Empirical prediction models for adaptive resource provisioning in the cloud

Authors:
Sadeka Islam;Jacky Keung;Kevin Lee;Anna Liu
Affiliations:
National ICT Australia, Sydney, Australia and School of Computer Science and Engineering, University of New South Wales, Sydney, Australia;National ICT Australia, Sydney, Australia and School of Computer Science and Engineering, University of New South Wales, Sydney, Australia and Department of Computing, The Hong Kong Polytechnic Un ...;National ICT Australia, Sydney, Australia and School of Computer Science and Engineering, University of New South Wales, Sydney, Australia;National ICT Australia, Sydney, Australia and School of Computer Science and Engineering, University of New South Wales, Sydney, Australia
Venue:
Future Generation Computer Systems
Year:
2012

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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.