The Key-Value Solid State Drive (KV-SSD) represents a significant evolution in storage device interfaces by accommodating non-page-aligned key-value pairs, a departure from conventional models. However, KV-SSDs encounter challenges as their specialized data transfer and packing requirements conflict with established storage protocols like NVMe, which are designed around fixed memory page units. This discord leads to inefficient data movement and increased NAND page write I/Os, which in turn escalates network traffic and degrades both performance and NAND efficiency. To tackle these challenges, this paper introduces BandSlim, a novel solution equipped with two methods to streamline bandwidth during I/O transmission: (i) a fine-grained inline value transfer utilizing NVMe commands for bandwidth-efficient value transfer, and (ii) a selective value packing strategy combined with a backfilling policy to reduce NAND page write I/Os. We integrated BandSlim on a state-of-the-art FPGA-based LSM-tree KV-SSD, utilizing the Cosmos+ OpenSSD platform. Our comprehensive evaluations illustrate that BandSlim achieves a remarkable reduction in PCIe traffic of up to 97.9% and NAND page write counts by up to 98.1% compared to the NVMe-based KV-SSD without employing BandSlim.

Popular software key-value stores such as LevelDB and RocksDB are often tailored for efficient writing. Yet, they tend to also perform well on read operations. This is because while data is initially stored in a format that favors writes, it is later transformed by the DB in the background into a format that better accommodates reads. Write-optimized key-value stores can still block writes. This happens when those background workers cannot keep up with the foreground insertion workload.This paper advocates for a hardware-accelerated key-value store, enabling performance-critical operations, like background data reorganization and queries, to execute directly on storage instead of a host as existing key-value stores do. This better hides background work latency, prevents it from blocking foreground writes, and improves overall I/O efficiency. Our prototype, called KV-CSD, is a key-value based computational storage device consisting of an NVMe SSD and a System-on-a-Chip (SoC) that implements an ordered key-value store atop the SSD. Through offloaded processing, KV-CSD streamlines data insertion, reduces host-device data movement for both background data reorganization and query processing, and shows up to 10.6× lower write times and up to 7.4× faster queries compared to the current state-of-the-art software key-value stores on a real scientific dataset.

In this paper, we propose OctoKV, an innovative network-based key-value storage system. OctoKV addresses the repetitive address translation overhead associated with traditional key-value stores running on file systems on the client side. To mitigate this overhead, we implemented the key-value store on the server side using NVMe-oF and a user-level NVMe driver. In particular, we employed fine-grained resource monitoring and load balancing based on heuristics to optimize I/O performance. OctoKV is deployed on a Linux cluster with Intel SPDK. The extensive evaluation shows that OctoKV achieves lower I/O response times in comparison to traditional approaches where key-value stores run on the client side. Also, the proposed load balancing strategies efficiently enhance I/O response times by equally distributing the workload from overloaded cores to other cores.

Key-Value SSD (KVSSD) has shown great potential for several important classes of emerging data stores due to its high throughput and low latency. When designing a key-value store with range queries, an LSM-tree is considered a better choice than a hash table due to its key ordering. However, the design space for range queries in LSM-tree-based KVSSDs has yet to be explored, despite range queries being one of the most demanding features. In this paper, we investigate the design constraints in LSM-tree-based KVSSDs from the perspective of range queries and propose three design principles. Based on these principles, we present IterKVSSD, an Iterator interface extended LSM-tree-based KVSSD for range queries. We implement IterKVSSD on OpenSSD Cosmos+, and our evaluation shows that it increases range query throughput by up to 4.13× and 7.22× for random and sequential key distributions, respectively, compared to existing KVSSDs.

Key-value SSDs (KVSSDs) introduced so far are limited in their use as an alternative to the key-value store running on the host due to the following technical limitations. First, they were designed only for a single tenant, limiting the use of multiple tenants. Second, they mainly focused on designing indexes for primary key-based searches, without supporting various queries using a combination of primary key and non-primary attribute-based searches. This article proposes Cerberus , a Log Structured Merged (LSM) tree-based KVSSD armed with (1) namespace and performance isolation for multiple tenants in a multi-tenant environment and (2) capability for processing non-primary attribute-based search queries. Specifically, Cerberus identifies the tenant’s namespace and splits a single large LSM-tree into namespace-specific LSM-tree indexes for tenants. Cerberus also manages secondary LSM-tree indexes to enable non-primary attribute-based data access and fast search query processing. With the SSD-internal CPU/DRAM resources, Cerberus supports non-primary attribute-based search queries and handles complex queries that are combined with search and computing operations. We prototyped Cerberus on the Cosmos+ OpenSSD platform. When there are multiple tenants, Cerberus exhibits up to 2.9× higher read throughput and negligible write overhead compared to existing KVSSD. Cerberus also shows lower latency by up to 9.31× for non-primary attribute-based queries.

Several key-value stores such as RocksDB and MongoDB are implemented on the file system using the Log-Structured Merge-Tree (LSM-tree). The LSM-tree involves high compaction overhead. To minimize this overhead, WiscKey, the state-of-the-art LSM-tree, separates key and value, appends the value to the Value Log file, and LSM-tree manages only the key and Value Log offset. This minimizes the compaction overhead by reducing the number of SSTables managed by the LSM-tree. However, WiscKey still has a high I/O stack overhead that must go through the OS file system and block-layer. Therefore, this paper proposes iLSM-SSD that implements WiscKey in SSD and supports near-data processing. iLSM-SSD has the following features: (i) iLSM-SSD implements a key-value separation based LSM-tree in a limited memory space inside the SSD. (ii) The Value Log offset update management overhead incurred during the Value Log cleaning has a significant performance impact on CPU and memory-constrained SSD environments. To minimize this overhead, iLSM-SSD implements Scattered Logging, which reuses invalidated Value Log pages on the Value Log. (iii) iLSM-SSD manages the data layout internally. This enables iLSM-SSD to eliminate the need for file system interactions to obtain the data layout for in-storage processing on traditional block-interface-based SSDs. We prototyped the iLSM-SSD on the Cosmos+ OpenSSD platform in a Linux environment. Extensive evaluations with synthetic benchmarks have shown that the PUT performance of iLSM-SSD is 1.6-4 times higher than that of WiscKey implemented in RocksDB.