FEMTO: Fair and Energy-Minimized Task Offloading for Fog-Enabled IoT Networks

Abstract

Future Internet of Things (IoT) networks enabled with fog computing is promising to achieve lower processing delay and lighter link burden, by effectively offloading the computing tasks of the terminal nodes (TNs) to nearby fog nodes (FNs) at the network edge. Existing researches for the energy consumption in fog-enabled networks mostly focused on the minimization of the overall energy consumed by the task offloading services. However, fair offloading among multiple FNs while maintaining a satisfactory energy efficiency is of great significance for the sustainability of the fog-enabled IoT networks, especially in the scenarios with battery-powered FNs. In this paper, we propose a fair and energy-minimized task offloading (FEMTO) algorithm based on a fairness scheduling metric, taking three important characteristics into consideration, which include the task offloading energy consumption, the FN's historical average energy and the FN priority. The analytical results of the optimal target FN, the optimal TN transmission power, and the optimal subtask size are obtained in a fair and energy-minimized manner. Extensive simulations are carried out for the heterogeneous fog-enabled IoT network, and the numerical results indicate that the proposed FEMTO algorithm effectively determines the FN feasibility and the minimum energy consumption for the task offloading services. Moreover, a high and robust fairness level for the FNs' energy consumptions is obtained by the proposed FEMTO algorithm.