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
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;
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
DOI :
10.1109/DASC.2011.84