Abstract:
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.
