How to manage a dynamic environment and how to provide task partitioning are two key concerns when developing distributed computing applications. The emergence of Grid computing environments extends these problems. Conventional resource management systems are based on a relatively static resource model and a centralized scheduler that assigns computing resources to users. Distributed management introduces resource heterogeneity: not only the set of available resources, but even the set of resource types is constantly changing. Obviously this is unsuitable for the present Grid. In addition, the Grid provides users with the physical infrastructure to run parallel programs. Because of this increasing availability, there are more requirements for parallelization technologies. Therefore, based on problems outlined above, this thesis provides a novel scheduler which not only enables dynamic management but also provides skeleton library to support the task partition. Dynamic management is derived from the concept of reflectiveness, which allows the Grid to perform like an efficient market with some limited government controls. To supplement the reflective mechanism, this thesis integrates a statistical forecasting approach to predict the environment of the Grid in the next period. The task partitioning support is extended from the skeleton library in the parallel computing and cluster computing areas. The thesis shows how this idea can be applied in the Grid environment to simplify the user’s programming works. Later in this PhD thesis, a Petri-net based simulation methodology is introduced to examine the performance of the reflective scheduler. Moreover, a real testing environment is set up by using a reflective scheduler to run a geometry optimization application. In summary, by combining knowledge from economics, statistics, mathematics and computer science, this newly invented scheduler not only provides a convenient and efficient way to parallelize users’ tasks, but also significantly improves the performance of the Grid.

How to manage a dynamic environment and how to provide task partitioning are two key concerns when developing distributed computing applications. The emergence of Grid computing environments extends these problems. Conventional resource management systems are based on a relatively static resource model and a centralized scheduler that assigns computing resources to users. Distributed management introduces resource heterogeneity: not only the set of available resources, but even the set of resource types is constantly changing. Obviously this is unsuitable for the present Grid. In addition, the Grid provides users with the physical infrastructure to run parallel programs. Because of this increasing availability, there are more requirements for parallelization technologies. Therefore, based on problems outlined above, this thesis provides a novel scheduler which not only enables dynamic management but also provides skeleton library to support the task partition. Dynamic management is derived from the concept of reflectiveness, which allows the Grid to perform like an efficient market with some limited government controls. To supplement the reflective mechanism, this thesis integrates a statistical forecasting approach to predict the environment of the Grid in the next period. The task partitioning support is extended from the skeleton library in the parallel computing and cluster computing areas. The thesis shows how this idea can be applied in the Grid environment to simplify the user’s programming works. Later in this PhD thesis, a Petri-net based simulation methodology is introduced to examine the performance of the reflective scheduler. Moreover, a real testing environment is set up by using a reflective scheduler to run a geometry optimization application. In summary, by combining knowledge from economics, statistics, mathematics and computer science, this newly invented scheduler not only provides a convenient and efficient way to parallelize users’ tasks, but also significantly improves the performance of the Grid.