摘要
在研究现有云环境下独立任务和工作流任务调度模型的基础上,提出一种满足Qo S约束的部分相关任务调度模型,并改进蚁群算法为每个子群选择信息素更新方法,通过小范围局部优化从而获得整体最优解。Cloud Sim仿真结果表明,该调度模型具有较高的收敛性和寻优能力,适用于云环境下任务调度。
Based on the study of independent tasks and workflow scheduling model in existing cloud environment, a partial dependence task scheduling model with QoS constraints is proposed in this paper. At the same time, ant colony algorithm is improved to select pheromone update method for each sub-group and small-scale local optimization is conducted to obtain the overall optimal solution. CloudSim simulation result shows that the scheduling model has higher convergence and optimization capability for the task scheduling in cloud environment.
出处
《扬州职业大学学报》
2014年第4期34-37,共4页
Journal of Yangzhou Polytechnic College
关键词
蚁群算法
任务调度
QOS
云计算
ant colony algorithm
task scheduling
QoS （quality of service）
cloud computing
