云环境下基于Bayesian主观信任模型的动态级调度算法  

A dynamic level scheduling algorithm based on a Bayesian subjective trust model for cloud computing

作　　者：齐平[1] 王福成[1] 朱桂宏[1] 

机构地区：[1]铜陵学院数学与计算机学院,安徽铜陵244000

出　　处：《计算机工程与科学》2015年第11期2068-2077,共10页Computer Engineering & Science

基　　金：国家自然科学青年基金资助项目(61402005)

摘　　要：针对云环境下存在的信任问题,提出了一种基于Bayesian方法的主观信任模型,用于量化和评估节点的可信程度。该模型给出了信任传递与合成的数学表述和实现方法,同时考虑云资源节点具有动态性、异构性、欺骗性等特征,引入了惩罚机制和分级剪枝过滤机制。最后将该模型应用于DLS算法得到基于Bayesian主观信任模型的动态级调度算法(BST-DLS)。分析及仿真实验结果表明,提出的BSTDLS算法能够以较小的调度长度为代价,有效地提高云环境下任务执行的成功率。Aiming at the trust problem existing in cloud computing environment, we first propose a subjective trust model based on the Bayesian method to quantify and evaluate the trustworthiness of computing nodes, and demonstrate its mathematical description and implementation. Duo to the charac- teristics of dynamic, heterogeneity and deception, resource nodes are inevitably unreliable in cloud envi- ronments. So we also introduce a punishment mechanism and a pruning-filtering mechanism. We finally propose a dynamic level scheduling algorithm based on a Bayesian subjective trust model named BST- DLS by integrating the existing DLS algorithm. Theoretical analyses and simulation experimental results prove that the BST-DLS algorithm can efficiently improve the ratio of successful execution at the cost of sacrificing fewer schedule length.

关 键 词：云计算 Bayesian估计 可信度 推荐信任 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]