A Recursively-Adjusting Co-allocation scheme with a Cyber-Transformer in Data Grids

  • Authors:

  • Chao-Tung Yang;I-Hsien Yang;Shih-Yu Wang;Ching-Hsien Hsu;Kuan-Ching Li

  • Affiliations:

  • High Performance Computing Laboratory, Department of Computer Science and Information Engineering, Tunghai University, Taichung 40704, Taiwan, ROC;High Performance Computing Laboratory, Department of Computer Science and Information Engineering, Tunghai University, Taichung 40704, Taiwan, ROC;High Performance Computing Laboratory, Department of Computer Science and Information Engineering, Tunghai University, Taichung 40704, Taiwan, ROC;Department of Computer Science and Information Engineering, Chung Hua University, Hsinchu 300, Taiwan;Parallel and Distributed Processing Center, Department of Computer Science and Information Management, Providence University, Taichung 43301, Taiwan, ROC

  • Venue:
  • Future Generation Computer Systems
  • Year:
  • 2009

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Abstract

A co-allocation architecture was developed in order to enable parallel downloads of datasets from multiple servers. Several co-allocation strategies have been coupled and used to exploit rate differences among various client-server links and to address dynamic rate fluctuations by dividing files into multiple blocks of equal sizes. However, a major obstacle, the idle time of faster servers having to wait for the slowest server to deliver the final block, makes it important to reduce differences in finish times among replica servers. In this paper, we propose Recursively-Adjusting Co-Allocation, a dynamic co-allocation scheme for improving data transfer performance in Data Grids. The experimental results show that our approach can reduce the idle time spent waiting for the slowest server and decrease data transfer completion times. We developed Cyber-Transformer, a new toolkit with a friendly GUI interface that makes it easy for inexperienced users to manage replicas and download files in Data Grid environments. We also provide an effective scheme for reducing the cost of reassembling data blocks.