Cloud-DLS: Dynamic trusted scheduling for Cloud computing

Clouds are rapidly becoming an important platform for scientific applications. In the Cloud environment with uncountable numeric nodes, resource is inevitably unreliable, which has a great effect on task execution and scheduling. In this paper, inspired by Bayesian cognitive model and referring to the trust relationship models of sociology, we first propose a novel Bayesian method based cognitive trust model, and then we proposed a trust dynamic level scheduling algorithm named Cloud-DLS by integrating the existing DLS algorithm. Moreover, a benchmark is structured to span a range of Cloud computing characteristics for evaluation of the proposed method. Theoretical analysis and simulations prove that the Cloud-DLS algorithm can efficiently meet the requirement of Cloud computing workloads in trust, sacrificing fewer time costs, and assuring the execution of tasks in a security way.