华南理工大学学报(自然科学版) ›› 2008, Vol. 36 ›› Issue (4): 69-74.

• 计算机科学与技术 • 上一篇    下一篇

基于穿越路径和分代调整的分代调度算法优化

郑伟平 齐德昱   

  1. 华南理工大学 计算机科学与工程学院, 广东 广州 510640
  • 收稿日期:2007-05-24 修回日期:2007-07-18 出版日期:2008-04-25 发布日期:2008-04-25
  • 通信作者: 郑伟平(1979-),男,博士生,华南师范大学工程师,主要从事分布式系统研究. E-mail:newmood@tom.com
  • 作者简介:郑伟平(1979-),男,博士生,华南师范大学工程师,主要从事分布式系统研究.
  • 基金资助:

    粤港关键领域重点突破项目(2005A10307007)

Optimization of Generational Scheduling Algorithm Based on Through Path and Generation Adjustment

Zheng Wei-ping  Qi De-yu   

  1. School of Computer Science and Engineering, South China University of Technology, Guangzhou 510640, Guangdong, China
  • Received:2007-05-24 Revised:2007-07-18 Online:2008-04-25 Published:2008-04-25
  • Contact: 郑伟平(1979-),男,博士生,华南师范大学工程师,主要从事分布式系统研究. E-mail:newmood@tom.com
  • About author:郑伟平(1979-),男,博士生,华南师范大学工程师,主要从事分布式系统研究.
  • Supported by:

    粤港关键领域重点突破项目(2005A10307007)

摘要: 分代调度(GS)算法在分解任务图时只考虑任务间的偏序约束关系,没有考虑任务划分可能对整体调度时间的影响.其局部调度使用已有的独立调度算法,仅考虑任务子集的完成时间,缺乏全局优化能力.为此,文中提出一种改进的基于GS的GA-DLTPF算法.该算法在GS分代的基础上进行分代调整,并基于“穿越路径”的概念实现了最长穿越路径优先的局部调度策略.仿真实验表明,GA-DLTPF算法的调度性能优于GS的改进算法——OGS,而且任务图深度越大,GA-DLTPF算法的优势越明显.

关键词: 调度算法, 依赖任务, 分代调度, 网格, 优化

Abstract:

In the generational scheduling (GS) algorithm, only the partial orders between tasks are considered during the generation division, and the effect of the task partition on the total scheduling time is entirely ignored. Besides, as the existing independent scheduling algorithms employed by the local scheduling consider the makespan of task subsets only, there may result in the absence of global optimization. In order to solve these problems, a modified algorithm named GA-DLTPF is presented based on GS. GA-DLTPF performs the generation adjustment based on the generation division of GS, and ensures the priority of the longest through path for the local scheduling according to the definition of through path. Simulated results indicate that GA-DLTPF is of better scheduling performance than the improved GS algorithm OGS, especially in the condition of deep task graph.

Key words: scheduling algorithm, dependent task, generational scheduling, grid, optimization