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Enterprise storage systems are facing enormous challenges due to increasing growth and heterogeneity of the data stored. Designing future storage systems requires comprehensive in-sights that existing trace analysis methods are ill-equipped to supply. In this paper, the authors seek to provide such insights by using a new methodology that leverages an objective, multi-dimensional statistical technique to extract data access patterns from network storage system traces. They apply their method on two large-scale real-world production net-work storage system traces to obtain comprehensive access patterns and design insights at user, application, file, and directory levels.
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