Non-Convex Optimization of Resource Allocation in Fog Computing Using Successive Approximation

Fog computing can deliver low delay and advanced IT services to end users with substantially reduced energy consumption. Nevertheless, with soaring demands for resource service and the limited capability of fog nodes, how to allocate and manage fog computing resources properly and stably has become the bottleneck. Therefore, the paper investigates the utility optimization-based resource allocation problem between fog nodes and end users in fog computing. The authors first introduce four types of utility functions due to the diverse tasks executed by end users and build the resource allocation model aiming at utility maximization. Then, for only the elastic tasks, the convex optimization method is applied to obtain the optimal results; for the elastic and inelastic tasks, with the assistance of Jensen’s inequality, the primal non-convex model is approximated to a sequence of equivalent convex optimization problems using successive approximation method. Moreover, a two-layer algorithm is proposed that globally converges to an optimal solution of the original problem. Finally, numerical simulation results demonstrate its superior performance and effectiveness. Comparing with other works, the authors emphasize the analysis for non-convex optimization problems and the diversity of tasks in fog computing resource allocation.