A probabilistic trust model for cloud services using Bayesian networks

被引:0
|
作者
Mihan Hosseinnezhad
Mohammad Abdollahi Azgomi
Mohammad Reza Ebrahimi Dishabi
机构
[1] Miyaneh Branch,Department of Computer Engineering
[2] Islamic Azad University,undefined
来源
Soft Computing | 2024年 / 28卷
关键词
Cloud computing; Cloud services; Trust; Bayesian network; Quality of service;
D O I
暂无
中图分类号
学科分类号
摘要
With the industry’s fast adoption of cloud computing, it’s been hard to keep trust between cloud service providers and people who use their services. In fact, trust management in cloud computing has become very challenging given the urgent need for cloud service requesters to choose efficient, trustworthy, and non-risky services. One of the most important factors that can be considered in the applicant’s trust or distrust of a service is the different quality of services related to the service. Therefore, approaches are needed to assess the trustworthiness of cloud services with respect to their quality of service (QoS). Given the uncertainty that exists for cloud services, it is more realistic to model their QoS parameters as random variables and also consider different dependencies between them. In this paper, a new trust model for cloud services is proposed using a Bayesian network, which is a probabilistic graphical model that can be used as one of the best methods to control uncertainty. Using Bayesian networks makes it possible to infer more accurate QoS values, which leads to the selection of highly trustworthy services by several cloud service requesters. To evaluate the proposed trust model, experimental results are performed using a real-world cloud service quality feedback, collected by the CloudArmor\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$CloudArmor$$\end{document} project. The results of the experiments show that the proposed trust model is highly accurate and significantly reduces the estimation error.
引用
收藏
页码:509 / 526
页数:17
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