With the rise of cloud storage and many data intensive applications, there is an unprecedented growth in the volume of unstructured data. In response, key-value object storage is becoming more popular for the ease with which it can store, manage, and retrieve large amounts of this data. Seagate recently launched Kinetic direct-access-over-Ethernet hard drives which incorporate a LevelDB key-value store inside each drive. In this work, we evaluate these drives using micro as well as macro benchmarks to help understand the performance limits, trade-offs, and implications of replacing traditional hard drives with Kinetic drives in data centers and high performance systems. We perform in-depth throughput and latency benchmarking of these Kinetic drives (each acting as a tiny independent server) from a client machine connected to them via Ethernet. We compare these results to a SATA-based and a faster SAS-based traditional server running LevelDB. Our sample Kinetic drives are CPU-bound, but they still average sequential write throughput of 63 MB/sec and sequential read throughput of 78 MB/sec for 1 MB value sizes. They also demonstrate unique Kinetic features including direct disk-to-disk data transfer. Our macro benchmarking using the Yahoo Cloud Serving Benchmark (YCSB) shows that mid-range LevelDB servers outperform the Kinetic drives for several workloads; however, this is not always the case. For larger value sizes, even these first generation sample Kinetic drives outperform a full server for several different workloads.
|Original language||English (US)|
|Title of host publication||Proceedings - 2017 IEEE 23rd International Conference on Parallel and Distributed Systems, ICPADS 2017|
|Publisher||IEEE Computer Society|
|Number of pages||10|
|State||Published - May 29 2018|
|Event||23rd IEEE International Conference on Parallel and Distributed Systems, ICPADS 2017 - Shenzhen, China|
Duration: Dec 15 2017 → Dec 17 2017
|Name||Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS|
|Other||23rd IEEE International Conference on Parallel and Distributed Systems, ICPADS 2017|
|Period||12/15/17 → 12/17/17|
Bibliographical noteFunding Information:
This work was supported in part by the Center for Research in Intelligent Storage (CRIS), which is supported by National Science Foundation grant no. IIP-1439622 (July, 2014 - August, 2019) and member companies. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. This work was partially supported by Hankuk University of Foreign Studies Research Fund. The authors would like to thank Seagate for their support of this project and, in particular, Bryan Wyatt for his technical assistance.
This work was supported in part by the Center for Research in Intelligent Storage (CRIS), which is supported by National Science Foundation grant no. IIP-1439622 (July, 2014 - August, 2019) and member companies.
© 2017 IEEE.
Copyright 2018 Elsevier B.V., All rights reserved.
- Cloud Applications
- Data Center Storage Architecture
- Key -Value Store
- Performance Evaluation