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Key-value store based on a log-structured merge-tree (LSM-tree) is preferable to hash-based key-value store, because an LSM-tree can support a wider variety of operations and show better performance, especially for writes. However, LSM-tree is difficult to implement in the resource constrained environment of a key-value SSD (KV-SSD), and, consequently, KV-SSDs typically use hash-based schemes. We present PinK , a design and implementation of an LSM-tree-based KV-SSD, which compared to a hash-based KV-SSD, reduces 99th percentile tail latency by 73%, improves average read latency by 42%, and shows 37% higher throughput. The key idea in improving the performance of an LSM-tree in a resource constrained environment is to avoid the use of Bloom filters and instead, use a small amount of DRAM to keep/pin the top levels of the LSM-tree. We also find that PinK is able to provide a flexible design space for a wide range of KV workloads by leveraging the read-write tradeoff in LSM-trees.

We propose LightStore, a key-value flash store, as a substitute for x86-based storage servers. A LightStore node has a low-power embedded-class processor, a few gigabytes of DRAM and a few terabytes of NAND flash, and can be directly connected to a network port in a datacenter. A largescale distributed storage cluster can be formed simply by adding more LightStore nodes to the network. Applications in a datacenter can take multiple "software-defined" views of LightStore stores via thin LightStore adapter layers, which translate conventional KV, YCSB, block, and file accesses to KV ones for LightStore. LightStore is estimated to be 2.0x power-efficient and 2.3x space-efficient than an x86-based all-flash array system of the same capacity. Experimental results on our LightStore prototype show that 1) the Light-Store node performance is comparable to an x86 server with a single SSD; 2) a four-node LightStore cluster exhibits up to 7.4x better ops/J than an x86 server with four SSDs.

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