Using Neural Network Technologies to Enhance Efficiency of the Distirbuted Data Storage Systems

The paper deals with the methods of performance improvement in the distributed data storage systems based on the threshold schemes (on example of Shamir-s secret sharing scheme). The factors affecting the performance of distributed data processing systems are analyzed, the ways of enhancing performance are presented, namely, through the use of hardware-emulated neural networks, parallel matrix algebra algorithms (recursive block scheme of matrix multiplication in the quaternary trees format, a parallel Strassen-s method modification) and modular arithmetic algorithms. The paper proposes the use of the mathematical apparatus of residual classes, what leads to neural network-based calculations and independent processing. The NM6403 neuroprocessors-based hardware platform required for the implementation of the proposed methods is discussed.