Automated and Intelligent Data Migration Strategy in High Energy Physical Storage Systems

Abstract

As a data-intensive computing application, high-energy physics requires storage and computing for large amounts of data at the PB level. IHEP computing center is beginning to use tiered storage architectures, such as tape, disk or solid state drives to reduce hardware purchase costs and power consumption. At present, automatic data migration strategies are mainly used to resolve data migration between memory and disk. So the rules are relatively simple. This paper attempted to use the deep learning algorithm model to predict the evolution trend of data access heat as the basis for data migration. The implementation of some initial parts of the system were discussed, as well as the file trace collector and the LSTM model. At last some preliminary experiments are conducted with these parts.