云计算环境中服务动态选择算法研究  

Research on Service Dynamic Selection Algorithm in Cloud Computing

作　　者：张恒巍[1] 韩继红[1] 寇广[1] 卫波[1] 

机构地区：[1]解放军信息工程大学,郑州450001

出　　处：《计算机科学》2015年第5期251-254,269,共5页Computer Science

基　　金：国家自然科学基金项目(61303074;61309013);国家重点基础研究发展计划("973"计划)基金项目(2012CB315900;河南省科技攻关计划项目(12210231003;13210231002)资助

摘　　要：为解决云计算环境下的服务动态选择问题,设计了综合考虑反应时间和成本的适应度函数,提出了求解服务动态选择问题的分布估计蛙跳算法。在蛙跳算法的基础上,借鉴交叉操作改写蛙跳算法的进化算子,并引入分布估计进化策略改进蛙跳算法的青蛙更新模式,使改进后的新算法具有更全面的学习能力,能够有效避免算法陷入局部最优。仿真实验验证了算法的可行性和有效性,与蛙跳算法和分布估计算法相比,该算法的收敛性能和寻优能力均得到改善,能够更好地解决云计算环境下的服务动态优化选择问题。To solve the service dynamic selection prohlem in cloud computing environment, a fitness function which considers both the response time and the cost was designed, and an estimation of distribution-shuffled frog leaping algorithm was proposed to solve the problem of service dynamic selection. On the basis of leapfrog algorithm, evolutionary operators of leapfrog algorithm was redefined by drawing crossover operation of genetic algorithm, and distribution estimation evolutionary strategy was introduced to improve frog update mode of the leapfrog algorithms, so that the new improved algorithm has a more comprehensive learning ability and it can effectively avoid the local optimum. Simulation results demonstrate the feasibility and effectiveness of the proposed algorithm, and compared with the leapfrog algorithm and estimation of distribution algorithms, the convergence performance and optimization capabilities of the proposed algorithm are improved, and it can better solve the service dynamic selection problem in cloud computing environment.

关 键 词：云计算 服务动态选择 服务质量 进化算子 适应度函数 概率模型 

分 类 号：TP393.7[自动化与计算机技术—计算机应用技术]