An Approach to Reducing Device Uncertainty in Fog-Cloud Computing

Abstract

Various problems arise in building distributed data processing systems, including the problem of uncertainty. Uncertainty is a major problem and can be divided into several types based on its origin. The paper proposes a combined approach for reducing uncertainty in cloud computing. This approach is based on modular arithmetic and divides the data to be processed and stored among devices according to a specific algorithm. Simulation results show that this method reduces uncertainty and improves data processing efficiency in distributed systems. It has also been found that as the number of participating devices increases in a given task, the benefits of the proposed approach become more significant. This indicates that dividing tasks into smaller units and distributing them using a “confidence coefficient” yields more stable and reliable outcomes. In the future, it will be necessary to conduct research to identify the most effective way to divide the task into smaller subtasks.