An optimization method for resource allocation in fog computing

With the rapid development of the network, a huge amount of data will be generated. Existing technologies usually use cloud computing to process data. However, when all the data of users need to be transferred to the cloud for processing, it will cause long transmission delay due to the distance between users and cloud computing data center. Fog computing connects network edge devices downward and cloud data center upward, which greatly shortens the transmission distance of user data. Fog computing is an effective way to solve the problems of high delay, bandwidth bottleneck and security. In this paper, we have developed an optimization method, which establish an optimization problem with minimum average task delay as optimization objective and various system resources. By variable substitution, the nonconvex optimization problem is equivalent to a new optimization problem whose objective is nonconvex and all constraints are convex. In order to solve the global optimal solution of the nonconvex optimization problem, a new lower bound is designed by relaxing the objective to a lower bound quadratic function. We have evaluated our system by simulation test. The results verify the effectiveness of the optimization method.