Characterizing E-Science File Access Behavior via Latent Dirichlet Allocation

E-science is moving from grids to clouds. Getting the best of both worlds needs to build on the experience gained by the steady operation of production grids since some years. We propose a new approach for analyzing behavioral traces: as most of them are indeed text documents, state of the art techniques in text mining, and specifically latent Dirichlet allocation, can be exploited. The advantages are twofold: providing some level of explanation inferred from the data, and a relatively scalable way to capture the temporal variability of the behavior of interest, while retaining the full dimensionality of the problem at hand. We experiment the text mining analogy by characterizing file access behavior on data from the steady operation of the largest production grid. We validate the resulting probabilistic model by showing that it is capable of generating synthetic traces statistically consistent with the real ones. The approach would equally apply to wider contexts such as social networks activity or web access.