No abstract

Security of data has always been a big problem in information technology. Because the data are stored in a variety of locations, including all over the world, this problem becomes even more pressing in the context of cloud computing. Concerns about cloud technology stem primarily from users' concerns regarding data security and privacy. The heterogeneity of cloud resources and the numerous shared applications they serve can benefit from effective scheduling. Considering the quality of the service that is provided to users, this will cut costs and energy use for them. Goal of this study is to improve cloud soft computing's resource allocation and data protection using a secure channel model and machine learning architecture combined with distributed social networks. The cloud architecture data protection in the proposed network model is accomplished by developing the channel model using hierarchical lightweight cryptography analysis. Then, Q-bayes propagation quantum networks are used to allocate resources. Memory capacity, data protection analysis, throughput, end-end delay, and processing time are all used in experimental analysis.Proposed technique attained memory capacity of 73%, data protection analysis of 69%, throughput of 95%, end-end delay of 69%, processing time of 49%.