中文摘要：

为提高云计算系统的资源利用率,优化系统性能,同时兼顾用户的服务质量（Qo S）需求约束,文中结合云计算和工作流建立了云工作流系统,给出了具有两个调度阶段的系统资源调度模型.在第1阶段中,考虑了Qo S的时间及价格约束、工作流内各个任务之间的依赖关系以及各个任务所产生的中间数据的处理,提出了改进的粒子群优化（MPSO）算法,并利用Pareto获得最优解,以提高调度效率;在第2阶段中,考虑了资源在主机上的分配情况,提出了具有负载感知的调度策略,根据系统的负载情况进行资源调度,以提高系统的资源利用率.实验结果表明：在云工作流系统的资源优化调度中,与经典的异构最早完成时间算法、单目标优化的遗传算法相比,MPSO算法的任务执行速度更快、资源利用率更高,能满足用户的Qo S需求;具有负载感知的调度策略能更有效地根据负载情况进行调度,提高任务执行的效率和资源利用率.

英文摘要：

In order to improve the resource uti lization of cloud computing systems and optimize the system perfor-mance ,by taking into account the users^ QoS demand, a workflow cloud computing system is established by integra-ting cloud computing with workflow, and a two-stage resource scheduling model is constructed for the cloud compu-ting workflow system. In the first stage, by considering the time and cost constraints of QoS, the dependencies among the tasks in the workflow, and the processing of the intermediate data from each task, a modified particle swarm optimization algorithm （MPSO） is proposed, and the Pareto is used to obtain an optimal solution so as to im-prove scheduling efficiency. In the second stage, by considering the resource allocation on hosts, a scheduling stra-tegy with load-aware is proposed to perform resource scheduling according to system loads, so as to improve the re-source utilization of the system. Experimental results show that, in the resource scheduling process of the workflow cloud computing system, the modified MPSO algorithm is superior to the earliest heterogeneous finish-time algorithm and single-objective optimization genetic algorithm in terms of execution speed, resource utilization and users7 satis-faction with QoS, and that, the proposed scheduling strategy with load-aware can schedule more efficiently accor-ding to the system loads, and the task execution efficiency and the resource utilizationare are thus improved.