Retina: Cross-Layered Key-Value Store using Computational Storage

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Virginia Tech


Modern SSDs are getting faster and smarter with near-data computing capabilities. Due to their design choices, traditional key-value stores do not fully leverage these new storage devices. These key-value stores become CPU-bound even before fully utilizing the IO bandwidth. LSM or B+ tree-based key-value stores involve complex garbage collection and store sorted keys and complicated synchronization mechanisms. In this work, we propose a cross-layered key-value store named Retina that decouples the design to delegate control path manipulations to host CPU and data path manipulations to computational SSD to maximize performance and reduce compute bottlenecks. We employ many design choices not explored in other persistent key-value stores to achieve this goal. In addition to the cross-layered design paradigm, Retina introduces a new caching mechanism called Mirror cache, support for variable key-value pairs, and a novel version-based crash consistency model. By enabling all the design features, we equip Retina to reduce compute hotspots on the host CPU, take advantage of the on-storage accelerators to leverage the data locality on the computational storage, improve overall bandwidth and reduce the bandwidth net- work latencies. Thus when evaluated using YCSB, we observe the CPU utilization reduced by 4x and throughput performance improvement of 20.5% against the state-of-the-art for read-intensive workloads.



Computational storage, Key-Value store, Crash consistency