An Energy-Efficient Mixed-Task Paradigm in Resource Allocation for Fog Computing

Abstract

We study the energy efficiency problem for a fog computing system with multiple users and fog nodes. In order to handle various kinds of tasks at the same time, a mixed-task paradigm is proposed to combine both binary offloading and partial offloading. Then, a mixed-task resource allocation problem with the consideration of computation and communication resources is formulated as a mixed integer nonlinear programming problem (MINLP), which cannot be handled by the traditional relaxation algorithm due to the joint design of binary and partial offloading. To solve this problem efficiently, we first adopt a replacement-based method to transform the problem, and then design an augmented Lagrange method (ALM)-based resource allocation scheme. To further accelerate the solution procedure, a novel optimization technique, AMSGrad, is applied to the designed scheme. The performance of the proposed scheme is demonstrated by simulation results.