Popularity Prediction Tool for ATLAS Distributed Data Management

This paper describes a popularity prediction tool for data-intensive data management systems, such as ATLAS distributed data management (DDM). It is fed by the DDM popularity system, which produces historical reports about ATLAS data usage, providing information about files, datasets, users and sites where data was accessed. The tool described in this contribution uses this historical information to make a prediction about the future popularity of data. It finds trends in the usage of data using a set of neural networks and a set of input parameters and predicts the number of accesses in the near term future. This information can then be used in a second step to improve the distribution of replicas at sites, taking into account the cost of creating new replicas (bandwidth and load on the storage system) compared to gain of having new ones (faster access of data for analysis). To evaluate the benefit of the redistribution a grid simulator is introduced that is able replay real workload on different data distributions. This article describes the popularity prediction method and the simulator that is used to evaluate the redistribution.