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As more and more grid applications are put into use, the performance information plays a more important role in evaluating the running status of the grid system. However, most existing monitoring tools only provide system-level performance data, which are often massive in storage and meanwhile contain either duplicate or missing, even erroneous raw data. In order to make the collected data more easily to understand and thus reusable, in this paper we discuss grid workload modeling approaches to better serve the grid environment. We extract three key workload objects from numerous monitoring entities as the backbone of the workload and put them into use to examine the completeness of a performance trace. We present a methodology for accessing and interpreting grid workload information. A grid workload, which is recorded as a structured collection of monitoring information that is kept in repository continuously updated as a reflection of the execution, is studied at three hierarchical levels. From a goal-oriented perspective, this systematic approach could help use the workload data to guide and improve the resource mappings and fine tune the application and system, opening the way towards an intelligent grid environment.