An Adaptive Performance Prediction Method of Distributed File System Based on Performance Correlation

Distributed file systems are emerging as a key component of large scale cloud storage platform due to the continuous growth of the amount of application data. Performance modeling and analysis is an important concern in the distributed file system area. This paper focuses on the performance prediction and modeling issues. An adaptive prediction model (APModel) is proposed to predict the performance of distributed file systems by capturing the performance correlation of different performance factors. We perform a series of experiments to validate the proposed prediction model. The experiment results indicate our proposed approach can get better prediction accuracy. It is practical and can achieve sufficient performance analysis for distributed file systems.