Optimizing resources allocation for fog computing-based internet of things networks to reduce latency cost

Growing popularity of smart devices and 5G Internet, loT technology has also developed. The increase in the number of smart objects has led to an increase in data volumes and computational loads on a large scale. For this reason, cloud computing is used as a solution for this amount of data. However, given the importance of service quality, the cloud computing solution may not be responsive to latency-sensitive requests. Allocating resources in cloudy computing also reduces the cost of latency. Dynamic programming has been used due to the large number of requests and problem constraints. The proposed method reduces the cost of latency for loT requests. In this study, the proposed system modeling and algorithms were implemented for four cases. In these cases, two proposed methods have been the contribution of this research that these proposed algorithms have seen a significant reduction in the total latency cost. But as expected, the backward algorithm had a better response than the forward algorithm.