Most cloud services are built with multi-tenancy which enables data and configuration segregation upon shared infrastructures.It offers tremendous advantages for enterprises and service providers.It is anticipated tha...Most cloud services are built with multi-tenancy which enables data and configuration segregation upon shared infrastructures.It offers tremendous advantages for enterprises and service providers.It is anticipated that this situation will evolve to foster cross-tenant collaboration supported by Authorization as a service.To realize access control in a multi-tenant cloud computing environment,this study proposes a multi-tenant cloud computing access control model based on the traditional usage access control model by building trust relations among tenants.The model consists of three sub-models,which achieve trust relationships between tenants with different granularities and satisfy the requirements of different application scenarios.With an established trust relation in MT-UCON(Multi-tenant Usage Access Control),the trustee can precisely authorize cross-tenant accesses to the trustor’s resources consistent with constraints over the trust relation and other components designated by the trustor.In addition,the security of the model is analyzed by an information flow method.The model adapts to the characteristics of a dynamic and open multi-tenant cloud computing environment and achieves fine-grained access control within and between tenants.展开更多
With increasingly complex website structure and continuously advancing web technologies,accurate user clicks recognition from massive HTTP data,which is critical for web usage mining,becomes more difficult.In this pap...With increasingly complex website structure and continuously advancing web technologies,accurate user clicks recognition from massive HTTP data,which is critical for web usage mining,becomes more difficult.In this paper,we propose a dependency graph model to describe the relationships between web requests.Based on this model,we design and implement a heuristic parallel algorithm to distinguish user clicks with the assistance of cloud computing technology.We evaluate the proposed algorithm with real massive data.The size of the dataset collected from a mobile core network is 228.7GB.It covers more than three million users.The experiment results demonstrate that the proposed algorithm can achieve higher accuracy than previous methods.展开更多
文摘Most cloud services are built with multi-tenancy which enables data and configuration segregation upon shared infrastructures.It offers tremendous advantages for enterprises and service providers.It is anticipated that this situation will evolve to foster cross-tenant collaboration supported by Authorization as a service.To realize access control in a multi-tenant cloud computing environment,this study proposes a multi-tenant cloud computing access control model based on the traditional usage access control model by building trust relations among tenants.The model consists of three sub-models,which achieve trust relationships between tenants with different granularities and satisfy the requirements of different application scenarios.With an established trust relation in MT-UCON(Multi-tenant Usage Access Control),the trustee can precisely authorize cross-tenant accesses to the trustor’s resources consistent with constraints over the trust relation and other components designated by the trustor.In addition,the security of the model is analyzed by an information flow method.The model adapts to the characteristics of a dynamic and open multi-tenant cloud computing environment and achieves fine-grained access control within and between tenants.
基金supported in part by the Fundamental Research Funds for the Central Universities under Grant No.2013RC0114111 Project of China under Grant No.B08004
文摘With increasingly complex website structure and continuously advancing web technologies,accurate user clicks recognition from massive HTTP data,which is critical for web usage mining,becomes more difficult.In this paper,we propose a dependency graph model to describe the relationships between web requests.Based on this model,we design and implement a heuristic parallel algorithm to distinguish user clicks with the assistance of cloud computing technology.We evaluate the proposed algorithm with real massive data.The size of the dataset collected from a mobile core network is 228.7GB.It covers more than three million users.The experiment results demonstrate that the proposed algorithm can achieve higher accuracy than previous methods.
