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Forecasting Service Performance on the Basis of Temporal Information by the Conditional Restricted Boltzmann Machine
Jiali YOU Hanxing XUE Yu ZHUO Xin ZHANG Jinlin WANG
IEICE TRANSACTIONS on Communications
Publication Date: 2018/05/01
Online ISSN: 1745-1345
Type of Manuscript: PAPER
video service, performance prediction, Conditional Restricted Boltzmann Machine, time-series data,
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Predicting the service performance of Internet applications is important in service selection, especially for video services. In order to design a predictor for forecasting video service performance in third-party application, two famous service providers in China, Iqiyi and Letv, are monitored and analyzed. The study highlights that the measured performance in the observation period is time-series data, and it has strong autocorrelation, which means it is predictable. In order to combine the temporal information and map the measured data to a proper feature space, the authors propose a predictor based on a Conditional Restricted Boltzmann Machine (CRBM), which can capture the potential temporal relationship of the historical information. Meanwhile, the measured data of different sources are combined to enhance the training process, which can enlarge the training size and avoid the over-fit problem. Experiments show that combining the measured results from different resolutions for a video can raise prediction performance, and the CRBM algorithm shows better prediction ability and more stable performance than the baseline algorithms.