Abstract
To improve calculation and storage capacity of mobile cloud data storage algorithm and solve the problem of low reliability and low energy utilization efficiency in remote server mode, the resampling mobile cloud data storage algorithm based on Gibbs probability allocation estimate is proposed. This algorithm firstly uses vote data allocation and vote data processing model to construct the calculation model for expected propagation time of resampling model in the condition of node failure probability and establishes vote dynamic network integrating energy efficiency and fault tolerance. Secondly, for the constructed dynamic network model, the storage path is optimized via sample probability allocation estimate. In addition, to improve process performance of allocation estimate, the Gibbs sampling process is used to realize high-dimensional coupling and non-supervised training for sample data in the process of allocation estimate. Lastly the effectiveness of proposed model algorithm was verified by experiment.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
