For the cloud computing system,combined wth 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 ...For the cloud computing system,combined wth 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 analyzee.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 showthat 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.展开更多
Due to the lack of pre-recognition and post- prediction in existing survivable systems, a recognition model of survival situations for survivable systems is proposed. First, the survival situation data is clustered in...Due to the lack of pre-recognition and post- prediction in existing survivable systems, a recognition model of survival situations for survivable systems is proposed. First, the survival situation data is clustered into several survival clusters with different service levels based on the Ward method, and then the survival clusters are classified and recognized by means of the error-eliminating decision-making method, which can realize the pre-recognition of the system＇s survival situation. Secondly, the differentiated survival situation data is used to generate stationary predicting sequences. The autoregressive integrated moving average （ARIMA） model is constructed, and the stability, randomness and reversibility index of the model are verified by the auto- correlation function and partial auto-correlation function. Finally, fuzzy particles and the residual correction for the support vector regression （SVR） model are applied to realize the post-prediction of the survival situation. Compared with traditional decision-making methods, the simulation experiments show that the pre-recognition module can not only cluster the survival situation data and identify the service ranks, but can also recognize the illegal users. According to the prediction of abnormal situations numbers and residual correction, the model can effectively realize the post- prediction of survival situations for survivable systems.展开更多
基金The National Natural Science Foundation of China(No.61202458,61403109)the Natural Science Foundation of Heilongjiang Province of China(No.F2017021)Harbin Science and Technology Innovation Research Funds(No.2016RAQXJ036)
文摘For the cloud computing system,combined wth 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 analyzee.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 showthat 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.
基金The National Natural Science Foundation of China(No.61202458,61403109)the Natural Science Foundation of Heilongjiang Province(No.F2017021)Harbin Science and Technology Innovation Research Funds(No.2016RAQXJ036)
文摘Due to the lack of pre-recognition and post- prediction in existing survivable systems, a recognition model of survival situations for survivable systems is proposed. First, the survival situation data is clustered into several survival clusters with different service levels based on the Ward method, and then the survival clusters are classified and recognized by means of the error-eliminating decision-making method, which can realize the pre-recognition of the system＇s survival situation. Secondly, the differentiated survival situation data is used to generate stationary predicting sequences. The autoregressive integrated moving average （ARIMA） model is constructed, and the stability, randomness and reversibility index of the model are verified by the auto- correlation function and partial auto-correlation function. Finally, fuzzy particles and the residual correction for the support vector regression （SVR） model are applied to realize the post-prediction of the survival situation. Compared with traditional decision-making methods, the simulation experiments show that the pre-recognition module can not only cluster the survival situation data and identify the service ranks, but can also recognize the illegal users. According to the prediction of abnormal situations numbers and residual correction, the model can effectively realize the post- prediction of survival situations for survivable systems.
