A combined forecast-based virtual machine migration in cloud data centers

被引：35

作者：

Paulraj, Getzi Jeba Leelipushpam ^{[1
]}

Francis, Sharmila Anand John ^{[2
]}

Peter, J. Dinesh ^{[1
]}

Jebadurai, Immanuel Johnraja ^{[1
]}

机构：

[1] Karunya Univ, Dept Comp Sci Technol, Coimbatore, Tamil Nadu, India

[2] King Khalid Univ, Dept Comp Sci, Abha, Saudi Arabia

来源：

COMPUTERS & ELECTRICAL ENGINEERING | 2018年 / 69卷

关键词：

Cloud computing; Combined forecasting; Artificial neural networks; Live virtual machine migration; Virtual machine placement; DYNAMIC VMS PLACEMENT; LIVE MIGRATION; PERFORMANCE;

D O I：

10.1016/j.compeleceng.2018.01.012

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Live virtual machine (VM) migration improves the performance of cloud data center in terms of energy efficiency, fault tolerance, and availability. The workload handled by cloud data center is dynamic in nature. This increases the resource requirement of either the migrated virtual machine or collocated virtual machine at any time leading to further migration. Inappropriately handled live VM migration imposes severe application performance degradation. In this paper, a combined forecasting technique to predict the resource requirement of any virtual machine is proposed. Based on the current and predicted resource utilization, live migration is performed by Combined Forecast Load-Aware technique. Experiments were carried out to evaluate the performance of the proposed technique on live VM migration. The outcomes indicate that the proposed approach has minimized the number of migrations, energy usage, and the message overhead when compared with the existing state-of-art technique. (C) 2018 Elsevier Ltd. All rights reserved.

引用

页码：287 / 300

页数：14

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