A flexible direct attached storage for a data intensive application

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

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Big data analysis and a data storing applications require a huge volume of storage and a high I/O performance. Applications can achieve high level of performance and cost efficiency by exploiting the high I/O performance 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. Generally, the data is copied via Ethernet when transferring the data from the internal HDDs to the new server. However, the amount of data will continue to rapidly increase, and thus, it will be hard to make these types of transfers through the Ethernet since it will take a long time. A storage area network such as iSCSI can be used to avoid this problem because the data can be shared with the servers. However, this decreases the level of performance and increases the costs. Improving the flexibility without incurring I/O performance degradation is required in order to improve the DAS architecture. In response to this issue, we propose FlexDAS, which improves the flexibility of direct attached storage by using a disk area network (DAN) without degradation the I/O performance. A resource manager connects or disconnects the computation nodes to the HDDs via the FlexDAS switch, which supports the SAS or SATA protocols. This function enables for the servers to be replaced in a short period of time. 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. We also confirmed that the FlexDAS improves the performance of the data intensive applications by up to 2.84 times compared with the iSCSI.

Original languageEnglish
Pages (from-to)2168-2177
Number of pages10
JournalIEICE Transactions on Information and Systems
Issue number12
Publication statusPublished - Dec 2015
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence


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