Aiming at the lack of security assessment in cloud environment, a cloud-based security modeling method was proposed, and a Security-Performance (S-P) association model in cloud environment was established. Firstly, a model was constructed for virtual machines, the most important component of the cloud system, to evaluate its security. The model fully reflected the impact of security mechanisms and malicious attacks on virtual machines. Secondly, based on the relationship between virtual machine and cloud system, an indicator was proposed for assessing the security of the cloud system. Thirdly, a hierarchical modeling method was proposed to establish an S-P association model. Queuing theory was used to model the performance of cloud computing systems, and the relationship between security and performance was established based on Bayesian theory and association analysis, and a new index for evaluating the association of complex S-P was proposed. Experimental results verify the correctness of the theoretical model and reveal the dynamic change rule of performance caused by safety factors.

Concerning the problems of heavy network traffic, which is caused by the excessive load of master node, bad expansibility and poor ability to handle node failure in existing monitoring system, a novel Cloud Monitoring System based on Bionic autonomic nervous system (B-CMS) was proposed. Firstly, B-CMS imported hierarchical storage and batch-wise report mechanism to upload the monitoring information, which can decrease the network traffic at any moment to ensure monitor system's stability. In addition, the use of similar Dynamic Host Configuration Protocol (DHCP) and polling-driven heartbeat checking mechanism enabled the monitor system to get the self-organizing and self-repairing ability which is similar to Bionic Autonomic Nervous System (BANS) when adding new node and handling node failure in autonomic way. The experimental results show that B-CMS achieves self-organizing and self-repairing, and effectively decreases network traffic. In some special moment, the network traffic is only one-third of the original system.