混合云环境下基于模糊理论的科学工作流数据布局策略  

Data Placement Strategy of Scientific Workflow Based on Fuzzy Theory in Hybrid Cloud

作　　者：刘漳辉[1,2] 赵旭 林兵 陈星 LIU Zhang-hui;ZHAO Xu;LIN Bing;CHEN Xing(College of Mathematics and Computer Science,Fuzhou University,Fuzhou 350116,China;Fujian Key Laboratory of Network Computing and Intelligent Information Processing,Fuzhou 350116,China;College of Physics and Energy,Fujian Normal University,Fuzhou 350117,China)

机构地区：[1]福州大学数学与计算机科学学院,福州350116 [2]福建省网络计算与智能信息处理重点实验室,福州350116 [3]福建师范大学物理与能源学院,福州350117

出　　处：《计算机科学》2021年第11期199-207,共9页Computer Science

基　　金：国家重点研发计划(2018YFB1004800);福建省引导性项目(2018H0017)。

摘　　要：混合云环境下,合理的数据布局策略对科学工作流的高效执行至关重要。传统的科学工作流数据布局策略主要基于确定性环境,而在实际网络环境中,由于不同数据中心之间的负载不同、带宽波动和网络拥塞等原因以及计算机自身的特性,数据传输时间存在不确定性。为了解决该问题,基于模糊理论,以最小化数据模糊传输时间为目标,提出了一种基于遗传算法算子的模糊自适应离散粒子群优化算法(Fuzzy Adaptive Discrete Particle Swarm Optimization Algorithm Based on Genetic Algorithm Operators,FGA-DPSO),对科学工作流数据进行合理布局,同时满足数据集的隐私要求和数据中心的容量限制。实验结果表明,该算法能够有效地减少混合云环境下科学工作流的数据模糊传输时间。A reasonable data placement strategy is essential to the efficient execution of scientific workflow in hybrid cloud environment.The traditional data placement strategy mainly focuses on the deterministic environment,but the data transmission time is uncertain due to the different load,bandwidth fluctuation and network congestion between different data centers and computer characteristics in the actual network environment.To solve this problem,a fuzzy adaptive discrete particle swarm optimization algorithm based on the fuzzy theory and genetic algorithm operator(FGA-DPSO)is proposed to minimize the fuzzy transmission time of data,place the scientific workflow data reasonably and meet the privacy requirements of the data set and the capacity limit of the data center.The experimental results show that the algorithm can effectively reduce the fuzzy data transmission time of scientific workflow in hybrid cloud environment.

关 键 词：混合云 科学工作流 数据布局 模糊理论 时间优化 

分 类 号：TP338[自动化与计算机技术—计算机系统结构]