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A rough set-based hypergraph trust measure parameter selection technique for cloud service selection

机译:用于云服务选择的基于粗糙集的超图信任度量参数选择技术

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Selection of trustworthy cloud services has been a major research challenge in cloud computing, due to the proliferation of numerous cloud service providers (CSPs) along every dimension of computing. This scenario makes it hard for the cloud users to identify an appropriate CSP based on their unique quality of service (QoS) requirements. A generic solution to the problem of cloud service selection can be formulated in terms of trust assessment. However, the accuracy of the trust value depends on the optimality of the service-specific trust measure parameters (TMPs) subset. This paper presents TrustCom-a novel trust assessment framework and rough set-based hypergraph technique (RSHT) for the identification of the optimal TMP subset. Experiments using Cloud Armor and synthetic trust feedback datasets show the prominence of RSHT over the existing feature selection techniques. The performance of RSHT was analyzed using Weka tool and hypergraph-based computational model with respect to the reduct size, time complexity and service ranking.
机译:由于在计算的各个维度上大量的云服务提供商(CSP)激增,因此选择可信赖的云服务一直是云计算中的主要研究挑战。这种情况使云用户很难根据他们独特的服务质量(QoS)要求来确定合适的CSP。可以根据信任评估来制定云服务选择问题的通用解决方案。但是,信任值的准确性取决于特定于服务的信任度量参数(TMP)子集的最优性。本文提出了TrustCom-一种新颖的信任评估框架和基于粗糙集的超图技术(RSHT),用于识别最佳TMP子集。使用Cloud Armor和综合信任反馈数据集进行的实验表明,RSHT在现有特征选择技术上的突出优势。使用Weka工具和基于超图的计算模型对RSHT的性能进行了分析,涉及还原量,时间复杂度和服务等级。

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