Paper Info
Title
Intelligent cloud capacity management
Abstract
Cloud computing as a service promises many business benefits. The cost to pay is that it also faces many technique challenges. One of the challenges is to effectively manage cloud capacity in response to the increased demand changes in clouds, as computing customers now can provision and de-provision virtual machines more frequently. This paper studies cloud capacity prediction as a response to the challenge. We propose an integrated solution for intelligent cloud capacity estimation. In this solution, a novel measure is introduced to quantify and guide the prediction process. Then an ensemble method is utilized to predict the future provisioning/de-provisioning demands respectively. The cloud capacity is estimated using the active virtual machines and the future provisioning/de-provisioning demands altogether. Our proposed solution is simple and with low computational cost. The experiments on the IBM Smart Cloud Enterprise trace data shows our solution is effective.
Year
DOI
Venue
2012
10.1109/NOMS.2012.6211941
NOMS
Keywords
Field
DocType
ensemble method,cloud capacity prediction,capacity management,cloud service,service quality maintenance,intelligent cloud capacity estimation,virtual machines,provisioning-deprovisioning demands,intelligent cloud capacity management,cloud computing,ibm smart cloud enterprise trace data,mathematical model,virtual machine,time series analysis,estimation,service quality,servers,prediction algorithms
IBM,Virtual machine,Computer science,Server,Computer network,Capacity management,Provisioning,Prediction algorithms,Business benefits,Distributed computing,Cloud computing
Conference
ISSN
ISBN
Citations 
1542-1201 E-ISBN : 978-1-4673-0268-5
978-1-4673-0268-5
4
PageRank 
References 
Authors
0.43
0
4
Name
Order
Citations
PageRank
Yexi Jiang125314.60
Chang-Shing Perng247835.92
Tao Li37216393.45
Rong N. Chang434629.75