TY  - JOUR
A1  - Liang, Feng
A1  - Liu, Yunzhen
A1  - Liu, Hai
A1  - Ma, Shilong
A1  - Schnor, Bettina
T1  - A Parallel Job Execution Time Estimation Approach Based on User Submission Patterns within Computational Grids
JF  - International journal of parallel programming
N2  - Scheduling performance in computational grid can potentially benefit a lot from accurate execution time estimation for parallel jobs. Most existing approaches for the parallel job execution time estimation, however, require ample past job traces and the explicit correlations between the job execution time and the outer layout parameters such as the consumed processor numbers, the user-estimated execution time and the job ID, which are hard to obtain or reveal. This paper presents and evaluates a novel execution time estimation approach for parallel jobs, the user-behavior clustering for execution time estimation, which can give more accurate execution time estimation for parallel jobs through exploring the job similarity and revealing the user submission patterns. Experiment results show that compared to the state-of-art algorithms, our approach can improve the accuracy of the job execution time estimation up to 5.6 %, meanwhile the time that our approach spends on calculation can be reduced up to 3.8 %.
KW  - User submission pattern
KW  - Parallel job execution time estimation
KW  - Computational grid
Y1  - 2015
U6  - https://doi.org/10.1007/s10766-013-0294-1
SN  - 0885-7458
SN  - 1573-7640
VL  - 43
IS  - 3
SP  - 440
EP  - 454
PB  - Springer
CY  - New York
ER  -