heuristic. Also, they do not achieve a good balance between multiple different goals, such as resource utilization, QoS, and energy efficiency. To address this problem, we propose an effective and efficient VM placement approach called MOEA/D-based VM placement, with the goal of optimizing energy efficiency and resource utilization. We develop an improved MOEA/D algorithm to search for a Pareto-compromise solution for VM placement. Our experiment results demonstrate that the proposed multi-objective optimization (MOO) model and VM placement solution have immense potential as it offers significant cost savings and a significant improvement in energy efficiency and resource utilization under dynamic workload scenarios.

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Bayesian Networks-Based Selection Algorithm for Virtual Machine to Be Migrated

Cited by 4 publications

References 14 publications

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

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

Dynamic Virtual Machine Consolidation Algorithms for Energy-Efficient Cloud Resource Management: A Review

Energy-Efficient and Load-Aware VM Placement in Cloud Data Centers

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