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Resource Selection and Topology Transformation on Computational Grids
|關鍵字:||格網運算;異質性;計算格網;格網資源拓樸;資源選擇;資源拓樸轉換法;虛擬網目||出版社:||資訊科學與工程學系所||引用:|| I. Foster and C. Kesselman, Eds., The Grid: Blueprint for a New ComputingInfrastructure, 1st ed. San Francisco, CA, USA: Morgan-Kaufman, July 1998.  W. Gentzsch, “Grid computing, a vendor's vision,” in CCGRID '02: Proceed-ings of the 2nd IEEE/ACM International Symposium on Cluster Computingand the Grid, 2002, p. 290.  A. Iamnitchi, I. Foster, and D. Nurmi, “A peer-to-peer approach to resourcelocation in grid environments,” in Proceedings of the 11th IEEE InternationalSymposium on High Performance Distributed Computing, August 2002.  R. D. Schlichting, A. A. Chien, C. Kesselman, K. Marzullo, J. Plank, and S. K.Shrivastava, “Dependability and the grid: Issues and challenges,” in DSN '02:Proceedings of the 2002 International Conference on Dependable Systems andNetworks, 2002, pp. 263-266.  Grid Computing Info Centre (GRID Infoware). [Online]. Available: http://www.gridcomputing.com  Globus Alliance. [Online]. 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Stein, Introduction toAlgorithms, Second Edition. The MIT Press, September 2001.||摘要:||
格網運算(grid computing)是未來分散式電腦運算的發展趨勢，因為它透過網路的連結，整合許多異質性(heterogeneous)的計算資源、設備與資料庫，可提供使用者強大的運算能量與資料處理能力。此論文在計算格網(computational grid)領域中提供有用的計算資源的使用與管理技術，論文討論的主題包括以下三個部分：
首先，為了模擬計算格網，作者定義了格網資源拓樸的模型(grid resource topology)，並探討如何將其真實化的議題，以便讓格網資源拓樸能具備現實資源的特性。
第三，此論文提出的計算格網之資源拓樸轉換法(resource topology transformation)可有效地將格網資源拓樸轉換成一個虛擬網目(virtual mesh)，此虛擬網目可結合傳統平行處理的演算法與計算格網的能力，以便在計算格網上使用以網目架構為基礎的平行演算法來解決科學計算問題。
Grid computing represents a new paradigm in distributed computing, integrating the heterogeneous computers, networks, databases, scientific instruments, and other resources managed by multiple organizations. Grid technology can provide massive computing power to support applications requiring large-scale computation or data analysis.
This dissertation addresses three major topics related to computational grids. The first topic deals with the characterization of grid resources. The author defines a grid resource model and establishes a workflow to depict the grid resource topology, using a number of probabilistic and statistical techniques. These techniques are then used to approximate the characteristics of grid resources in the real world.
The second topic deals with the selection of resources on computational grids. The author proposes several fundamental policies for grid resource selection and evaluates them according to their performance in scheduling DAG-like problem models. The experiments produces two significant results: the useful number of selected grid resources is strongly bounded by the implicit parallelism of tasks in the problem models; and the scheduling length can be reduced by considering both of computational and communicational costs to solve problems on computational grids.
Thirdly, the author proposes a heuristic transformation algorithm for structuring a set of grid resources in arbitrary topology to form a virtual mesh. The transformation of the virtual mesh enables efficient mapping as well as multiple parallel mesh-structured computations using a given set of grid resources.
Finally, the techniques associated with the three research topics presented in this dissertation provide a solid base from which to construct a problem-solving environment on computational grids.
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