<i>Detour:</i> Dynamic Task Offloading in Software-Defined Fog for IoT Applications

TLDR

The study addresses task offloading in software‑defined fog networks where IoT devices connect to fog nodes via multi‑hop access points. It formulates the offloading decision, fog‑node selection, and path selection as an integer linear program and solves it with a greedy‑heuristic that balances delay, energy, multi‑hop paths, and dynamic network conditions such as link utilization and SDN rule capacity. Experiments demonstrate that the scheme cuts average delay by 12 % and energy consumption by 21 % versus the state of the art.

Abstract

In this paper, we consider the problem of task offloading in a software-defined access network, where IoT devices are connected to fog computing nodes by multi-hop IoT access-points (APs). The proposed scheme considers the following aspects in a fog-computing-based IoT architecture: 1) optimal decision on local or remote task computation; 2) optimal fog node selection; and 3) optimal path selection for offloading. Accordingly, we formulate the multi-hop task offloading problem as an integer linear program (ILP). Since the feasible set is non-convex, we propose a greedy-heuristic-based approach to efficiently solve the problem. The greedy solution takes into account delay, energy consumption, multi-hop paths, and dynamic network conditions, such as link utilization and SDN rule-capacity. Experimental results show that the proposed scheme is capable of reducing the average delay and energy consumption by 12% and 21%, respectively, compared with the state of the art.