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A PTS-PGATS based approach for data-intensive scheduling in data grids

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Abstract

Grid computing is the combination of computer resources in a loosely coupled, heterogeneous, and geographically dispersed environment. Grid data are the data used in grid computing, which consists of large-scale data-intensive applications, producing and consuming huge amounts of data, distributed across a large number of machines. Data grid computing composes sets of independent tasks each of which require massive distributed data sets that may each be replicated on different resources. To reduce the completion time of the application and improve the performance of the grid, appropriate computing resources should be selected to execute the tasks and appropriate storage resources selected to serve the files required by the tasks. So the problem can be broken into two sub-problems: selection of storage resources and assignment of tasks to computing resources. This paper proposes a scheduler, which is broken into three parts that can run in parallel and uses both parallel tabu search and a parallel genetic algorithm. Finally, the proposed algorithm is evaluated by comparing it with other related algorithms, which target minimizing makespan. Simulation results show that the proposed approach can be a good choice for scheduling large data grid applications.

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Authors and Affiliations

  1. College of Information Science and Engineering, Hunan University, Changsha, 410082, China

    Kenli Li, Zhao Tong, Dan Liu & Teklay Tesfazghi

  2. Computer School, National University of Defense Technology, Changsha, 410072, China

    Xiangke Liao

Authors
  1. Kenli Li
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  2. Zhao Tong
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  3. Dan Liu
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  4. Teklay Tesfazghi
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  5. Xiangke Liao
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Correspondence to Kenli Li.

Additional information

Kenli Li received his PhD in Computer Science from Huazhong University of Science and Technology, China, in 2003, his MSc in Mathematics from Central South University, China, in 2000, and BSc in Mathematics from the National University of Defense Technology, in 1995. He was a visiting scholar at University of Illinois at Champaign and Urbana from 2004 to 2005. Now He is a professor of Computer Science and Technology at Hunan University, a senior member of CCF. His major research includes parallel computing, grid and cloud computing, and DNA computing.

Zhao Tong received his MSc from Hunan Agricultural University, China, in 2010, and his BSc in Computer Science from Beijing Institute of Technology in 2007. He is currently a PhD candidate in Hunan University, China. His research interests include modeling and scheduling for parallel and distributed computing systems, parallel system reliability, and parallel algorithms.

Dan Liu received his BSc in accounting and MSc in Computer Science from Hunan University, China, in 2007 and 2010, respectively. His research interests focus on distributed computing, high performance computing, and grid computing.

Teklay Tesfazghi is a lecturer at MaiNefhi Institute of Technology, Eritrea. He received his BSc in Mathematics from the University of Asmara in 1996 and his MSc in Computer Engineering and Applications from Hunan University, China, in 2011. His research interests include distributed computing, parallel databases, and data mining.

Xiangke Liao received his BSc degree in Computer Science from Tsinghua University, China, in 1985, and his MSc in 1988 from the National University of Defense Technology, China, where he is now a professor and the dean of the School of Computers. His research interests include parallel and distributed computing, highperformance computer systems, operating systems, and networked embedded systems.

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Li, K., Tong, Z., Liu, D. et al. A PTS-PGATS based approach for data-intensive scheduling in data grids. Front. Comput. Sci. China 5, 513–525 (2011). https://doi.org/10.1007/s11704-011-0970-5

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