Empirical prediction models for adaptive resource provisioning in the cloud

被引：408

作者：

Islam, Sadeka ^{[1
,2
]}

Keung, Jacky ^{[1
,2
,3
]}

Lee, Kevin ^{[2
,4
]}

Liu, Anna ^{[2
]}

机构：

[1] Natl ICT Australia, Software Engn Res Grp, Sydney, NSW, Australia

[2] Univ New S Wales, Sch Comp Sci & Engn, Sydney, NSW, Australia

[3] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China

[4] Natl ICT Australia, Managing Complex Grp, Sydney, NSW, Australia

来源：

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE | 2012年 / 28卷 / 01期

关键词：

Cloud computing; Resource provisioning; Resource prediction; Machine learning;

D O I：

10.1016/j.future.2011.05.027

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Cloud computing allows dynamic resource scaling for enterprise online transaction systems, one of the key characteristics that differentiates the cloud from the traditional computing paradigm. However, initializing a new virtual instance in a cloud is not instantaneous; cloud hosting platforms introduce several minutes delay in the hardware resource allocation. In this paper, we develop prediction-based resource measurement and provisioning strategies using Neural Network and Linear Regression to satisfy upcoming resource demands. Experimental results demonstrate that the proposed technique offers more adaptive resource management for applications hosted in the cloud environment, an important mechanism to achieve on-demand resource allocation in the cloud. (C) 2011 Elsevier B.V. All rights reserved.

引用

页码：155 / 162

页数：8

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