Kinetic action: Performance analysis of integrated key-value storage devices vs. LevelDB servers

Manas Minglani, Jim Diehl, Xiang Cao, Binghze Li, Dongchul Park, David J. Lilja, David H.C. Du

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

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 languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE 23rd International Conference on Parallel and Distributed Systems, ICPADS 2017
PublisherIEEE Computer Society
Pages501-510
Number of pages10
ISBN (Electronic)9781538621295
DOIs
StatePublished - May 29 2018
Event23rd IEEE International Conference on Parallel and Distributed Systems, ICPADS 2017 - Shenzhen, China
Duration: Dec 15 2017Dec 17 2017

Publication series

NameProceedings of the International Conference on Parallel and Distributed Systems - ICPADS
Volume2017-December
ISSN (Print)1521-9097

Other

Other23rd IEEE International Conference on Parallel and Distributed Systems, ICPADS 2017
CountryChina
CityShenzhen
Period12/15/1712/17/17

Bibliographical note

Funding 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.

Funding 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.

Publisher Copyright:
© 2017 IEEE.

Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.

Keywords

  • Cloud Applications
  • Data Center Storage Architecture
  • Key -Value Store
  • Performance Evaluation

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