Ensuring cloud data integrity has become a security challenge that needs to be solved immediately. Widely-utilized blockchain technology provides a suitable solution to deal with this security challenge. The existing schemes combining blockchain and smart contract technology in which miners perform auditing validation work, suffer from low auditing efficiency, high communication overhead, and heavy auditing burden on Data Owner (DO). In response to the above issues, a Cloud data Auditing Scheme based on Voting mechanism and Ethereum smart Contracts (CASVEC) was proposed. Firstly, a Decentralized Autonomous Organization (DAO) was designed and deployed on Ethereum by combining voting mechanism and smart contract technology. The nodes of DAO voted to elect an auditing node to replace miners for the auditing verification work, effectively solving the defect of low efficiency in validation audit proof phase. Besides, reputation value was designed to ensure fairness and reliability of the voting process. Secondly, only a few on-chain resources were used to store final auditing results to reduce data volume during communication process, thus effectively solving the problem of high communication overhead in validation audit proof phase. Furthermore, DO only needed to delegate one audit request and retrieve final audit result from DAO. In the above process, DO had no need to call smart contracts so frequently to exchange related information, lightening the auditing burden of DO. Finally, from the theoretical analysis and experimental result perspectives, it was verified that compared with current blockchain-based cloud auditing schemes, CASVEC can significantly reduce time overhead and communication overhead of validation audit proof phase, as well as DO time overhead of audit phase.
Focused on the issue that the traditional interest area based visualization method can not pay attention to the details in the process of analyzing pilot eye movement data, a visual analysis method of eye movement data based on user-defined interest area was proposed. Firstly, according to the specific analysis task, the self-divison and self-definition of the background image of the task were introduced. Then, multiple auxiliary views and interactive approaches were combined, and an eye movement data visual analysis system for pilot training was designed and implemented to help analysts analyze the difference of eye movement between different pilots. Finally, through case analysis, the effectiveness of the visual analysis method and the practicability of the analysis system were proved. The experimental results show that compared with the traditional method, in the proposed method, the analysts' initiative in the analysis process is increased. The analysts are allowed to explore the local details of the task background in both global and local aspects, making the analysts' analyze the data in multi-angle; the analysts are allowed find the flight students' cognitive difficulties in the training process as a whole, so as to develop more targeted and more effective training courses.
For the problem of optimizing resource allocation to achieve profit maximization of cloud computing center, an analysis model based on Service Layer Agreement (SLA)-aware was proposed for optimizing server number and speed of cloud center. Meanwhile some important factors were taken into account, such as energy cost, server rental cost, customer waiting time, and SLA violation penalty. The impacts of cloud center profit by changing server number and speed were analyzed by numerical simulation. The numerical simulation results indicate that cloud center will obtain maximum profit by optimizing server number and speed at a certain request rate; with request rate increasing, profit will increase linearly by optimizing server number and speed. The analysis results can provide a reference method for cloud service provider to improve net business gain.
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For the problem of minimizing the energy consumption under performance constraint of cloud center, an optimal power consumption allocation method among multiple heterogeneous servers was proposed. First, an optimal energy consumption mathematical model of cloud center was built. Second, a Minimizing Power Consumption (MPC) algorithm for calculating the minimum energy was developed by using Lagrange multiplier method to obtain the optimal solution of the model. Finally, the MPC algorithm was verified by plenty of numerical experiments and compared with the Equal-Power (EP) baseline method. The experimental results indicate that MPC algorithm can save approximately 30% energy than the EP baseline method under the same load and the same response time conditions, and the proportion of energy saving will increase with load increasing. The MPC algorithm can effectively avoid energy configuration overload and it will provide ideas and reference data for optimal resource allocation of cloud center.
Since it is necessary to evaluate and analyze the service performance of cloud computing center to guarantee Quality of Service (QoS) and avoid violation of Service Layer Agreement (SLA), a approximated analysis model based on M/M/n/n+r queue theory was proposed for cloud computing center. By solving this model the probability distribution function of response time and other QoS indicators were acquired, meanwhile the relationship among the number of servers, size of queue buffers, response time, blocking probability and instance service probability were revealed and verified by simulation.The experimental results indicate that improving server service rate is better than increasing the number of servers for improving service performance.