A Virtualization-Based Hybrid Storage System for a Map-Reduce Framework-Reference-Cited by-同舟云学术

A Virtualization-Based Hybrid Storage System for a Map-Reduce Framework

Published:2016 Issue:9 Volume:E99.D Page:2248-2258
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

DEREJE TEKILU Aseffa¹,WU Chin-Hsien¹

Affiliation:

1. Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

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

Link

https://www.jstage.jst.go.jp/article/transinf/E99.D/9/E99.D_2015EDP7365/_pdf

Reference24 articles.

1. [1] J. Dean and S. Ghemawat, “MapReduce: Simplified data processing on large clusters,” In OSDI, vol.51, no.1, pp.107-113, 2004.

2. [2] S.-H. Kang, D.-H. Koo, W.-H. Kang, and S.-W. Lee, “A case for flash memory SSD in Hadoop applications,” International Journal of Control and Automation, vol.6, no.1, Feb. 2013.

3. [3] M. Wasi-ur-Rahman, N.S. Islam, X. Lu, J. Jose, H. Subramoni, H. Wang, and D.K.D. Panda, “High-performance RDMA-based design of Hadoop MapReduce over InfiniBand,” IEEE 27th International Symposium on Parallel and Distributed Processing Workshops and PhD Forum, Cambridge, MA, pp.1908-1917, 2013.

4. [4] K. Kambatla and Y. Chen, “The truth about MapReduce performance on SSDs,” 28th Large Installation System Administration Conference (LISA14), USENIX Association, Seattle, WA, 2014.

5. [5] S. Moon, J. Lee, Y.S. Kee, “Introducing SSDs to the Hadoop MapReduce Framework,” IEEE 7th International Conference on Cloud Computing (CLOUD), pp.272-279, June 27-July 2 2014.