A Data-Intensive Workflow Scheduling Algorithm for Grid Computing

被引：2

作者：

Xu, Meng ^{[1
]}

Cui, Lizhen ^{[1
]}

Wang, Haiyang ^{[1
]}

Bi, Yanbing ^{[1
]}

Bian, Ji ^{[1
]}

机构：

[1] Shandong Univ, Sch Comp Sci & Technol, Jinan 250100, Peoples R China

来源：

FOURTH CHINAGRID ANNUAL CONFERENCE, PROCEEDINGS | 2009年

关键词：

Data-intensive; Workflow; Scheduling; Grid computing;

D O I：

10.1109/ChinaGrid.2009.30

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

The data-intensive workflow in scientific and enterprise grids has gained popularity in recent times. Data-intensive workflow needs to access, process and transfer large datasets that may each be replicated on different data hosts. Because of the large data sets, the execution time is bounded by the cost of data transfer. Minimizing the time of transferring these datasets to the computational resources where the tasks of workflow are executed requires that appropriate computational and data resources be selected. In this paper, we introduce an algorithm MDTT to select the resource set which the task should be mapped. Our experiments show that our algorithm is able to minimize the total makespan of data-intensive workflow and the time of data transferring.

引用

页码：110 / 115

页数：6

共 10 条

[1] The data grid: Towards an architecture for the distributed management and analysis of large scientific datasets [J].

Chervenak, A ;

Foster, I ;

Kesselman, C ;

Salisbury, C ;

Tuecke, S .

JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, 2000, 23 (03) :187-200

[2] Special issue: Workflow in grid systems [J].

Fox, Geoffrey C. ;

Gannon, Dennis .

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2006, 18 (10) :1009-1019

[3]

HOSCHEK W, 2000, P 1 IEEE ACM INT WOR

[4]

Kesselman C., 1999, GRID BLUEPRINT FUTUR

[5] Data-intensive computing and digital libraries [J].

Moore, R ;

Prince, TA ;

Ellisman, M .

COMMUNICATIONS OF THE ACM, 1998, 41 (11) :56-62

[6]

Rahman M, 2007, P IEEE INT C E-SCI, P35

[7]

SAKELLARIOU R, 2004, 13 HET COMP WORKSH H

[8] Performance-effective and low-complexity task scheduling for heterogeneous computing [J].

Topcuoglu, H ;

Hariri, S ;

Wu, MY .

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2002, 13 (03) :260-274

[9] A set coverage-based mapping heuristic for scheduling distributed data-intensive applications on global Grids [J].

Venugopal, Srikumar ;

Buyya, Rajkumar .

2006 7TH IEEE/ACM INTERNATIONAL CONFERENCE ON GRID COMPUTING, 2006, :238-+

[10]

Yu J, 2008, STUD COMPUT INTELL, V146, P173

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