Research On Causal Consistency Model In Distributed Cloud Storage Environment

Data consistency in distributed cloud storage means that after the data of one node is updated,the data of other replica nodes also needs to be updated.Causal consistency requires that data operations in nodes meet the constraints of causal dependency.At present,the causal consistency model has been widely used in the research of data consistency.With the development of big data,to meet the requirement of high performance,further research is needed to improve the performance of causal consistency model.At the same time,the security situation of cloud storage environment is increasingly severe,and the causal consistency model faces more security risks.Therefore,how to achieve data consistency efficiently and safely in causal consistency model becomes a key problem to be solved.Given the above problems,the main contributions of this thesis are as follows:1.In order to solve the problem of metadata propagation overhead,operation delay and remote update visibility latency in the current causal consistency model,a causal consistency scheme for distributed data stores based on the shared graph and partial replication strategy was proposed.This model was based on the topology of the shared graph,and each data center stored an arbitrary subset of the data.At the same time,the global stabilization strategy combining shared stable vector and hybrid logical clocks was proposed to provide data consistency guarantees on the premise of ensuring causality.The theoretical analysis and experimental results show that the proposed scheme model can effectively balance the remote update visibility and the metadata overhead compared with the existing models while reducing the operation delay.2.To solve the problem of identity signature forgery,sensitive data tampering and remote update visibility delay in distributed cloud storage,a causal consistency model for adding trusted signature authentication for clients and servers is proposed.Firstly,based on the Partition Stable Vector,the scheme delimits the stable data of all remote data centers.At the same time,Replicater is selected in the synchronous data operation to ensure that the synchronized data meet the security constraints and improve the operation efficiency.The theoretical analysis and experimental results show that compared with the existing models,this scheme takes less overhead as the premise,ensures that the operation meets the requirements of trusted authentication,and reduces the remote update visibility delay.