Dynamic resource configuration based on multi-objective optimization in cloud computing

doi:10.11772/j.issn.1001-9081.2016.09.2396

Journal of Computer Applications ›› 2016, Vol. 36 ›› Issue (9): 2396-2401.DOI: 10.11772/j.issn.1001-9081.2016.09.2396

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Dynamic resource configuration based on multi-objective optimization in cloud computing

DENG Li^1,2, YAO Li¹, JIN Yu^1,2

1. College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan Hubei 430065, China;
2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan Hubei 430065, China

Received:2016-02-22 Revised:2016-03-23 Online:2016-09-10 Published:2016-09-08
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61303117), the Natural Science Foundation of Hubei Province (2014CFB817).

云计算中基于多目标优化的动态资源配置方法

邓莉^1,2, 姚力¹, 金瑜^1,2

1. 武汉科技大学计算机科学与技术学院, 武汉 430065;
2. 智能信息处理与实时工业系统湖北省重点实验室, 武汉 430065

通讯作者: 邓莉
作者简介:邓莉(1972-),女,湖北钟祥人,讲师,博士,CCF会员,主要研究方向:云计算、分布式计算;姚力(1990-),男,湖北罗田人,硕士研究生,主要研究方向:云计算;金瑜(1973-),女,湖北应城人,副教授,博士,主要研究方向:云计算、对等计算、信任模型。
基金资助:
国家自然科学基金青年项目（61303117）；湖北省自然科学基金面上项目（2014CFB817）。

Abstract

Abstract: Currently, most resource reallocation methods in cloud computing mainly aim to how to reduce active physical nodes for green computing, however, node stability of virtual machine placement solution is not considered. According to varying workload information of applications, a new virtual machine placement method based on multi-objective optimization was proposed for node stability, considering both the overhead of virtual machine reallocation and the stability of new virtual machine placement, and a new Multi-Objective optimization based Genetic Algorithm for Node Stability (MOGANS) was designed to solve this problem. The simulation results show that, the stability time of Virtual Machine (VM) placement obtained by MOGANS is 10.42 times as long as that of VM placement got by GA-NN (Genetic Algorithm for greeN computing and Numbers of migration). Meanwhile, MOGANS can well balance stability time and migration overhead.

Key words: cloud computing, multi-objective optimization, genetic algorithm, dynamic resource allocation, migration of virtual machine

摘要： 目前，云平台的大多数动态资源分配策略只考虑如何减少激活物理节点的数量来达到节能的目的，以实现绿色计算，但这些资源再配置方案很少考虑到虚拟机放置的稳定性。针对应用负载的动态变化特征，提出一种新的面向多虚拟机分布稳定性的基于多目标优化的动态资源配置方法，结合各应用负载的当前状态和未来的预测数据，综合考虑虚拟机重新放置的开销以及新虚拟机放置状态的稳定性，并设计了面向虚拟机分布稳定性的基于多目标优化的遗传算法（MOGANS）进行求解。仿真实验结果表明，相对于面向节能和多虚拟机重分布开销的遗传算法（GA-NN），MOGANS得到的虚拟机分布方式的稳定时间是GA-NN的10.42倍；同时，MOGANS也较好权衡了多虚拟机分布的稳定性和新旧状态转换所需的虚拟机迁移开销之间的关系。

关键词: 云计算, 多目标优化, 遗传算法, 动态资源分配, 虚拟机迁移

CLC Number:

TP319

DENG Li, YAO Li, JIN Yu. Dynamic resource configuration based on multi-objective optimization in cloud computing[J]. Journal of Computer Applications, 2016, 36(9): 2396-2401.

邓莉, 姚力, 金瑜. 云计算中基于多目标优化的动态资源配置方法[J]. 计算机应用, 2016, 36(9): 2396-2401.

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Dynamic resource configuration based on multi-objective optimization in cloud computing

云计算中基于多目标优化的动态资源配置方法

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