Nessie: A Decoupled, Client-Driven Key-Value Store Using RDMA
| Autor: | Jonathan Ma, Bernard Wong, Benjamin Cassell, Xiaoyi Liu, Tyler Szepesi, Tim Brecht |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
020203 distributed computing
Remote direct memory access Distributed database business.industry Computer science 020206 networking & telecommunications 02 engineering and technology computer.software_genre Network operations center NESSIE Computational Theory and Mathematics Hardware and Architecture Server Signal Processing 0202 electrical engineering electronic engineering information engineering Operating system Overhead (computing) Data center business computer Throughput (business) Computer network |
| Zdroj: | IEEE Transactions on Parallel and Distributed Systems. 28:3537-3552 |
| ISSN: | 1045-9219 |
| DOI: | 10.1109/tpds.2017.2729545 |
| Popis: | Key-value storage systems are an integral part of many data centre applications, but as demand increases so does the need for high performance. This has motivated new designs that use Remote Direct Memory Access (RDMA) to reduce communication overhead. Current RDMA-enabled key-value stores (RKVSes) target workloads involving small values, running on dedicated servers on which no other applications are running. Outside of these domains, however, there may be other RKVS designs that provide better performance. In this paper, we introduce Nessie, an RKVS that is fully client-driven, meaning no server process is involved in servicing requests. Nessie also decouples its index and storage data structures, allowing indices and data to be placed on different servers. This flexibility can decrease the number of network operations required to service a request. These design elements make Nessie well-suited for a different set of workloads than existing RKVSes. Compared to a server-driven RKVS, Nessie more than doubles system throughput when there is CPU contention on the server, improves throughput by 70 percent for PUT -oriented workloads when data value sizes are 128 KB or larger, and reduces power consumption by 18 percent at 80 percent system utilization and 41 percent at 20 percent system utilization compared with idle power consumption. |
| Databáze: | OpenAIRE |
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