PIVOT: An adaptive information discovery framework for computational grids

  • Authors:

  • Guiyi Wei;Yun Ling;Athanasios V. Vasilakos;Bin Xiao;Yao Zheng

  • Affiliations:

  • College of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou, PR China;College of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou, PR China;Department of Computer and Telecommunications Engineering, University of Western Macedonia, Kozani, Greece;Department of Computing, Hong Kong Polytechnic University, Hong Kong, PR China;School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, PR China

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2010

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Abstract

In a traditional computational grid environment, the owners of resources usually provide information about their resources extracted by pre-configured information services or web services. However, such information is not sufficient for the scheduler in the high-performance distributed computing. To solve this problem, we propose a scalable grid information service framework, named PIVOT (adaPtive Information discoVery framewOrk for compuTational grid). By using deadline-constrained flooding collector dissemination and P2P-like information collection schemes, PIVOT provides an active mechanism to collect application-specific resource information. In particular, PIVOT provides a resource information service for application-specific schedulers. The best-effort performance on overhead traffic and communication latency during information discovery is guaranteed by two new distributed cooperative algorithms. The experimental results in the simulations and real computational grid platform demonstrate that PIVOT has a high level of adaptability for application-specific resource information discovery, and also improves the accuracy of resource allocation and the efficiency of executing parallel tasks in traditional information services.