A typical storage hierarchy comprises of components with varying performance and cost characteristics, providing multiple options for data placement. We propose and evaluate a hierarchical storage system, DHIS, that uses application-level hints to discriminate between data with different access characteristics, and then customizes its placement and caching policies to each type. The data placement decisions in DHIS are made in an online fashion, during data creation. Most existing solutions

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... attempt to customize data layout require moving data around, based on access characteristics. DHIS uses two kinds of information to make its decisions. First, it uses knowledge about higher-level pointers between blocks (for example, file system pointers) to understand the relationship between blocks and consequently, their importance. Second, DHIS defines a set of generic attributes that the higher layers can use to annotate data, conveying various properties such as importance, access pattern, etc. Based on these attributes, DHIS dynamically decides to place the data in the hierarchy best suited for its requirements. By doing so, DHIS solves a critical problem faced by storage vendors and developers of higher level storage software, in terms of choosing the most efficient policy among many alternatives. Through several benchmarks, we show that DHIS's data placement decisions improve performance significantly.