中文摘要：

在分布式系统中,云计算作为一种新的服务提供模式出现,其执行科学应用数据流时的优势和缺点得到越来越多的关注,其主要特点为拥有大量同质和并发的任务包,并构成了性能瓶颈的主要因素.在云数据流中调度大规模任务是已被证实的NP难问题.专注于解决优化云数据流中的调度过程,并受现实世界启发,从不同角度将优化目标分别划分为用户指标（完工时间和经济成本）和云系统指标（网络带宽、存储约束和系统公平度）,并将该调度问题制定成为一个新的连续的合作博弈,设计出快速收敛的高效Muliti-Objective Game（MOG）调度算法,在优化用户指标的同时,实现系统指标的约束,并保证云资源的效率和公平度.通过综合实验,证实该方法与其他相关算法相比,在算法复杂度O（l？K？M）（明显改进数量级）、结果质量（一些情况下最佳）、系统级别公平性上具有明显的优越性.

英文摘要：

As a new emerging service provider, cloud computing, exhibiting advantages and disadvantages when executing the scientific data flows, is getting more and more attention. One of the main factors that constitute the performance bottleneck is there are many homogeneous and concurrent task packages in cloud. This paper focuses on optimizing the scheduling process in dataflow and transforming the optimization objectives into user metrics （makespan and economic cost） and indicators of cloud systems （network bandwidth, storage constraints and system fairness）. An efficient multi-objective game algorithm （MOG） is proposed by formulating the optimization problem as a new cooperative game. The MOG method is able to optimize the user metrics while satisfying the constraint of the system metrics and ensuring the efficiency and fairness of the cloud resources. Comprehensive experiments demonstrate thatcompared with other related algorithms, the proposed MOG method has obvious advantages in terms of algorithm complexity O（I.K.M） （improvement of magnitude）, result quality （optimum in some cases） and system level fairness.