混合云环境面向安全科学工作流数据布局策略
Security-oriedted data placement strategy of workflows in hybrid cloud environment
苏明辉 1林兵 2卢宇 3王素云3
作者信息
- 1. 福建师范大学物理与能源学院,福建福州 350117
- 2. 福建师范大学物理与能源学院,福建福州 350117;北京大学信息科学技术学院,北京 100871
- 3. 福建师范大学协和学院,福建福州 350117
- 折叠
摘要
为解决混合云环境下科学工作流数据布局问题,在考虑数据的安全需求的前提下,以优化跨数据中心传输时延为目标,提出了一种混合云环境下面向安全的科学工作流布局策略.分析数据集的安全需求以及数据中心所能提供的安全服务,提出安全等级分级规则;设计并提出基于遗传算法和模拟退火算法的自适应粒子群优化算法(adaptive particle swarm optimization algorithm based on SA and GA,SAGA-PSO),避免算法陷入局部极值,有效提高种群多样性;与其它经典布局算法对比,基于SAGA-PSO的数据布局策略在满足数据安全需求的同时能够大大降低传输时延.
Abstract
To address the issue of data layout in scientific workflows in a hybrid cloud environment,while considering the security requirements of the data,a security-oriented optimization strategy was proposed for scientific workflow layout with the goal of optimizing the cross-data center transmission delay.The security requirements of the dataset and the security services that could be provided by the data center were analyzed,and a security classification rule was proposed.An adaptive particle swarm optimi-zation algorithm based on genetic algorithm and simulated annealing algorithm(SAGA-PSO)was designed and proposed to avoid the algorithm from getting stuck in local optima and effectively improve population diversity.Compared with other classic layout algorithms,the data layout strategy based on SAGA-PSO can significantly reduce transmission delay while meeting the data security requirements.
关键词
混合云/科学工作流/数据布局/安全分级/时延优化/遗传粒子群优化算法/模拟退火Key words
hybrid cloud/scientific workflow/data placement/security classification/delay optimization/genetic particle swarm optimization algorithm/simulated annealing引用本文复制引用
基金项目
国家重点研发计划基金项目(2018YFB1004800)
国家自然科学基金项目(62072108)
国家自然科学基金项目(61672159)
福建省高校产学合作基金项目(2022H6024)
福建省自然科学基金项目(2019J01244)
出版年
2024
NETL
NSTL
万方数据