DiSK: A distributed shared disk cache for HPC environments

Author(s):  
Brandon Szeliga ◽  
Tung Nguyen ◽  
Weisong Shi
Keyword(s):  
Author(s):  
Hsung-Pin Chang ◽  
Ray-I Chang ◽  
Wei-Kuan Shih ◽  
Ruei-Chuan Chang
Keyword(s):  

2018 ◽  
Vol 8 (9) ◽  
pp. 1514 ◽  
Author(s):  
Bao Chang ◽  
Hsiu-Fen Tsai ◽  
Yun-Da Lee

This paper first integrates big data tools—Hive, Impala, and SparkSQL—which support SQL-like queries for rapid data retrieval in big data. The three introduced tools are not only suitable for operating in business intelligence to serve high-performance data retrieval, but they are also an open-source software solution with low cost for small-to-medium enterprise use. In practice, the proposed approach provides an in-memory cache and an in-disk cache to achieve a very fast response to a query if a cache hit occurs. Moreover, this paper develops so-called platform selection that is able to select the appropriate tool dealing with input query with effectiveness and efficiency. As a result, the speed of job execution of proposed approach using platform selection is 2.63 times faster than Hive in the Case 1 experiment, and 4.57 times faster in the Case 2 experiment.


2014 ◽  
Vol 9 (10) ◽  
Author(s):  
Liu Yang ◽  
Wei Wang
Keyword(s):  

Author(s):  
JingMin Tu ◽  
Xiangang Luo ◽  
Wenjie Zhao ◽  
Xuejing Xie ◽  
Xincai Wu
Keyword(s):  

1992 ◽  
Vol 41 (6) ◽  
pp. 665-676 ◽  
Author(s):  
D. Thiebaut ◽  
H.S. Stone ◽  
J.L. Wolf

1992 ◽  
Vol 18 (1) ◽  
pp. 44-54 ◽  
Author(s):  
S.D. Carson ◽  
S. Setia
Keyword(s):  

Sign in / Sign up

Export Citation Format

Share Document