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

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.