caa a22 4500 445852003 CHVBK 20180317145416.0 cr unu---uuuuu 170323e20110601xx s 000 0 eng 10.1007/s11227-009-0365-7 doi (NATIONALLICENCE)springer-10.1007/s11227-009-0365-7 Improving job scheduling performance with parallel access to replicas in Data Grid environment [Elektronische Daten] [Junwei Zhang, Bu-Sung Lee, Xueyan Tang, Chai-Kiat Yeo] Data Grid has evolved to be the solution for data-intensive applications, such as High Energy Physics (HEP), astrophysics, and computational genomics. These applications usually have large input of data to be analyzed and these input data are widely replicated across Data Grid to improve the performance. The job scheduling performance on traditional computing jobs can be studied using queuing theory. However, with the addition of data transfer, the job scheduling performance is too complex to be modeled. In this research, we study the impact of data transfer on the performance of job scheduling in the Data Grid environment. We have proposed a parallel downloading system that supports replicating data fragments and parallel downloading of replicated data fragments, to improve the job scheduling performance. The performance of the parallel downloading system is compared with non-parallel downloading system, using three scheduling heuristics: Shortest Turnaround Time (STT), Least Relative Load (LRL) and Data Present (DP). Our simulation results show that the proposed parallel download approach greatly improves the Data Grid performance for all three scheduling algorithms, in terms of the geometric mean of job turnaround time. The advantage of parallel downloading system is most evident when the Data Grid has relatively low network bandwidth and relatively high computing power. Springer Science+Business Media, LLC, 2009 Data Grid nationallicence Data Replication nationallicence Parallel download nationallicence Job scheduling nationallicence Zhang Junwei School of Computer Engineering, Nanyang Technological University, Singapore, Singapore aut Lee Bu-Sung School of Computer Engineering, Nanyang Technological University, Singapore, Singapore aut Tang Xueyan School of Computer Engineering, Nanyang Technological University, Singapore, Singapore aut Yeo Chai-Kiat School of Computer Engineering, Nanyang Technological University, Singapore, Singapore aut The Journal of Supercomputing Springer US; http://www.springer-ny.com 56/3(2011-06-01), 245-269 0920-8542 56:3<245 2011 56 11227 https://doi.org/10.1007/s11227-009-0365-7 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11227-009-0365-7 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Zhang Junwei School of Computer Engineering, Nanyang Technological University, Singapore, Singapore aut NATIONALLICENCE 700 1- Lee Bu-Sung School of Computer Engineering, Nanyang Technological University, Singapore, Singapore aut NATIONALLICENCE 700 1- Tang Xueyan School of Computer Engineering, Nanyang Technological University, Singapore, Singapore aut NATIONALLICENCE 700 1- Yeo Chai-Kiat School of Computer Engineering, Nanyang Technological University, Singapore, Singapore aut NATIONALLICENCE 773 0- The Journal of Supercomputing Springer US; http://www.springer-ny.com 56/3(2011-06-01), 245-269 0920-8542 56:3<245 2011 56 11227 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer