Empirical prediction models for adaptive resource provisioning in the cloud

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)155-162
Journal / PublicationFuture Generation Computer Systems
Volume28
Issue number1
Publication statusPublished - Jan 2012
Externally publishedYes

Abstract

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. © 2010 Elsevier B.V. All rights reserved.

Research Area(s)

  • Cloud computing, Machine learning, Resource prediction, Resource provisioning