Enterprise storage systems are facing enormous challenges due to increasing growth and heterogeneity of the data stored. Designing future storage systems requires comprehensive insights that existing trace analysis methods are ill-equipped to supply. In this paper, we 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. We apply our method on two large-scale real-world production network storage system traces to obtain comprehensive access patterns and design insights at user, application, file, and directory levels. We derive simple, easily implementable, threshold-based design optimizations that enable efficient data placement and capacity optimization strategies for servers, consolidation policies for clients, and improved caching performance for both.
Based on two large-scale network-attached storage file system traces from a realworld enterprise datacenter, Chen et al. conduct a multi-dimensional analysis of data access patterns at the user, application, file, and directory levels REF .
Design implications for enterprise storage systems via multi-dimensional trace analysis
SOSP '11
Computer Science