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MRIS: Multi-Resultion Image Store CSE602 Course Project In many scenarios, the access patterns and the sizes of structured objects present a property that matches the property of the storage hierarchy. For example, in an image storage system, the attributes and thumbnails of stored images are small in size but frequently accessed, which makes them fit well in small but fast and expensive top-tiered storage devices, such as NVRAM and SSD. Whereas the images themselves are big but less accessed, which makes them suitable for large but slow and inexpensive bottom-tiered storage devices, such as HDD and tape. Moreover, slow seeks of bottom-tiered devices can be amortized by fast sequential access followed. For small objects, throughput is important in term of op/sec. They tend to be accessed randomly because of their small sizes and the implication that they are likely to be metadata and attributes. Top-tiered devices, e.g., NVRAM, exhibit great IOPS performance, and they allow storage to be used in finer granularity which causes less inner fragmentation as well. But for large objects, throughput is more important in terms of mb/s. Their I/Os tend to be more sequential as well. Bottom-tiered devices, e.g., HDD, are large in capacity and exhibit satisfactory throughput for sequential I/Os. This project tries to prove the idea that when objects present a size-tiered property, a corresponding size-tiered object store can provide good trade-off between cost and performance as it gets the best from different tiers of the storage hierarchy. Essentially, we are considering the object size as one more cost in the design of storage cache (NVRAM). Some in-memory cache already do that, for example, the "cache charge" in LevelDB cache. However, most storage systems do not because of the traditional view of block devices as blocks of fixed size. This project is a follow-up work of my labmate's work publised in "An Efficient Multi-Tier Tablet Server Storage Architecture". The idea is also supported by some recent publications, for example, Facebook's Sigmetrics'12 paper "Workload analysis of a large-scale key-value store". The project is based on KV databases include LevelDB from Google and KVDB from FSL Stony Brook.