Performance and Energy-based Cost Prediction of Virtual Machines Live Migration in Clouds

被引：6

作者：

Aldossary, Moahammad ^{[1
,2
]}

Djemame, Karim ^{[2
]}

机构：

[1] Prince Sattam Bin Abdulaziz Univ, Al Kharj, Saudi Arabia

[2] Univ Leeds, Sch Comp, Leeds, W Yorkshire, England

来源：

CLOSER: PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND SERVICES SCIENCE | 2018年

关键词：

Cloud Computing; Cost Prediction; Workload Prediction; Live Migration; Power Consumption;

D O I：

10.5220/0006682803840391

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Virtual Machines (VMs) live migration is one of the important approaches to improve resource utilisation and support energy efficiency in Clouds. However, VMs live migration leads to performance loss and additional costs due to increased migration time and energy overhead. This paper introduces a Performance and Energy-based Cost Prediction Framework to estimate the total cost of VMs live migration by considering the resource usage and power consumption, while maintaining the expected level of performance. A series of experiments conducted on a Cloud testbed show that this framework is capable of predicting the workload, power consumption and total cost for heterogeneous VMs before and after live migration, with the possibility of recovering the migration cost e.g. 28.48% for the predicted cost recovery of the VM.

引用

页码：384 / 391

页数：8

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