Minimizing data redundancy for high reliable cloud storage systems

Cloud storage system provides reliable service to users by widely deploying redundancy schemes in its system - which brings high reliability to the data storage, but inversely introduces significant overhead to the system, consisting of storage cost and energy consumption. The core behind this issue is how to leverage the relationship between data redundancy and data reliability. To optimize both concurrently is apparently difficult. As such, to fix one as a constraint and then to reach another one becomes the consensus. We aim in the paper to pursue a storage allocation scheme that minimizes the data redundancy while achieving a given (high) data reliability. For this purpose, we have provided a novel model based on generating function. With this model, we have proposed a practical and efficient storage allocation scheme, which is proved to be able to minimize the data redundancy. We analytically demonstrate that the suggested solution brings several advantages, in particular the reduction of the search space and the acceleration to the computation. We also assess the improvement on the savings of data redundancy experimentally by adopting availability traces collected from real world - which encouragingly shows that the reduction of data redundancy by our solution can reach up to more than 30% as compared to the heuristic method recently proposed in the research community.