云计算环境下实体的多属性高效率评估策略设计  

Multi-attribute high-efficiency assessment strategies design of entity in cloud computing environment

作　　者：戴伟[1] 胡鹏[2] 

机构地区：[1]湖北理工学院经济与管理学院,湖北黄石435003 [2]湖北理工学院数理学院,湖北黄石435003

出　　处：《计算机应用研究》2016年第6期1708-1712,共5页Application Research of Computers

基　　金：国家教育部人文社会科学研究青年基金资助项目(13YJCZH028);湖北理工学院青年项目(13xjz07Q)

摘　　要：为了解决当前云计算的实体信任评估领域所实施的方法策略无法满足云计算环境的动态模糊性的问题,设计了一种多属性信任关系的动态评估模型。该方法以遗传自适应学习算法为理论基础,在云计算环境下的信任管理过程中,针对动态评估高效率的需求,其既可评估分析信任管理过程实体的多属性问题,同时可以在持续的评估过程中自主学习,使其评估分析的执行能力逐渐提升,最终能够满足上文中阐明的高效性与动态性的要求。仿真结果显示,所设计的模型在直接面对实时变化的云计算网络环境实体参数时,可以自适应地调整评估策略规则,最终克服了云计算环境的动态模糊性,对实体的多个属性进行了综合评估。In order to solve the problem which the current methods and strategies implemented in the field of cloud computing entity trust evaluation could not meet the dynamic fuzziness,this paper designed a dynamic evaluation model of multi-attribute trust relationships. The method was based on the genetic adaptive learning algorithm theory. For the dynamic assessment high efficiency needs in the trust management process adaptive learning of cloud computing environments,it could evaluate the entity multi-attribute problems of the trust management process,while independent learning in the ongoing evaluation process,so improved implementation capacity assessment analyzes gradually and eventually met the efficiency and the dynamic nature requirements of set forth above. The simulation results show that while the designed model face to the real-time entity parameters changed in cloud computing network environment directly,it can adjust its assessment of policy rules adaptively and ultimately overcomes the dynamic cloud computing environments fuzziness. It can comprehensively assess multiple properties entities.

关 键 词：云计算 实体 动态模糊性 遗传自适应学习 信任管理 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]