基于云平台的在线服务性产品任务流分配设计与研究

Research on online service product task flow assignment based on cloud platform

针对云计算环境下满足用户服务质量(QoS)约束条件的在线服务性产品任务流分配问题,提出一种基于QoS约束的差分进化算法(QoS-DE算法),以便实现多目标优化全局最优问题。该算法首先构建了云计算环境下的QoS模型,并对在线服务性产品的工作流分配约束指标进行了分析。然后利用差分进化算法实现约束条件下的计算资源多目标优化模型求解,并通过自适应的惯性权重调节,提高了全局优化能力。CloudSim云仿真平台上的测试结果表明,相比经典Min-Min算法和QoS-GA算法,提出的QoS-DE算法能够将任务合理分配到对应的节点,并在执行时间、执行费用等指标方面上表现出更好的性能。

In order to solve the problem of task flow assignment of online service products that meet the user′s constraint conditions for quality of service (QoS) in cloud computing environment, a differential evolution algorithm based on QoS constraints(QoS.DE algorithm)is proposed,so as to achieve multi-objective global optimization. The QoS model in the cloud computing environment is constructed for the algorithm. The workflow allocation constraint indicators of online service products are analyzed. The differential evolution algorithm is used to solve the multi-objective optimization model of computational resources under constraint conditions,and the global optimization ability is improved by adaptive inertia weight adjustment. The test results on the CloudSim cloud simulation platform show that,in comparison with the classical Min.Min algorithm and QoS-GA algorithm,the proposed QoS-DE algorithm can reasonably assign tasks to the corresponding nodes,and has better performance in the aspects of execution time and cost indicators.