云环境下基于Bayesian主观信任模型的动态级调度算法

摘要/Abstract

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BSTDLS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。

关键词: 云计算, Bayesian估计, 可信度, 推荐信任

Abstract:

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 characteristics of dynamic, heterogeneity and deception, resource nodes are inevitably unreliable in cloud environments. 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.

Key words: cloud computing;bayesian estimation;trustworthiness;recommendation trust

齐平，王福成，朱桂宏. 云环境下基于Bayesian主观信任模型的动态级调度算法[J]. J4, 2015, 37(11): 2068-2077.

QI Ping,WANG Fucheng,ZHU Guihong. A dynamic level scheduling algorithm based on a
Bayesian subjective trust model for cloud computing [J]. J4, 2015, 37(11): 2068-2077.

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