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. / Master of Science / Modern secondary storage systems are providing an exponential increase in memory access speeds. In addition, new generation storage systems attach compute resources near data to offload computation to storage. Traditional datastore systems are lacking in performance when used with the new generation SSDs (Solid State Drive). The key reason is the SSDs are underutilized due to CPU bottlenecks. Due to design choices, conventional datastores incur expensive CPU tasks that cause the CPU to bottleneck even before the storage speeds are fully utilized. Thus, when attached to a modern SSD, conventional datastores will underutilize the storage resources. 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. In addition to the cross-layered design paradigm, Retina introduces a new caching mechanism called Mirror cache 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 and improve overall access speed. To evaluate Retina, we use throughput and CPU utilization as the comparison metric. We test our implementation with Yahoo Cloud Serving Benchmark, a popular datastore benchmark. We evaluate against RocksDB(the most widely adopted datastore) to enable fair performance comparison. In conclusion, we show that Retina key-value store improves the throughput performance by offloading logic to computational storage to reduce the CPU bottlenecks.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/109279 |
| Date | 10 March 2022 |
| Creators | Bikonda, Naga Sanjana |
| Contributors | Electrical and Computer Engineering, Min, Chang Woo, Patterson, Cameron D., Zeng, Haibo |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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