摘要
网格资源分配属于NP-难问题,为了更好地解决该问题,首先建立一种性能QoS优化的作业级网格任务调度模型和目标函数,并对资源和任务数进行了分析.提出了基于动态信誉度的改进蚁群算法RACO(reputation-based ACO)进行网格任务调度,RACO引入空间效率和时间效率的动态调节因子,同时采用局部和全局信息素更新策略.仿真实验表明,RACO在资源利用率、动态均衡方面优于Min-min,Max-min和ACO算法.
Resource allocation in grid is an NP-hard problem.To optimize the grid system,a performance QoS optimization model is developed for grid task scheduling and objective function,with the number of resources and tasks analyzed in detail.Then,an improved ant colony algorithm named RACO(reputation-based ant colony algorithm) is presented to schedule tasks in grid,based on the dynamic reputation.Introducing a dynamic scheduling factor involving both space and time efficiencies,a local and global pheromone updating strategy is applied to RACO.Simulation results showed that RACO algorithm outperforms the conventional Min-min,Max-min and ACO in resource utilization rate and dynamic equilibrium.
出处
《东北大学学报(自然科学版)》
EI
CAS
CSCD
北大核心
2010年第5期630-633,共4页
Journal of Northeastern University(Natural Science)
基金
国家自然科学基金资助项目(60673159
70671020)
国家高技术研究发展计划项目(2007AA041201)
教育部科学技术研究发展计划项目(108040)
高等学校博士学科点专项科研基金资助项目(20060145012
20070145017)
关键词
网格计算
任务调度
动态均衡
蚁群算法
信誉
grid compution
task scheduling
dynamic equilibrium
ant colony algorithm
reputation