En-AR-PRNS: Entropy-Based Reliability for Configurable and Scalable Distributed Storage Systems

Abstract

Storage-as-a-service offers cost savings, convenience, mobility, scalability, redundant locations with a backup solution, on-demand with just-in-time capacity, syncing and updating, etc. While this type of cloud service has opened many opportunities, there are important considerations. When one uses a cloud provider, their data are no longer on their controllable local storage. Thus, there are the risks of compromised confidentiality and integrity, lack of availability, and technical failures that are difficult to predict in advance. The contribution of this paper can be summarized as follows: (1) We propose a novel mechanism, En-AR-PRNS, for improving reliability in the configurable, scalable, reliable, and secure distribution of data storage that can be incorporated along with storage-as-a-service applications. (2) We introduce a new error correction method based on the entropy (En) paradigm to correct hardware and software malfunctions, integrity violation, malicious intrusions, unexpected and unauthorized data modifications, etc., applying a polynomial residue number system (PRNS). (3) We use the concept of an approximation of the rank (AR) of a polynomial to reduce the computational complexity of the decoding. En-AR-PRNS combines a secret sharing scheme and error correction codes with an improved multiple failure detection/recovery mechanism. (4) We provide a theoretical analysis supporting the dynamic storage configuration to deal with varied user preferences and storage properties to ensure high-quality solutions in a non-stationary environment. (5) We discuss approaches to efficiently exploit parallel processing for security and reliability optimization. (6) We demonstrate that the reliability of En-AR-PRNS is up to 6.2 times higher than that of the classic PRNS.