文件名称:A-GENETIC-ALGORITHM-APPROACH-FOR-UTILITY-MANAGEME
介绍说明－－下载内容均来自于网络，请自行研究使用
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.
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.
(系统自动生成,下载前可以参看下载内容)
下载文件列表
A GENETIC ALGORITHM APPROACH FOR UTILITY MANAGEMENT SYSTEM WORKFLOW SCHEDULING.pdf