Delay Minimized Task Scheduling in Fog-Enabled IoT Networks

Abstract

Through offloading the computing tasks of the terminal nodes (TNs) to the fog nodes (FNs) located at the network edge, fog computing is expected to address the unacceptable processing delay and heavy link burden existed in current cloud-based Internet of Things (IoT) networks. How to take full use of the computing resources deployed on the FNs is vital to provide high quality offloading services for delay-sensitive TN tasks in fog-enabled IoT networks. In this paper, we construct a general analytical model for the task offloading delay in a fog-enabled IoT network. The optimal solution including the subtask sizes and the TN transmission power are obtained by the proposed Delay-Minimized Task Offloading (DMTO) algorithm that minimizes the task offloading delay. Extensive simulations are carried out in a fog-enabled IoT network, and the numerical results indicate that the proposed algorithm can effectively provide the optimal offloading solutions for the delay-sensitive tasks.