计算机科学 ›› 2021, Vol. 48 ›› Issue (11): 199-207.doi: 10.11896/jsjkx.200900009
刘漳辉1,2, 赵旭1,2, 林兵2,3, 陈星1,2
LIU Zhang-hui1,2, ZHAO Xu1,2, LIN Bing2,3, CHEN Xing1,2
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。
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