Abstract
Migrating popular videos into cloud is an efficient mechanism to decrease the costs of VoD services, but how to get the set of popular videos is a key problem. In this paper, we propose a prediction mechanism named SBDP to evaluate future server bandwidth demands on each video. Firstly, SBDP adopts time-series analysis techniques and history dataset to predict online population in advance. Then in order to obtain initial online population, the curve shape similar (CSS) and Gaussian process regression (GPR) methods are introduced. And finally a multi linear regression (MLR) method with the online population as a main factor is proved and applied in predicting seeds’ upload bandwidth. The proposed methods are simulated on large dataset collected from a popular VoD services in China and the simulation results demonstrate that the prediction data are close to real-world data.
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