Research On Data Resources Integration Mechanism Of Mobile Cloud Computing Alliance

With the rapid development of social networking, mobile Internet, things like, cloud computing, the data has been growing in an unprecedented rate, how to management and utilize data resources better has attracted much attention. Cloud computing can provide storage and computing capabilities for data resources, to speed up the processing capacity of big data, which makes data resources realized the true value. However, faced with an increasingly complex cloud competitive market, and big data occupy a higher position, a single cloud service providers is difficult to adapt to the rapidly changing environment, and a new form of organization called Mobile Cloud Computing Alliance-MCCA generates. Facing the massive, heterogeneous and variant data resources in MCCA, how to integrate and reorganize them has been a key issue that the data resources can be successfully exploited and shared. Therefore, it is urgent to establish a set of perfect scientific data resources integration mechanism of Mobile Cloud Computing Alliance.This dissertation analyzes MCCA connotation, characteristics and data resources types and characteristics, thus we use evolution theory to reveal data resources integration mechanism of MCCA, and design integral framework of data resources integration mechanism of MCCA which contain data resources acquisition and storage mechanism, data resources clustering and composition mechanism. In the acquisition and storage mechanism, we focus on the use of RDF ontology language and Hbase technology to transform and store heterogeneous data resources, enabling standardized storage of data resources of MCCA; In order to ensure effective classification and combination of data resources, in the clustering and composition mechanism we use an improved clustering algorithm based on breadth-first search to classify data resources, secondly, a composition optimization model for MCCA data resource based on Agent was established, and quantum multi-objective evolutionary algorithm was adopted to solve the model. Finally, ZOL MCCA is chosen to do empirical research.This study is beneficial to the sharing and utilization of data resources of MCCA, and enriches the basic theory of Mobile Cloud Computing Alliance and data resources integration mechanism, while it is important theoretical and practical significance for sharing and utilization of Mobile Cloud Computing Alliance data resources.