计算密集型与数据密集型混合网格作业调度算法

针对计算密集型作业与数据密集型作业混合情况，在一个作业有时间限制的动态环境中，对传统的网格作业调度方法进行扩展，提出了三种网格作业调度启发式算法:Emin min、Ebest、Esufferage。并在一个由多个Cluster组成的、通过高速网络连接的网格模型上，对三种算法进行验证。与Min min算法的比较结果显示：三种算法均优于Min min算法。与ASJS算法比较结果显示：Emin min减少了等待时间与作业的makespan; Esufferage算法以减少作业完成量为代价，减少了作业的等待时间及makespan; Ebest在完成作业数量上与ASJS基本保持一致，但却增加了作业的等待时间与makespan。总体上，Emin min具有比较大的优势。

Three grid job scheduling methods heuristics for data-intensive and computing-intensive jobs

Most of the existing grid job scheduling methods focus on either data intensive jobs or computing intensive jobs.In the dynamic environment where every job has its deadline,we extend the traditional grid job scheduling methods to propose three new grid job scheduling methods:Emin min,Ebest and Esufferage.The three methods are validated on the Grid model with clusters that are connected by the high speed network.Simulation results demonstrate that our proposed methods are better than Min min.The comparison between the three methods and ASJS shows that,Emin min reduces the waiting time and makespan,Esufferage reduces the waiting time and the makespan greatly with the sacrifice of some jobs,and Ebest gives the same performance in unfinished jobs but has a larger value in waiting time and makespan than ASJS. In general,Eminmin has a better performance than Min min and ASJS.