中文摘要：

针对云计算中平台主机之间工作负载分布的作业调度问题,提出了一种基于近似ε-约束的优化算法。将作业调度问题建模为一个数学决策模型,求出模型的可行工作调度集,利用ε-约束算法获得每个单目标模型的帕累托前沿,从而优化作业的总平均等待时间、最长工作调度中作业的平均等待时间（如调度跨度）和所需主机数目。实验通过建立实例将算法与传统的加权和（WS）算法进行比较,实验结果显示,算法需要更少的平均等待时间和主机数目,找到的非支配解平均数比WS算法多77.8%,表明算法更具多样化,适合用于解决云计算环境下的大规模多目标作业调度问题。

英文摘要：

For the issue that optimizing job scheduling between the platform host workload distribution in cloud computing,this paper proposed an optimal algorithm based on approximate ε-constraint to solve the homogeneous cloud computing platform multi-objective job scheduling problem. Firstly,it modeled the job scheduling problem as a mathematical decision model.Then,it found the feasible set of job scheduling model. Finally,it used ε-bound algorithm to obtain the Pareto frontier of each single object model,in order to optimize the total average waiting time for a job,the longest job scheduling the average waiting time and the number of jobs required by the host（ eg span scheduling）. It created an instance to compare proposed with traditional weighted sum（ WS） algorithm,the results show that proposed method needs less average waiting time and hosts numbers than WS algorithm,and it is superior to WS method with 77. 8% increasing the number of non-dominated solution. Experimental results indicate that proposed algorithm has more diversity,which is suitable for settling large-scale multi-objective scheduling problem in cloud computing.