Abstract
In this paper we propose to use Zipf-like distribution to predict popularity data in storage systems. It can estimate prediction parameters according to the present statistics of I/O access. We classify the popularity data from every trace, and analyze the prediction rate through the classified popularity data’s characteristic. We synthesize the analysis results in different prediction time granularity and prediction popularity data queue. Finally, we use block I/O traces to discuss the effectiveness of prediction method. The discussion and analysis results indicate that this prediction method can predict the popularity data efficiently.
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