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Establishing an accurate edge server power model is helpful for resource providers to predict and optimize power consumption within edge data centers. Considering the fact that the accuracy of the previous energy consumption model is easily affected by the workload types, this paper develops an edge server power model based on BP (back propagation) neural network and feature selection, which is denoted by DSBF. For different task types, DSBF leverages "principal component analysis (PCA)" to analyze the contribution of each energy consumption parameter and selects "representative parameter", and then builds a power model based on BP neural network. In contrast to other power models, DSBF can effectively handle the variable workload. To measure the effectiveness of the DSBF model, a series of experiments were conducted. The results suggest that compared with other energy consumption models, DSBF can better adapt to the changing workload and has advantages in predicting the accuracy of the energy consumption model. • Designing the power modeling process, including parameter selection, parameter analysis and screening, model training and evaluation. • Using the "principal component analysis (PCA)" to analyze the factor contribution of each parameter. • Building a new power model named DSBF based on BP neural network and feature selection. [ABSTRACT FROM AUTHOR]

With the rapid development of cloud computing, secure search has become a hot research spot, which is a promising technique that allows a data user to perform privacy-preserving keyword-based search over encrypted cloud data. In this paper, we further consider the secure search problem based on a practical application scenario that a data owner needs to grant different keyword query permissions for different data users to achieve flexible access control on outsourced encrypted data in the cloud computing environment. To address this problem, we propose a fine-grained authorized keyword secure search scheme by leveraging the ciphertext policy attribute-based encryption (ABE), which not only supports privacy-preserving keyword-based search over encrypted data, but also inherits flexible and fine-grained data privilege control properties of ABE. Moreover, our proposed scheme is able to achieve fine-grained search permission update with very small communication and computation cost. By running the attribute revocation sub-protocol and attribute addition sub-protocol, the data owner can flexibly and efficiently update a data user's keyword search permissions when the data user's system role changes. We provide detailed performance analysis and rigorous security proof for our scheme. Extensive experiments demonstrate the correctness and practicality of the proposed scheme. • Presented a fine-grained authorized keyword secure search scheme in cloud computing. • Presented an efficient and fine-grained search permission update protocol. • Analyzed and proved the security of the proposed scheme. • Analyzed the complexity and tested the performance of the proposed scheme. [ABSTRACT FROM AUTHOR]