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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