DocumentCode
2862970
Title
BoostPred: An Automatic Demand Predictor for the Cloud
Author
Wong, Waiho ; Davis, Joseph
Author_Institution
Sch. of Inf. Technol., Univ. of Sydney, Sydney, NSW, Australia
fYear
2011
fDate
12-14 Dec. 2011
Firstpage
411
Lastpage
418
Abstract
As Cloud Computing adoption by enterprise customers grows, so too the need for optimal utilisation of their virtual resources. Likewise, cost pressures on cloud providers with a utility business model e.g. Amazon Web Services, would also need to optimise the utilisation of their physical infrastructure. Clearly, the ability to predict demand would be valuable. We introduce Boost red, an automatic demand predictor for the cloud. BoostPred´s design goals are to require no human expert intervention in making accurate predictions from noisy real world demand signals. We evaluate the accuracy of Boost red using noisy real-world signals which reveal its potential and current shortcomings.
Keywords
Web services; cloud computing; security of data; Amazon Web Services; BoostPred; automatic demand predictor; cloud computing; enterprise customers; optimal utilisation; physical infrastructure; virtual resources; Accuracy; Boosting; Noise; Prediction algorithms; Quality of service; Resource management; Training; boosting; cloud computing; demand forecasting; demand prediction; neural networks; resource allocation;
fLanguage
English
Publisher
ieee
Conference_Titel
Dependable, Autonomic and Secure Computing (DASC), 2011 IEEE Ninth International Conference on
Conference_Location
Sydney, NSW
Print_ISBN
978-1-4673-0006-3
Type
conf
DOI
10.1109/DASC.2011.84
Filename
6118754
Link To Document