Abstract. In this paper, we present a scheduler for distributing workflows in Utility Management System (UMS).

The system executes a large number of workflows, which have very high resource requirements. The workflows have

different computational requirements and thus the optimization of resource utilization must be performed in a way that

is different from the standard approach of scheduling workflows. We developed a strategy for allocating workflows,

which is based on a genetic algorithm. The proposed architecture executes a scheduling algorithm by using a feedback

from the execution monitor. We also report on an experimental study, which shows that a significant improvement of

overall execution time can be achieved by using the genetic algorithm. The algorithm is used for designing effective

Grid schedulers that optimize makespan. The study further shows that the overall system (UMS) performance is

significantly improved this finding indicates that there can be reduction in hardware investment.-Abstract. In this paper, we present a scheduler for distributing workflows in Utility Management System (UMS).

The system executes a large number of workflows, which have very high resource requirements. The workflows have

different computational requirements and thus the optimization of resource utilization must be performed in a way that

is different from the standard approach of scheduling workflows. We developed a strategy for allocating workflows,

which is based on a genetic algorithm. The proposed architecture executes a scheduling algorithm by using a feedback

from the execution monitor. We also report on an experimental study, which shows that a significant improvement of

overall execution time can be achieved by using the genetic algorithm. The algorithm is used for designing effective

Grid schedulers that optimize makespan. The study further shows that the overall system (UMS) performance is

significantly improved　this finding indicates that there can be reduction in hardware investment.