FlexDAS: A flexible direct attached storage for I/O intensive applications

Takatsugu Ono, Yotaro Konishi, Teruo Tanimoto, Noboru Iwamatsu, Takashi Miyoshi, Jun Tanaka

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

5 Citations (Scopus)

Abstract

Big data analysis and a data storing applications require a huge volume of storage and a high I/O performance. Applications can achieve high levels of performance and cost efficiency by exploiting the high I/O performances of direct attached storages (DAS) such as internal HDDs. With the size of stored data ever increasing, it will be difficult to replace servers since internal HDDs contain huge amounts of data. In response to this issue, we propose FlexDAS, which improves the flexibility of direct attached storage by using a disk area network (DAN) without degrading the I/O performance. We developed a prototype FlexDAS switch and quantitatively evaluated the architecture. Results show that the FlexDAS switch can disconnect and connect the HDD to the server in just 1.16 seconds. The I/O performances of the disks connected via the FlexDAS switch were almost the same as the conventional DAS architecture.

Original languageEnglish
Title of host publicationProceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014
EditorsWo Chang, Jun Huan, Nick Cercone, Saumyadipta Pyne, Vasant Honavar, Jimmy Lin, Xiaohua Tony Hu, Charu Aggarwal, Bamshad Mobasher, Jian Pei, Raghunath Nambiar
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages147-152
Number of pages6
ISBN (Electronic)9781479956654
DOIs
Publication statusPublished - Jan 7 2015
Externally publishedYes
Event2nd IEEE International Conference on Big Data, IEEE Big Data 2014 - Washington, United States
Duration: Oct 27 2014Oct 30 2014

Publication series

NameProceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014

Other

Other2nd IEEE International Conference on Big Data, IEEE Big Data 2014
Country/TerritoryUnited States
CityWashington
Period10/27/1410/30/14

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Information Systems

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