混合云中面向多目标的工作流数据放置策略  

Multi-objective oriented data placement strategy for workflows in hybrid cloud

作　　者：林兵 汪昕隆 苏明辉 郑裕恒 卢宇[4] LIN Bing;WANG Xinlong;SU Minghui;ZHENG Yuheng;LU Yu(College of Physics and Energy,Fujian Normal University,Fuzhou 350117,China;School of Electronics Engineering and Computer Science,Peking University,Beijing 100871,China;Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing,Fuzhou 350108,China;Concord University College,Fujian Normal University,Fuzhou 350117,China)

机构地区：[1]福建师范大学物理与能源学院,福建福州350117 [2]北京大学信息科学技术学院,北京100871 [3]福建省网络计算与智能信息处理重点实验室,福建福州350108 [4]福建师范大学协和学院,福建福州350117

出　　处：《计算机集成制造系统》2025年第1期219-234,共16页Computer Integrated Manufacturing Systems

基　　金：国家自然科学基金资助项目(62072108);福建省高校产学合作资助项目(2022H6024,2021H6026)。

摘　　要：针对混合云环境下工业软件工作流的数据放置问题,如何在保证数据安全的前提下平衡用户和服务提供商的利益,综合考虑数据的传输时延,工业软件工作流执行代价以及数据中心间的负载是一个重要的挑战。为此,提出一种安全等级分级机制,并设计出一种基于改进的多目标优化进化算法(IO-MOEA)的数据放置策略。该策略在传统非支配排序遗传算法(NSGA-II)中对选择算子进行自适应改进,提高了算法的收敛性和种群的多样性,之后结合熵权法和理想解相似性排序偏好技术(TOPSIS)法,客观评估Pareto最优解集中解的优劣,从而找到最佳方案。实验结果表明,所提算法能够有效降低工业软件工作流传输时间和执行代价,同时兼顾数据中心间的负载均衡。相比于改进前的算法,改进后的IO-MOEA算法在超平面指标上提高了约3%~19%,在空间指标上改善了11%~21%。Data placement for industrial workflows in hybrid cloud environments presents significant challenges,involving the assurance of data security and the balancing of interests between users and service providers,while taking into consideration factors including data transfer latency,workflow execution cost,and load balancing among data centers.To address these challenges,an Improved Optimization Multi-objective Optimization Evolutionary Algorithm(IO-MOEA)based data placement strategy was proposed.This approach improved its convergence and diversity by adaptively enhancing the selection operator within the fast elitist Non-dominated Sorting Genetic Algorithm(NSGA-II).Furthermore,the entropy weight method and the Technique for Ordering Preference by Similarity to Ideal Solution(TOPSIS)method were combined to objectively evaluate the advantages and disadvantages of the solutions in the Pareto optimal set to find the best one.Experimental results showed that the proposed algorithm could effectively reduce the data transfer time and execution cost for industrial workflows,while ensuring the load balancing among data centers.Compared to the original algorithm,the IO-MOEA algorithm improved the hypervolume by about 3%~19%and the space by about 11%~21%.

关 键 词：云计算 工业软件工作流 多目标优化 数据放置 负载均衡 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP311.5[自动化与计算机技术—控制科学与工程]