Aiming at the problem of uncertain factors in network trust evaluation, under the research background of security trading in complex open network, the extension cloud theory was introduced. Using the matter-element theory of extenics and the uncertainty of cloud model and the advantage of both the quantitative and qualitative analysis, an extension cloud-based network trust evaluation model was proposed. In the proposed model, the transformation between qualitative trust value and quantitative trust value was realized. And then, an evaluation method based on the extension cloud was put forward on the basis of the proposed model. The trust assessment of network security trading can be achieved effectively for providing good basis for final trust decision. The simulation experimental results show that, the trust evaluating and scheduling algorithm has improved the accuracy of trust evaluation and the successful rate of transaction in complex network environment and alleviated the network transaction entity fraud effectively. The proposed network trust extension method is effective and feasible.

The service consumers lack trust in cloud service providers in the interactive process. Aiming at the problem, a trust model of cloud computing based on Service Level Agreement (SLA) was proposed. In the model, when registering to a third-party trust platform which called service center, a cloud service provider must submit its strength evaluation report on its strength, operations, technologies and service attributes, etc. According to the relevant criteria, service center made an evaluation of the cloud service provider and got the system trust. Then, the system trust was combined to traditional reputation. Thus, direct trust, indirect trust and system trust were made as three important factors for evaluating a cloud service provider, and the last trust value was calculated. Finally, the service consumer could make the SLA negotiation with the service provider according to the service and the last trust value, was used to determine the selection from multi-service providers. Thus, the dishonest or less reputable cloud service providers were excluded. The experimental results show that the last trust value is obtained more comprehensively and accurately due to the introduction of system trust and service consumer can select the cloud service provider with high credibility, which can effectively prevent the dishonest behaviors of cloud service providers and improve the success rate of interaction in the proposed trust model.

In the research and application of multi-cross channel model, to maximize fault recovery of individual channel is the basis of the correctness to vote. There is some time redundancy in a task period. For a task processing in a given step, to summarize the time redundancy of pre-voting step, and assume fault-free on succedent step, then there will be a time redundancy on succedent step. The redundancy time of previous and succedent steps was counted, then a superior time window was used to do more deep recovery of fault. Based on the above ideas, a dynamic time series of multi-cross channel model was proposed, which was analyzed for deep recovery, and a backward recovery algorithm was given, which endowed more time to the fault unit, then the instantaneous fault could be eliminated to the utmost. Moreover, a monitoring logic was put forward to support the recovery algorithm. Theoretical analysis and experiments show that the backward recovery algorithm is effective to enhance the recovery rate and to reduce in the number of steps falling out. Compared with the statical recovery, the recovery rate increased by 47.49% and 72.35% respectively, and the number of out of step decreased by 58% and 85% respectively in the condition of 4 channel and 6 channel, which boosts the reliability of multi-cross channel model, especial in the condition of a large number of voting steps.