摘要：
For the cloud computing system, combined with the memory function and incomplete matching of the biological immune system, a formal modeling and analysis method of the cloud computing system survivability is proposed by analyzing the survival situation of critical cloud services. First, on the basis of the SAIR(susceptible, active, infected, recovered) model, the SEIRS(susceptible, exposed, infected, recovered, susceptible) model and the vulnerability diffusion model of the distributed virtual system, the evolution state of the virus is divided into six types, and then the diffusion rules of the virus in the service domain of the cloud computing system and the propagation rules between service domains are analyzed. Finally, on the basis of Bio-PEPA(biological-performance evaluation process algebra), the formalized modeling of the survivability evolution of critical cloud services is made, and the SLIRAS ( susceptible, latent, infected, recovered, antidotal, susceptible) model is obtained. Based on the stochastic simulation and the ODEs (ordinary differential equations) simulation of the Bio-PEPA model, the sensitivity parameters of the model are analyzed from three aspects, namely, the virus propagation speed of inter-domain, recovery ability and memory ability. The results show that the proposed model has high approximate fitting degree to the actual cloud computing system, and it can well reflect the survivable change of the system.%面向云计算系统,结合生物免疫系统的记忆功能以及不完全匹配性,通过对关键云服务可生存态势的分析,提出了一种云计算系统可生存性的形式化建模与分析方法.首先,在SAIR模型、SEIRS模型和分布式虚拟化系统脆弱性扩散模型的基础上,将病毒演化状态分为6种类型,然后分析了病毒在云计算系统服务域内的扩散规则和服务域间的传播规则.最后,基于Bio-PEPA对关键云服务可生存性态势演化进行形式化建模,得到SLIRAS模型.基于随机模拟和Bio-PEPA模型的ODEs模拟,从病毒的域间传播速率、修复能力、记忆能力3个方面对模型敏感参数进行了试验分析.结果表明,所建立的模型与实际云计算系统的可生存性态势近似拟合度高,能够很好地反映系统可生存性的变化.
