一种基于动态Skyline和遗传粒子群优化的云服务组合方法

发布时间：2018-10-16 22:23

【摘要】：提出一种改进的基于动态Skyline和多种群遗传粒子群优化的云服务组合优化方法,旨在解决动态、不确定环境下大规模云服务组合优化问题.对云服务组合和服务质量(QoS)形式化描述,提出一种云服务组合优化模型;对Skyline操作进行建模的基础上,设计Skyline云服务动态更新算法,以满足云服务因临时加入、退出及QoS变化而引起的动态性和不确定性需求;最后,设计一种新的云服务组合优化算法,算法采用动态Skyline操作和用户约束降低问题求解空间,并基于种群相似性和遗传操作进行防早熟收敛处理.通过真实数据集和随机数据集的大量仿真实验,结果验证了本文算法的可行性和有效性.
[Abstract]:An improved cloud service composition optimization method based on dynamic Skyline and multi-swarm genetic particle swarm optimization is proposed to solve the problem of large-scale cloud service composition optimization in dynamic and uncertain environments. Based on the formal description of cloud service composition and quality of service (QoS), an optimization model of cloud service composition is proposed, and a dynamic updating algorithm of Skyline cloud service is designed on the basis of modeling Skyline operation to satisfy the temporary addition of cloud service. Finally, a new cloud service composition optimization algorithm is designed, which uses dynamic Skyline operation and user constraint reduction problem to solve the problem. And based on population similarity and genetic operation to prevent premature convergence. A large number of simulation experiments on real data sets and random data sets show that the proposed algorithm is feasible and effective.
【作者单位】：安徽大学信息保障技术协同创新中心;
【基金】：国家科技支撑计划项目(2015BAK24B01)资助教育部人文社会科学规划基金项目(15YJAZH112)资助安徽省自然科学基金项目(1408085MF132)资助安徽省科技攻关项目(1301032162)资助
【分类号】：TP393.09

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