Grid harvest service: A performance system of grid computing |
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| Affiliation: | 1. Faculty of Natural and Applied Sciences, Notre Dame University, Deir el Qamar, Lebanon;2. LARlFA-EDST Laboratory, Faculty of Sciences, Lebanese University, Fanar, Lebanon;1. Arizona State University, 660 S. College Avenue, Tempe, AZ 85281, United States;2. Civil, Construction and Environmental Engineering, 418 Mann Hall, North Carolina State University, Raleigh, NC 27695, United States;1. Stony Brook University, Stony Brook, NY 11794-2424, USA;2. Center for Computing Research, Sandia National Laboratories, Albuquerque, NM 87185, USA;3. Washington University in St. Louis, St. Louis, MO 63130, USA;1. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China;2. Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom;3. Department of Automation, Tsinghua University, Beijing 100084, China |
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| Abstract: | Conventional performance evaluation mechanisms focus on dedicated systems. Grid computing infrastructure, on the other hand, is a shared collaborative environment constructed on virtual organizations. Each organization has its own resource management policy and usage pattern. The non-dedicated characteristic of Grid computing prevents the leverage of conventional performance evaluation systems. In this study, we introduce the grid harvest service (GHS) performance evaluation and task scheduling system for solving large-scale applications in a shared environment. GHS is based on a novel performance prediction model and a set of task scheduling algorithms. GHS supports three classes of task scheduling, single task, parallel processing and meta-task. Experimental results show that GHS provides a satisfactory solution for performance prediction and task scheduling of large applications and has a real potential. |
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