Storage Performance Optimization Based on ARIMA

This paper analyzes the potential causes of the performance bottleneck in I/O access paths of storage architecture and proposes a predictive approach based on feedforward to optimize the I/O performance of storage subsystems effectively, which uses a time series analysis method based on ARIMA to build the predictive and monitor model of the performance. This approach can improve the availability of the storage subsystem effectively and decrease TCO by decreasing the possibility of I/O bottlenecks.