  The LHCb collaboration is one of the four major experiments at the Large
Hadron Collider at CERN. Many petabytes of data are produced by the detectors
and Monte-Carlo simulations. The LHCb Grid interware LHCbDIRAC is used to make
data available to all collaboration members around the world. The data is
replicated to the Grid sites in different locations. However the Grid disk
storage is limited and does not allow keeping replicas of each file at all
sites. Thus it is essential to optimize number of replicas to achieve a better
Grid performance.
  In this study, we present a new approach of data replication and distribution
strategy based on data popularity prediction. The popularity is performed based
on the data access history and metadata, and uses machine learning techniques
and time series analysis methods.
