Fully Automatic Bayesian Neural Forecaster - NN GC1

Ferreira, Vitor Hugo; Silva, Alexandre P. Alves da

doi:10.1109/ijcnn.2010.5596861

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2011

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Cited by 2 publications

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“…Clearly, a more efficient method is needed. In future work, we intend to explore the methods proposed in [23] [24].…”

Section: Resultsmentioning

confidence: 99%

BoostPred: An Automatic Demand Predictor for the Cloud

Wong

Davis

2011

2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing

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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 BoostPred, an automatic demand predictor for the cloud. BoostPred's design goals are to require no human expert intervention in making accurate predictions from noisy realworld demand signals. We evaluate the accuracy of BoostPred using noisy real-world signals which reveal its potential and current shortcomings.

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“…Clearly, a more efficient method is needed. In future work, we intend to explore the methods proposed in [23] [24].…”

Section: Resultsmentioning

confidence: 99%