基于连续度聚类和动态ARMA时间序列预测I／O区域

为了动态优化存储系统中数据的分布，存储系统需要能动态发现密集I／O区域和预测未来密集I／O访问的区域，并根据发现和预测的结果来指导存储系统的优化。为此，本文根据存储系统的特点提出了实用且高效的基于连续度的聚类算法来发现密集I／O访问的区域，并采用ARMA时间序列模型来预测密集I／O可能访问的区域。为提高预测的准确性，采用了动态参数估计的策略。通过大量实验的结果验证了这两种算法的正确性和预测的准确性，对存储系统的优化具有较好的指导作用。

A New Sequence Degree-based Clustering Algorithm and Dynamical ARMA Time Series Forecasting for I/O Requests

Storage system could optimize data distribution accordingly, on condition that it dynamically finds and predicts the storage areas requested frequently. So, this paper not only introduces a new sequence degree-based clustering algorithm to find the frequently accessed storage areas, but also adopts ARMA time series model to forecast the storage areas requested frequently by future I/O requests. To address the problem of accurate forecast, this paper adopts dynamic parameter estimation policy to ARMA model. The results of a large number of simulations validate the accuracy of the clustering algorithm and the preciseness of the ARMA time series model of dynamic parameter estimation policy.