基于自我意识的云计算程序智能化研究

Han-Bin Liu, Wuqi Gao, Junmin Luo
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引用次数: 0

摘要

通过对云计算MapReduce编程框架的研究,目前的MapReduce程序只解决具体问题,没有MapReduce程序的设计经验或设计特征总结,更没有知识库的形式化描述和经验继承与应用。为了解决智能云计算程序的问题,设计了一种通用的MapReduce程序生成方法。本文通过研究AORBCO模型,结合云计算技术,提出了智能云计算的体系结构。根据AORBCO模型中的行为控制机制,提出了一种智能云计算中MapReduce的程序生成方法。该方法提取输入数据集中的实体信息和智能云计算知识库中的实体信息进行相似度计算,提取智能云计算判断数据集中最高顺序的实体作为关键-键-值对信息。对数据处理类型进行划分,并针对具体的MapReduce能力进行对齐,在AORBCO模型开发平台上验证MapReduce程序生成实验。实验表明,大数据MapReduce程序代码的复杂性得到了简化,生成的代码执行效率良好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Intelligentization of Cloud Computing Programs Based on Self-awareness
Abstract Through the research of MapReduce programming framework of cloud computing, the current MapReduce program only solves specific problems, and there is no design experience or design feature summary of MapReduce program, let alone formal description and experience inheritance and application of knowledge base. In order to solve the problem of intelligent cloud computing program, a general MapReduce program generation method is designed. This paper proposes the architecture of intelligent cloud computing by studying AORBCO model and combining cloud computing technology. According to the behavior control mechanism in AORBCO model, a program generation method of MapReduce in intelligent cloud computing is proposed. This method will extract entity information in input data set and entity information in knowledge base in intelligent cloud computing for similarity calculation, and extract the entity in the top order as key key-value pair information in intelligent cloud computing judgment data set. The data processing types are divided, and then aligned with each specific MapReduce capability, and the MapReduce program generation experiment is verified in the AORBCO model development platform. The experiment shows that the complexity of big data MapReduce program code is simplified, and the generated code execution efficiency is good.
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