Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/26461
Title: Empirical prediction models for adaptive resource provisioning in the cloud
Authors: Islam, S
Keung, J
Lee, K
Liu, A
Keywords: Cloud computing
Machine learning
Resource prediction
Resource provisioning
Issue Date: 2012
Publisher: North-Holland
Source: Future generation computer systems, 2012, v. 28, no. 1, p. 155-162 How to cite?
Journal: Future generation computer systems 
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.
URI: http://hdl.handle.net/10397/26461
ISSN: 0167-739X
EISSN: 1872-7115
DOI: 10.1016/j.future.2011.05.027
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