Decreasing End-to-End Job Execution Times by Increasing Resource Utilization using Predictive Scheduling in the Grid
Ioan Raicu · 2005
The Grid has the potential to grow significantly over the course of the next decade and therefore the mechanisms that make the Grid possible need to become more efficient in order for the Grid to scale. One of these mechanisms revolves around resource management; ultimately, there will be so many resources in the Grid, that if they are not managed properly, only a very small fraction of those resources will be utilized. While good resource utilization is very important, it is also a hard problem due to widely distributed dynamic environments normally found in the Grid. It is important to develop an experimental methodology for automatically characterizing grid software in a manner that allows accurate evaluation of the software’s behavior and performance before deployment in order to make better informed resource management decisions. Many Grid services and software are designed and characterized today largely based on the designer’s intuition and on ad hoc experimentation; having the capability to automatically map complex, multi-dimensional requirements and performance data among resource providers and consumers is a necessary step to ensure consistent good resource utilization in the Grid. This automatic matching between the software characterization and a set of raw or logical resources is a much needed functionality that is currently lacking in today’s Grid resource management infrastructure. Ultimately, my proposed work, which addresses performance modeling with the goal to improve resource management, could ensure that the efficiency of